.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/swissmetro/plot_b22multiple_models.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_swissmetro_plot_b22multiple_models.py: .. _plot_b22multiple_models: Assisted specification ====================== Example of the estimation of several versions of the model using assisted specification algorithm. The catalog of specifications is defined in :ref:`plot_b22multiple_models_spec` . Compared to :ref:`plot_b21multiple_models`, the number fo specifications exceeds the maximum limit, so a heuristic is applied. See `Bierlaire and Ortelli, 2023 `_ for a detailed description of the use of the assisted specification algorithm. Michel Bierlaire, EPFL Sat Jun 28 2025, 12:25:12 .. GENERATED FROM PYTHON SOURCE LINES 21-33 .. code-block:: Python import biogeme.biogeme_logging as blog from biogeme.assisted import AssistedSpecification from biogeme.catalog import count_number_of_specifications from biogeme.multiobjectives import aic_bic_dimension from biogeme.results_processing import compile_estimation_results from plot_b22multiple_models_spec import PARETO_FILE_NAME, the_biogeme logger = blog.get_screen_logger(blog.INFO) logger.info('Example b22multiple_models') .. rst-class:: sphx-glr-script-out .. code-block:: none Example b22multiple_models .. GENERATED FROM PYTHON SOURCE LINES 34-41 .. code-block:: Python nbr = count_number_of_specifications(the_biogeme.log_like) if nbr is None: print('There are too many possible specifications to be enumerated') else: print(f'There are {nbr} possible specifications') .. rst-class:: sphx-glr-script-out .. code-block:: none There are 504 possible specifications .. GENERATED FROM PYTHON SOURCE LINES 42-51 Creation of the object capturing the assisted specification algorithm. Its constructor takes three arguments: - the biogeme object containing the specifications and the database, - an object defining the objectives to minimize. Here, we use three objectives: AIC, BIC and number of parameters. - the name of the file where the estimated are saved, and organized into a Pareto set. .. GENERATED FROM PYTHON SOURCE LINES 51-57 .. code-block:: Python assisted_specification = AssistedSpecification( biogeme_object=the_biogeme, multi_objectives=aic_bic_dimension, pareto_file_name=PARETO_FILE_NAME, ) .. rst-class:: sphx-glr-script-out .. code-block:: none Biogeme parameters read from biogeme.toml. Pareto set initialized from file with 465 elements [7 Pareto] and 0 invalid elements. .. GENERATED FROM PYTHON SOURCE LINES 58-59 The algorithm is run. .. GENERATED FROM PYTHON SOURCE LINES 59-61 .. code-block:: Python non_dominated_models = assisted_specification.run() .. rst-class:: sphx-glr-script-out .. code-block:: none Biogeme parameters read from biogeme.toml. Model with 4 unknown parameters [max: 50] *** Estimate b07everything_000161 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time b_cost asc_car Function Relgrad Radius Rho 0 -0.92 -0.67 -0.88 -0.49 5.4e+03 0.041 10 1.1 ++ 1 -0.73 -1.2 -1 -0.18 5.3e+03 0.0072 1e+02 1.1 ++ 2 -0.7 -1.3 -1.1 -0.16 5.3e+03 0.00018 1e+03 1 ++ 3 -0.7 -1.3 -1.1 -0.16 5.3e+03 1.1e-07 1e+03 1 ++ default_specification=asc:no_seg;train_cost_catalog:linear;train_headway_catalog:without_headway;train_tt_catalog:linear The number of possible specifications [504] exceeds the maximum number [100]. A heuristic algorithm is applied. *** VNS *** asc:no_seg;train_cost_catalog:linear;train_headway_catalog:without_headway;train_tt_catalog:linear [10670.504013832326, np.float64(10697.78385743747), 4] Initial pareto: 7 Attempt 0/100 Biogeme parameters read from biogeme.toml. Model with 15 unknown parameters [max: 50] *** Estimate b07everything_000162 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho 0 -1 -0.17 -0.015 0 0 0 -0.53 -0.0047 0 0 -0.73 -0.37 -0.047 0 0 5.8e+03 2.4 10 1 ++ 1 -1.9 0.83 0.76 0 0 0 -1.1 -0.0052 0 0 -1 0.05 -0.39 0 0 5.5e+03 0.53 1e+02 1.1 ++ 2 -2.3 1.2 1.1 0 0 0 -1.1 -0.0055 0 0 -1 0.028 -0.47 0 0 5.5e+03 0.073 1e+03 1.1 ++ 3 -2.3 1.2 1.2 0 0 0 -1.1 -0.0055 0 0 -1 0.028 -0.48 0 0 5.5e+03 0.0018 1e+04 1 ++ 4 -2.3 1.2 1.2 0 0 0 -1.1 -0.0055 0 0 -1 0.028 -0.48 0 0 5.5e+03 1.2e-06 1e+04 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 10 unknown parameters [max: 50] *** Estimate b07everything_000163 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho 0 -0.65 0.046 -0.0052 -0.98 1.8 -1 -0.0022 -0.35 -0.18 -0.042 5.6e+03 1.6 1 0.76 + 1 -0.98 1 0.075 -1.7 0.81 -1.1 -0.0048 -0.34 0.15 -0.11 5.2e+03 0.56 10 1 ++ 2 -1 1.1 1.1 -1.7 0.53 -1.1 -0.0055 -0.13 0.061 -0.41 5.2e+03 0.029 1e+02 0.99 ++ 3 -1.1 1.2 0.96 -1.6 0.5 -1.1 -0.0055 -0.18 0.071 -0.3 5.2e+03 0.0025 1e+03 1 ++ 4 -1.1 1.2 0.96 -1.6 0.5 -1.1 -0.0055 -0.18 0.071 -0.3 5.2e+03 5.5e-06 1e+03 1 ++ Considering neighbor 1/20 for current solution Attempt 1/100 Biogeme parameters read from biogeme.toml. Model with 14 unknown parameters [max: 50] *** Estimate b07everything_000164 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.65 0.022 -0.01 -1 0 0 0 0 0 -0.22 -0.2 -0.029 0 0 5.7e+03 0.045 10 1 ++ 1 -0.91 0.8 0.63 -1.5 0 0 0 0 0 0.25 -0.088 -0.72 0 0 5.5e+03 0.009 1e+02 1.1 ++ 2 -1.2 1.1 0.9 -1.6 0 0 0 0 0 0.24 -0.083 -0.77 0 0 5.5e+03 0.001 1e+03 1.1 ++ 3 -1.2 1.1 0.94 -1.6 0 0 0 0 0 0.24 -0.084 -0.77 0 0 5.5e+03 1.8e-05 1e+04 1 ++ 4 -1.2 1.1 0.94 -1.6 0 0 0 0 0 0.24 -0.084 -0.77 0 0 5.5e+03 5e-09 1e+04 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 12 unknown parameters [max: 50] *** Estimate b07everything_000165 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.71 0.23 -1 0 0 0 0 0 -0.31 -0.14 0 0 5.6e+03 0.044 10 1 ++ 1 -0.77 2.1 -1.6 0 0 0 0 0 0.24 -1 0 0 5.3e+03 0.023 1e+02 0.93 ++ 2 -0.88 1.9 -1.6 0 0 0 0 0 0.23 -1.3 0 0 5.2e+03 0.00045 1e+03 1 ++ 3 -0.88 1.9 -1.6 0 0 0 0 0 0.23 -1.3 0 0 5.2e+03 1.6e-06 1e+03 1 ++ Considering neighbor 1/20 for current solution Attempt 2/100 Biogeme parameters read from biogeme.toml. Model with 16 unknown parameters [max: 50] *** Estimate b07everything_000166 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 6.1e+03 0.07 10 1.1 ++ 1 5.9e+03 0.0023 1e+02 1 ++ 2 5.9e+03 1.1e-05 1e+03 1 ++ 3 5.9e+03 2.6e-10 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 3/100 Biogeme parameters read from biogeme.toml. Model with 17 unknown parameters [max: 50] *** Estimate b07everything_000167 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.5e+03 2.7 10 1.1 ++ 1 5.4e+03 0.42 1e+02 1.1 ++ 2 5.4e+03 0.038 1e+03 1.1 ++ 3 5.4e+03 0.0004 1e+04 1 ++ 4 5.4e+03 5.3e-08 1e+04 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 15 unknown parameters [max: 50] *** Estimate b07everything_000168 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_ma asc_car_diff_wi beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho 0 -0.72 -0.9 1 0 0 0 -0.94 -0.0035 0 0 -0.78 -0.29 -0.18 0 0 5.3e+03 2.9 10 1.1 ++ 1 -1 -1.1 1.9 0 0 0 -0.99 -0.0058 0 0 -1.2 0.25 -0.14 0 0 5.2e+03 0.61 1e+02 1.1 ++ 2 -1 -1.2 2.1 0 0 0 -1 -0.0068 0 0 -1.2 0.25 -0.14 0 0 5.2e+03 0.05 1e+03 1.1 ++ 3 -1 -1.2 2.1 0 0 0 -1 -0.007 0 0 -1.2 0.25 -0.14 0 0 5.2e+03 0.00038 1e+04 1 ++ 4 -1 -1.2 2.1 0 0 0 -1 -0.007 0 0 -1.2 0.25 -0.14 0 0 5.2e+03 2.2e-08 1e+04 1 ++ Considering neighbor 1/20 for current solution Biogeme parameters read from biogeme.toml. Model with 11 unknown parameters [max: 50] *** Estimate b07everything_000169 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_ma asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho 0 -0.52 -0.82 0.23 -0.0093 -1 -1 -0.0023 -0.48 -0.18 -0.18 -0.049 5.3e+03 2.5 10 1.1 ++ 1 -0.091 -1.1 0.73 0.32 -2.9 -1 -0.0049 -0.48 0.27 0.11 -0.11 5.1e+03 0.42 1e+02 1.1 ++ 2 -0.13 -1.1 0.93 0.5 -3.3 -1.1 -0.0058 -0.48 0.31 0.082 -0.14 5.1e+03 0.04 1e+03 1 ++ 3 -0.14 -1.1 0.96 0.53 -3.3 -1.1 -0.0059 -0.49 0.31 0.082 -0.14 5.1e+03 0.00046 1e+04 1 ++ 4 -0.14 -1.1 0.96 0.53 -3.3 -1.1 -0.0059 -0.49 0.31 0.082 -0.14 5.1e+03 6.7e-07 1e+04 1 ++ Considering neighbor 2/20 for current solution Attempt 4/100 Biogeme parameters read from biogeme.toml. Model with 8 unknown parameters [max: 50] *** Estimate b07everything_000170 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost b_headway asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho 0 -0.57 -0.95 -0.86 2 -0.88 0.0074 -0.2 0.092 5.8e+03 2.6 1 0.68 + 1 0.056 -0.95 -0.49 1.8 -1.9 -0.0081 -0.56 0.079 5.3e+03 0.43 10 0.99 ++ 2 0.056 -0.95 -0.49 1.8 -1.9 -0.0081 -0.56 0.079 5.3e+03 0.43 5 -2.7e+03 - 3 0.056 -0.95 -0.49 1.8 -1.9 -0.0081 -0.56 0.079 5.3e+03 0.43 2.5 -16 - 4 0.056 -0.95 -0.49 1.8 -1.9 -0.0081 -0.56 0.079 5.3e+03 0.43 1.2 -0.53 - 5 -0.035 -1.3 -1.3 0.56 -2.2 0.0026 -0.35 0.51 5.1e+03 0.48 1.2 0.83 + 6 0.63 -1.3 -1.7 0.38 -2.3 -0.0057 -0.37 0.34 5.1e+03 0.14 12 0.99 ++ 7 0.63 -1.3 -1.7 0.42 -2.4 -0.0057 -0.36 0.33 5.1e+03 0.0024 1.2e+02 0.99 ++ 8 0.63 -1.3 -1.7 0.42 -2.4 -0.0057 -0.36 0.33 5.1e+03 2.3e-07 1.2e+02 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 19 unknown parameters [max: 50] *** Estimate b07everything_000171 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.8e+03 2.9 10 1 ++ 1 5.7e+03 0.32 1e+02 1.1 ++ 2 5.7e+03 0.014 1e+03 1 ++ 3 5.7e+03 2.1e-05 1e+04 1 ++ 4 5.7e+03 4.6e-11 1e+04 1 ++ Considering neighbor 1/20 for current solution Attempt 5/100 Biogeme parameters read from biogeme.toml. Model with 14 unknown parameters [max: 50] *** Estimate b07everything_000172 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost lambda_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_ma beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho 0 -0.57 -0.94 0 0 0 -0.84 1.3 -0.0031 0 0 -1 -0.046 0 0 5.6e+03 2.6 10 1 ++ 1 -0.57 -0.94 0 0 0 -0.84 1.3 -0.0031 0 0 -1 -0.046 0 0 5.6e+03 2.6 2.1 -2.1e+02 - 2 -0.57 -0.94 0 0 0 -0.84 1.3 -0.0031 0 0 -1 -0.046 0 0 5.6e+03 2.6 1 -2.6 - 3 -0.39 -1.5 0 0 0 -1.3 0.24 -0.0064 0 0 -1.3 0.078 0 0 5.5e+03 0.58 10 1 ++ 4 -0.51 -1.4 0 0 0 -1.1 0.19 -0.0059 0 0 -1.2 0.22 0 0 5.5e+03 0.022 1e+02 0.97 ++ 5 -0.52 -1.3 0 0 0 -1.1 0.19 -0.0059 0 0 -1.2 0.21 0 0 5.5e+03 5.2e-05 1e+03 1 ++ 6 -0.52 -1.3 0 0 0 -1.1 0.19 -0.0059 0 0 -1.2 0.21 0 0 5.5e+03 1.4e-09 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 6/100 Biogeme parameters read from biogeme.toml. Model with 9 unknown parameters [max: 50] *** Estimate b07everything_000173 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost lambda_cost b_headway asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho 0 -0.55 -0.88 -0.9 1.8 -1 1 0.006 -0.27 -0.004 5.6e+03 2.2 1 0.78 + 1 0.45 -1.1 -1.1 1.3 -0.97 0.49 -0.0078 -0.68 0.34 5.3e+03 0.47 10 1.1 ++ 2 0.45 -1.1 -1.1 1.3 -0.97 0.49 -0.0078 -0.68 0.34 5.3e+03 0.47 1 -2.4 - 3 0.52 -1.1 -1.9 0.27 -1.3 0.69 -0.0043 -0.54 0.46 5.2e+03 0.039 1 0.68 + 4 0.57 -1.3 -1.6 0.36 -1.1 0.62 -0.0058 -0.51 0.31 5.2e+03 0.014 10 0.99 ++ 5 0.56 -1.3 -1.6 0.39 -1.1 0.56 -0.0058 -0.53 0.32 5.2e+03 0.00011 1e+02 0.98 ++ 6 0.56 -1.3 -1.6 0.39 -1.1 0.56 -0.0058 -0.53 0.32 5.2e+03 8.6e-07 1e+02 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 11 unknown parameters [max: 50] *** Estimate b07everything_000174 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost lambda_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho 0 -0.68 0.065 -0.004 -1 1.8 -0.88 1 -0.0015 -0.35 -0.19 -0.046 5.7e+03 1.6 1 0.7 + 1 -0.96 1.1 0.085 -1.4 1.1 -1 0.73 -0.0064 -0.33 -0.04 -0.16 5.3e+03 0.57 10 1.1 ++ 2 -0.74 1.1 1 -2.1 0.28 -1.1 0.7 -0.0056 0.05 -0.1 -0.7 5.3e+03 0.069 10 0.69 + 3 -1.2 1.2 0.95 -1.5 0.38 -1.1 0.55 -0.0055 -0.2 -0.059 -0.63 5.2e+03 0.0064 1e+02 0.97 ++ 4 -1.1 1.2 0.95 -1.6 0.44 -1.1 0.55 -0.0055 -0.2 -0.062 -0.62 5.2e+03 0.00054 1e+03 0.96 ++ 5 -1.1 1.2 0.95 -1.6 0.44 -1.1 0.55 -0.0055 -0.2 -0.062 -0.62 5.2e+03 1.1e-06 1e+03 1 ++ Considering neighbor 1/20 for current solution Attempt 7/100 Biogeme parameters read from biogeme.toml. Model with 14 unknown parameters [max: 50] *** Estimate b07everything_000175 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.72 0.65 -0.95 2 0 0 0 -0.0015 0 0 -0.41 -0.3 0 0 6e+03 2.3 1 0.54 + 1 -0.93 1.6 -0.38 1.9 0 0 0 -0.0073 0 0 -0.15 -0.73 0 0 5.5e+03 0.17 1 0.86 + 2 -1.2 1.8 -0.83 0.89 0 0 0 -0.0047 0 0 -0.24 -0.85 0 0 5.3e+03 0.026 10 1 ++ 3 -0.51 1.8 -1.7 -0.05 0 0 0 -0.0059 0 0 0.19 -1.3 0 0 5.2e+03 0.12 10 0.49 + 4 -0.51 1.9 -1.7 0.32 0 0 0 -0.006 0 0 0.18 -1.4 0 0 5.2e+03 0.015 1e+02 1 ++ 5 -0.6 1.9 -1.6 0.34 0 0 0 -0.006 0 0 0.12 -1.3 0 0 5.2e+03 0.00078 1e+03 0.98 ++ 6 -0.6 1.9 -1.6 0.34 0 0 0 -0.006 0 0 0.12 -1.3 0 0 5.2e+03 8.4e-07 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 8/100 Biogeme parameters read from biogeme.toml. Model with 18 unknown parameters [max: 50] *** Estimate b07everything_000176 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 6e+03 0.073 10 1.1 ++ 1 5.5e+03 0.031 1e+02 0.98 ++ 2 5.5e+03 0.0011 1e+03 1 ++ 3 5.5e+03 4.7e-06 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 9/100 Biogeme parameters read from biogeme.toml. Model with 5 unknown parameters [max: 50] *** Estimate b07everything_000177 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time b_cost b_headway asc_car Function Relgrad Radius Rho 0 -0.74 -0.73 -1 -0.0011 -0.3 5.4e+03 2.3 10 1.1 ++ 1 -0.54 -1.2 -2.1 -0.0044 -0.19 5.3e+03 0.25 1e+02 1.1 ++ 2 -0.49 -1.2 -2.3 -0.0053 -0.21 5.3e+03 0.0056 1e+03 1 ++ 3 -0.48 -1.2 -2.3 -0.0053 -0.21 5.3e+03 0.0011 1e+04 1 ++ 4 -0.48 -1.2 -2.3 -0.0053 -0.21 5.3e+03 0.00014 1e+05 1 ++ 5 -0.48 -1.2 -2.3 -0.0053 -0.21 5.3e+03 1.2e-05 1e+06 1 ++ 6 -0.48 -1.2 -2.3 -0.0053 -0.21 5.3e+03 1.4e-08 1e+06 1 ++ Considering neighbor 0/20 for current solution Attempt 10/100 Biogeme parameters read from biogeme.toml. Model with 8 unknown parameters [max: 50] *** Estimate b07everything_000178 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost lambda_cost b_headway asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho 0 -0.53 0.2 -1 -0.23 1 -0.0043 -0.53 -0.13 5.5e+03 2.5 10 1 ++ 1 -0.53 0.2 -1 -0.23 1 -0.0043 -0.53 -0.13 5.5e+03 2.5 4.5 -3e+05 - 2 -0.53 0.2 -1 -0.23 1 -0.0043 -0.53 -0.13 5.5e+03 2.5 2.2 -1.1e+02 - 3 -0.53 0.2 -1 -0.23 1 -0.0043 -0.53 -0.13 5.5e+03 2.5 1.1 -3.7 - 4 -0.75 1.3 -1.6 -1.2 1.1 -0.0031 -0.22 -0.49 5.1e+03 0.22 11 1 ++ 5 -0.75 1.3 -1.6 -1.2 1.1 -0.0031 -0.22 -0.49 5.1e+03 0.22 1.1 -6.5 - 6 -0.73 2.4 -1.7 -1.4 0.48 -0.007 -0.27 -0.99 5e+03 0.27 11 1 ++ 7 -0.94 2.2 -1.6 -1.7 -0.12 -0.0061 -0.31 -1.8 4.9e+03 0.021 11 0.79 + 8 -0.93 2.2 -1.6 -1.5 -0.069 -0.0061 -0.28 -1.8 4.9e+03 0.00089 1.1e+02 1 ++ 9 -0.92 2.2 -1.6 -1.5 -0.038 -0.0061 -0.28 -1.9 4.9e+03 4.3e-05 1.1e+03 1 ++ 10 -0.92 2.2 -1.6 -1.5 -0.038 -0.0061 -0.28 -1.9 4.9e+03 3.9e-07 1.1e+03 1 ++ Considering neighbor 0/20 for current solution Considering neighbor 1/20 for current solution Attempt 11/100 Biogeme parameters read from biogeme.toml. Model with 20 unknown parameters [max: 50] *** Estimate b07everything_000179 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.6e+03 0.044 10 1.1 ++ 1 5.4e+03 0.01 1e+02 1.1 ++ 2 5.4e+03 0.00066 1e+03 1 ++ 3 5.4e+03 3.5e-06 1e+03 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 18 unknown parameters [max: 50] *** Estimate b07everything_000180 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.8e+03 0.037 10 1 ++ 1 5.7e+03 0.0061 1e+02 1.1 ++ 2 5.7e+03 0.00019 1e+03 1 ++ 3 5.7e+03 1.9e-07 1e+03 1 ++ Considering neighbor 1/20 for current solution Attempt 12/100 Biogeme parameters read from biogeme.toml. Model with 6 unknown parameters [max: 50] *** Estimate b07everything_000181 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho 0 -0.33 -0.81 -0.63 -0.89 -0.52 -0.039 5.3e+03 0.043 10 1.1 ++ 1 0.038 -1.2 -1.2 -1 -0.47 0.28 5.2e+03 0.0093 1e+02 1.1 ++ 2 0.089 -1.2 -1.2 -1.1 -0.46 0.31 5.2e+03 0.00033 1e+03 1 ++ 3 0.089 -1.2 -1.2 -1.1 -0.46 0.31 5.2e+03 4.1e-07 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 13/100 Biogeme parameters read from biogeme.toml. Model with 11 unknown parameters [max: 50] *** Estimate b07everything_000182 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho 0 -0.91 0 0 0 -0.42 -0.0075 0 0 -1 0 0 5.8e+03 2.5 10 1 ++ 1 -1.3 0 0 0 -0.91 -0.0058 0 0 -0.89 0 0 5.6e+03 0.098 1e+02 1 ++ 2 -1.4 0 0 0 -0.94 -0.0055 0 0 -0.91 0 0 5.6e+03 0.0014 1e+03 1 ++ 3 -1.4 0 0 0 -0.94 -0.0055 0 0 -0.91 0 0 5.6e+03 2.2e-07 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 14/100 Biogeme parameters read from biogeme.toml. Model with 10 unknown parameters [max: 50] *** Estimate b07everything_000183 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho 0 -0.72 0.11 -0.0034 -0.97 2 -0.8 -0.0012 -0.29 -0.15 -0.05 5.9e+03 1.9 1 0.55 + 1 -0.93 0.75 0.059 -0.68 1.7 -1.8 -0.008 -0.26 0.0067 -0.11 5.4e+03 0.021 10 1 ++ 2 -0.93 0.75 0.059 -0.68 1.7 -1.8 -0.008 -0.26 0.0067 -0.11 5.4e+03 0.021 4.1 -6.6e+02 - 3 -0.93 0.75 0.059 -0.68 1.7 -1.8 -0.008 -0.26 0.0067 -0.11 5.4e+03 0.021 2 -12 - 4 -0.93 0.75 0.059 -0.68 1.7 -1.8 -0.008 -0.26 0.0067 -0.11 5.4e+03 0.021 1 0.069 - 5 -1.3 0.83 0.16 -1.5 0.72 -2.1 -0.00036 -0.24 0.33 -0.15 5.2e+03 0.19 10 0.97 ++ 6 -1 1.1 1.1 -1.7 0.43 -2.3 -0.0057 -0.14 0.16 -0.31 5.1e+03 0.12 1e+02 0.93 ++ 7 -1.1 1.2 0.95 -1.6 0.46 -2.4 -0.0055 -0.17 0.16 -0.23 5.1e+03 0.0019 1e+03 1 ++ 8 -1.1 1.2 0.95 -1.6 0.46 -2.4 -0.0055 -0.17 0.16 -0.23 5.1e+03 1.6e-06 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 15/100 Biogeme parameters read from biogeme.toml. Model with 10 unknown parameters [max: 50] *** Estimate b07everything_000184 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost lambda_cost asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho 0 -0.45 -0.67 0.68 -0.96 2 -0.96 1 -0.51 -0.28 -0.32 5.7e+03 0.13 1 0.6 + 1 -0.68 -1.3 1.7 -0.62 1.7 -0.84 0.66 -0.49 0.16 -0.78 5.1e+03 0.015 10 1.1 ++ 2 -0.68 -1.3 1.7 -0.62 1.7 -0.84 0.66 -0.49 0.16 -0.78 5.1e+03 0.015 5 -3.1e+03 - 3 -0.68 -1.3 1.7 -0.62 1.7 -0.84 0.66 -0.49 0.16 -0.78 5.1e+03 0.015 2.5 -28 - 4 -0.68 -1.3 1.7 -0.62 1.7 -0.84 0.66 -0.49 0.16 -0.78 5.1e+03 0.015 1.2 -0.62 - 5 -0.85 -1.2 1.9 -1.3 0.4 -1.6 0.41 -0.6 0.3 -1.1 4.9e+03 0.033 1.2 0.9 + 6 -0.36 -1.1 2.1 -1.6 0.22 -1.5 0.12 -0.55 0.46 -1.8 4.8e+03 0.0056 12 0.99 ++ 7 -0.38 -1.1 2.1 -1.6 0.22 -1.5 0.032 -0.56 0.48 -2 4.8e+03 0.00028 1.2e+02 1 ++ 8 -0.38 -1.1 2.1 -1.6 0.22 -1.5 0.032 -0.56 0.48 -2 4.8e+03 2.1e-06 1.2e+02 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 10 unknown parameters [max: 50] *** Estimate b07everything_000185 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost b_headway asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho 0 -0.52 -0.79 0.53 -0.88 1.7 -1 0.003 -0.29 -0.051 -0.22 5.3e+03 2.3 1 0.88 + 1 -0.29 -1 1.5 -1.1 1 -1 -0.0043 -0.51 0.087 -0.35 4.9e+03 0.6 10 1.1 ++ 2 0.43 -1.1 1.9 -2.2 -0.078 -1.1 -0.0066 -0.37 0.46 -0.44 4.9e+03 0.14 10 0.21 + 3 0.15 -1.2 2 -1.8 0.17 -1.1 -0.0066 -0.51 0.4 -0.44 4.9e+03 0.0078 1e+02 1.1 ++ 4 0.11 -1.2 2 -1.7 0.33 -1.1 -0.0067 -0.54 0.41 -0.43 4.9e+03 0.0043 1e+03 1 ++ 5 0.098 -1.2 2 -1.7 0.34 -1.1 -0.0067 -0.55 0.41 -0.43 4.9e+03 0.00011 1e+04 1 ++ 6 0.098 -1.2 2 -1.7 0.34 -1.1 -0.0067 -0.55 0.41 -0.43 4.9e+03 2.2e-07 1e+04 1 ++ Considering neighbor 1/20 for current solution Attempt 16/100 Biogeme parameters read from biogeme.toml. Model with 16 unknown parameters [max: 50] *** Estimate b07everything_000186 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.5e+03 0.039 10 1.1 ++ 1 5.4e+03 0.009 1e+02 1.1 ++ 2 5.4e+03 0.00054 1e+03 1.1 ++ 3 5.4e+03 4.8e-06 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 17/100 Biogeme parameters read from biogeme.toml. Model with 6 unknown parameters [max: 50] *** Estimate b07everything_000187 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time lambda_tt b_cost lambda_cost asc_car Function Relgrad Radius Rho 0 -0.69 -1 1.7 -0.67 1 -0.55 5.8e+03 0.073 1 0.71 + 1 -0.69 -1.7 0.75 -1.3 0.87 -0.33 5.4e+03 0.053 10 0.98 ++ 2 -0.55 -1.5 0.55 -1.1 0.68 -0.09 5.4e+03 0.0023 1e+02 1 ++ 3 -0.56 -1.6 0.46 -1.1 0.58 -0.1 5.4e+03 0.0003 1e+03 1 ++ 4 -0.56 -1.6 0.46 -1.1 0.58 -0.1 5.4e+03 5.4e-07 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 18/100 Considering neighbor 0/20 for current solution Attempt 19/100 Biogeme parameters read from biogeme.toml. Model with 5 unknown parameters [max: 50] *** Estimate b07everything_000188 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time b_cost lambda_cost asc_car Function Relgrad Radius Rho 0 -0.93 -0.64 -0.85 2 -0.56 5.7e+03 0.067 10 0.92 ++ 1 -0.93 -0.64 -0.85 2 -0.56 5.7e+03 0.067 4.1 -1.3e+05 - 2 -0.93 -0.64 -0.85 2 -0.56 5.7e+03 0.067 2.1 -1.2e+02 - 3 -0.93 -0.64 -0.85 2 -0.56 5.7e+03 0.067 1 -9.9 - 4 -0.93 -0.64 -0.85 2 -0.56 5.7e+03 0.067 0.52 -0.53 - 5 -0.7 -1.2 -0.62 1.7 -0.18 5.5e+03 0.0091 5.2 1.1 ++ 6 -0.7 -1.2 -0.62 1.7 -0.18 5.5e+03 0.0091 2.5 -5.6e+02 - 7 -0.7 -1.2 -0.62 1.7 -0.18 5.5e+03 0.0091 1.3 -5.7 - 8 -0.56 -1.3 -1.2 0.46 -0.14 5.4e+03 0.012 1.3 0.57 + 9 -0.78 -1.2 -1.1 0.6 -0.26 5.4e+03 0.00052 13 1 ++ 10 -0.78 -1.2 -1.1 0.63 -0.26 5.4e+03 2.1e-05 1.3e+02 1 ++ 11 -0.78 -1.2 -1.1 0.63 -0.26 5.4e+03 1e-08 1.3e+02 1 ++ Considering neighbor 0/20 for current solution Attempt 20/100 Biogeme parameters read from biogeme.toml. Model with 8 unknown parameters [max: 50] *** Estimate b07everything_000189 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost b_headway asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho 0 -0.5 -0.77 -0.86 1.7 -1 0.0035 -0.28 -0.05 5.4e+03 2.1 1 0.87 + 1 0.27 -1.3 -1.6 0.68 -1.2 -0.0017 -0.54 0.39 5.1e+03 0.41 10 0.92 ++ 2 0.69 -1.3 -1.7 0.44 -1.1 -0.0059 -0.4 0.32 5.1e+03 0.022 1e+02 0.97 ++ 3 0.65 -1.3 -1.7 0.45 -1.1 -0.0058 -0.4 0.3 5.1e+03 2.5e-05 1e+03 1 ++ 4 0.65 -1.3 -1.7 0.45 -1.1 -0.0058 -0.4 0.3 5.1e+03 4.8e-07 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 21/100 Biogeme parameters read from biogeme.toml. Model with 6 unknown parameters [max: 50] *** Estimate b07everything_000190 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho 0 -0.37 -0.69 -0.63 -1 -0.37 -0.11 5.4e+03 0.038 10 1.1 ++ 1 0.00095 -1.1 -1.1 -2.1 -0.39 0.28 5.2e+03 0.011 1e+02 1.1 ++ 2 0.052 -1.2 -1.2 -2.3 -0.43 0.33 5.1e+03 0.00047 1e+03 1 ++ 3 0.052 -1.2 -1.2 -2.3 -0.43 0.33 5.1e+03 9e-07 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 22/100 Biogeme parameters read from biogeme.toml. Model with 7 unknown parameters [max: 50] *** Estimate b07everything_000191 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho 0 -0.37 -0.65 -1 -0.83 0.0008 -0.3 -0.073 5.3e+03 2.4 10 1.1 ++ 1 0.6 -1.3 -1.6 -1 -0.0046 -0.37 0.25 5.2e+03 0.49 1e+02 1.1 ++ 2 0.68 -1.4 -1.7 -1 -0.0057 -0.36 0.25 5.2e+03 0.021 1e+03 1 ++ 3 0.68 -1.4 -1.7 -1 -0.0057 -0.35 0.24 5.2e+03 2.6e-05 1e+04 1 ++ 4 0.68 -1.4 -1.7 -1 -0.0057 -0.35 0.24 5.2e+03 1.1e-08 1e+04 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 9 unknown parameters [max: 50] *** Estimate b07everything_000192 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho 0 -0.58 -0.83 0.7 -1 -0.96 -0.00091 -0.53 -0.2 -0.22 5.2e+03 2.6 10 1.1 ++ 1 -0.099 -0.98 1.8 -2.8 -1 -0.005 -0.54 0.34 -0.38 4.9e+03 0.69 1e+02 1.1 ++ 2 0.04 -1.1 1.9 -3.2 -1.1 -0.0066 -0.58 0.41 -0.42 4.9e+03 0.057 1e+03 1.1 ++ 3 0.049 -1.2 2 -3.2 -1.1 -0.0068 -0.59 0.41 -0.43 4.9e+03 0.00035 1e+04 1 ++ 4 0.049 -1.2 2 -3.2 -1.1 -0.0068 -0.59 0.41 -0.43 4.9e+03 9.9e-07 1e+04 1 ++ Considering neighbor 1/20 for current solution Attempt 23/100 Biogeme parameters read from biogeme.toml. Model with 7 unknown parameters [max: 50] *** Estimate b07everything_000193 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost lambda_cost asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho 0 -0.38 -0.76 -0.61 -0.87 2 -0.72 0.11 5.6e+03 0.068 10 0.94 ++ 1 -0.38 -0.76 -0.61 -0.87 2 -0.72 0.11 5.6e+03 0.068 4.2 -1.4e+05 - 2 -0.38 -0.76 -0.61 -0.87 2 -0.72 0.11 5.6e+03 0.068 2.1 -1.3e+02 - 3 -0.38 -0.76 -0.61 -0.87 2 -0.72 0.11 5.6e+03 0.068 1.1 -9.9 - 4 -0.38 -0.76 -0.61 -0.87 2 -0.72 0.11 5.6e+03 0.068 0.53 -0.41 - 5 -0.077 -0.94 -1.1 -0.59 1.8 -0.53 0.38 5.3e+03 0.0089 5.3 1.1 ++ 6 -0.077 -0.94 -1.1 -0.59 1.8 -0.53 0.38 5.3e+03 0.0089 2.6 -6e+02 - 7 -0.077 -0.94 -1.1 -0.59 1.8 -0.53 0.38 5.3e+03 0.0089 1.3 -5.5 - 8 0.33 -1.4 -1.1 -1.1 0.44 -0.54 0.33 5.3e+03 0.021 1.3 0.67 + 9 0.014 -1.2 -1.1 -1.1 0.61 -0.6 0.33 5.3e+03 0.00094 13 1 ++ 10 0.014 -1.2 -1.1 -1.1 0.61 -0.6 0.33 5.3e+03 4.5e-06 13 1 ++ Considering neighbor 0/20 for current solution Attempt 24/100 Biogeme parameters read from biogeme.toml. Model with 13 unknown parameters [max: 50] *** Estimate b07everything_000194 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_wi beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho 0 -0.91 0.23 0 0 0 -0.22 -0.0078 0 0 -1 -0.2 0 0 5.8e+03 2.6 10 1 ++ 1 -1.8 2.3 0 0 0 -1.4 -0.0061 0 0 -0.98 -1.2 0 0 5.2e+03 0.85 1e+02 0.99 ++ 2 -2 2.3 0 0 0 -1.5 -0.0063 0 0 -1.1 -1.6 0 0 5.2e+03 0.044 1e+03 1 ++ 3 -2 2.3 0 0 0 -1.5 -0.0064 0 0 -1.1 -1.6 0 0 5.2e+03 0.00035 1e+04 1 ++ 4 -2 2.3 0 0 0 -1.5 -0.0064 0 0 -1.1 -1.6 0 0 5.2e+03 1.5e-08 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 25/100 Biogeme parameters read from biogeme.toml. Model with 17 unknown parameters [max: 50] *** Estimate b07everything_000195 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.7e+03 2.5 10 1 ++ 1 5.2e+03 0.86 1e+02 1 ++ 2 5.2e+03 0.085 1e+03 1.1 ++ 3 5.2e+03 0.002 1e+04 1 ++ 4 5.2e+03 1.4e-06 1e+04 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 18 unknown parameters [max: 50] *** Estimate b07everything_000196 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 6e+03 2.3 1 0.54 + 1 5.4e+03 0.19 10 1 ++ 2 5.4e+03 0.19 5 -3e+03 - 3 5.4e+03 0.19 2.5 -24 - 4 5.4e+03 0.19 1.2 -0.64 - 5 5.2e+03 0.1 1.2 0.83 + 6 5.2e+03 0.079 12 0.96 ++ 7 5.2e+03 0.0015 1.2e+02 1 ++ 8 5.2e+03 1.6e-07 1.2e+02 1 ++ Considering neighbor 1/20 for current solution Attempt 26/100 Considering neighbor 0/20 for current solution Attempt 27/100 Biogeme parameters read from biogeme.toml. Model with 14 unknown parameters [max: 50] *** Estimate b07everything_000197 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost lambda_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_ma beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho 0 -0.57 -0.94 0 0 0 -0.84 1.3 -0.0031 0 0 -1 -0.046 0 0 5.6e+03 2.6 10 1 ++ 1 -0.57 -0.94 0 0 0 -0.84 1.3 -0.0031 0 0 -1 -0.046 0 0 5.6e+03 2.6 2.1 -2.1e+02 - 2 -0.57 -0.94 0 0 0 -0.84 1.3 -0.0031 0 0 -1 -0.046 0 0 5.6e+03 2.6 1 -2.6 - 3 -0.39 -1.5 0 0 0 -1.3 0.24 -0.0064 0 0 -1.3 0.078 0 0 5.5e+03 0.58 10 1 ++ 4 -0.51 -1.4 0 0 0 -1.1 0.19 -0.0059 0 0 -1.2 0.22 0 0 5.5e+03 0.022 1e+02 0.97 ++ 5 -0.52 -1.3 0 0 0 -1.1 0.19 -0.0059 0 0 -1.2 0.21 0 0 5.5e+03 5.2e-05 1e+03 1 ++ 6 -0.52 -1.3 0 0 0 -1.1 0.19 -0.0059 0 0 -1.2 0.21 0 0 5.5e+03 1.4e-09 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 28/100 Biogeme parameters read from biogeme.toml. Model with 17 unknown parameters [max: 50] *** Estimate b07everything_000198 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.5e+03 2.6 10 1 ++ 1 5.2e+03 0.87 1e+02 1 ++ 2 5.2e+03 0.069 1e+03 1.1 ++ 3 5.2e+03 0.0012 1e+04 1 ++ 4 5.2e+03 4.5e-07 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 29/100 Biogeme parameters read from biogeme.toml. Model with 11 unknown parameters [max: 50] *** Estimate b07everything_000199 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho 0 -0.94 -0.11 -0.014 0.52 -1 -0.65 -0.0031 -0.59 -0.29 -0.046 -0.24 5.4e+03 2.5 10 1 ++ 1 -1.2 0.4 0.18 2 -2.6 -1.3 -0.005 -0.36 0.097 -0.23 -1.4 4.9e+03 0.96 1e+02 1 ++ 2 -1.4 0.65 0.41 1.9 -2.9 -1.5 -0.0061 -0.36 0.082 -0.43 -1.8 4.9e+03 0.076 1e+03 1.1 ++ 3 -1.4 0.7 0.47 2 -2.9 -1.5 -0.0062 -0.36 0.08 -0.45 -1.9 4.9e+03 0.0016 1e+04 1 ++ 4 -1.4 0.7 0.47 2 -2.9 -1.5 -0.0062 -0.36 0.08 -0.45 -1.9 4.9e+03 8.6e-07 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 30/100 Biogeme parameters read from biogeme.toml. Model with 16 unknown parameters [max: 50] *** Estimate b07everything_000200 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 6.1e+03 2 1 0.51 + 1 5.7e+03 0.5 10 0.92 ++ 2 5.7e+03 0.5 5 -2.2e+03 - 3 5.7e+03 0.5 2.5 -18 - 4 5.7e+03 0.5 1.2 -0.14 - 5 5.5e+03 0.19 1.2 0.86 + 6 5.4e+03 0.053 12 1 ++ 7 5.4e+03 0.00089 1.2e+02 0.97 ++ 8 5.4e+03 2.4e-06 1.2e+02 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 14 unknown parameters [max: 50] *** Estimate b07everything_000201 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -1 0.4 0.04 -0.65 0 0 0 0 0 -0.29 -0.071 -0.12 0 0 5.6e+03 0.037 10 1.1 ++ 1 -1.2 0.89 0.71 -1.1 0 0 0 0 0 0.045 -0.044 -0.64 0 0 5.5e+03 0.0076 1e+02 1.1 ++ 2 -1.4 1.1 0.89 -1.1 0 0 0 0 0 0.057 -0.046 -0.68 0 0 5.5e+03 0.00067 1e+03 1.1 ++ 3 -1.4 1.1 0.91 -1.1 0 0 0 0 0 0.058 -0.046 -0.69 0 0 5.5e+03 6.1e-06 1e+04 1 ++ 4 -1.4 1.1 0.91 -1.1 0 0 0 0 0 0.058 -0.046 -0.69 0 0 5.5e+03 5.2e-10 1e+04 1 ++ Considering neighbor 1/20 for current solution Attempt 31/100 Biogeme parameters read from biogeme.toml. Model with 16 unknown parameters [max: 50] *** Estimate b07everything_000202 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.6e+03 0.038 10 1.1 ++ 1 5.3e+03 0.013 1e+02 1.1 ++ 2 5.3e+03 0.0006 1e+03 1 ++ 3 5.3e+03 3.1e-06 1e+03 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 5 unknown parameters [max: 50] *** Estimate b07everything_000203 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time b_cost b_headway asc_car Function Relgrad Radius Rho 0 -0.92 -1 -0.58 -0.0028 -0.66 5.6e+03 2.5 10 1 ++ 1 -0.53 -2.4 -2.3 -0.0062 -0.22 5.3e+03 0.4 1e+02 1 ++ 2 -0.27 -3.3 -2.3 -0.0053 -0.059 5.2e+03 0.0023 1e+03 1 ++ 3 -0.26 -3.3 -2.4 -0.0053 -0.055 5.2e+03 0.0058 1e+04 1 ++ 4 -0.26 -3.3 -2.4 -0.0053 -0.055 5.2e+03 3e-06 1e+04 1 ++ Considering neighbor 1/20 for current solution Attempt 32/100 Considering neighbor 0/20 for current solution Attempt 33/100 Biogeme parameters read from biogeme.toml. Model with 15 unknown parameters [max: 50] *** Estimate b07everything_000204 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.97 0.51 0.33 -0.63 0 0 0 -0.0026 0 0 -0.47 0.1 -0.43 0 0 5.6e+03 2.7 10 1.1 ++ 1 -1 0.91 0.71 -1.1 0 0 0 -0.0047 0 0 -0.042 -0.054 -0.68 0 0 5.5e+03 0.35 1e+02 1.1 ++ 2 -1.2 1.1 0.89 -1.1 0 0 0 -0.0053 0 0 -0.05 -0.044 -0.68 0 0 5.5e+03 0.026 1e+03 1 ++ 3 -1.2 1.1 0.91 -1.1 0 0 0 -0.0054 0 0 -0.05 -0.044 -0.68 0 0 5.5e+03 0.00018 1e+04 1 ++ 4 -1.2 1.1 0.91 -1.1 0 0 0 -0.0054 0 0 -0.05 -0.044 -0.68 0 0 5.5e+03 1.1e-08 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 34/100 Biogeme parameters read from biogeme.toml. Model with 13 unknown parameters [max: 50] *** Estimate b07everything_000205 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_ma beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho 0 -0.63 -0.9 0 0 0 -0.97 -0.003 0 0 -1 -0.032 0 0 5.6e+03 2.7 10 1.1 ++ 1 -0.55 -1.3 0 0 0 -1.1 -0.0052 0 0 -1.2 0.23 0 0 5.5e+03 0.33 1e+02 1.1 ++ 2 -0.53 -1.3 0 0 0 -1.1 -0.0059 0 0 -1.2 0.23 0 0 5.5e+03 0.013 1e+03 1 ++ 3 -0.53 -1.3 0 0 0 -1.1 -0.0059 0 0 -1.2 0.23 0 0 5.5e+03 1.8e-05 1e+04 1 ++ 4 -0.53 -1.3 0 0 0 -1.1 -0.0059 0 0 -1.2 0.23 0 0 5.5e+03 3.7e-11 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 35/100 Biogeme parameters read from biogeme.toml. Model with 20 unknown parameters [max: 50] *** Estimate b07everything_000206 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 6e+03 0.041 10 1 ++ 1 5.8e+03 0.023 1e+02 1.1 ++ 2 5.8e+03 0.0038 1e+03 1.1 ++ 3 5.8e+03 0.00012 1e+04 1 ++ 4 5.8e+03 1.3e-07 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 36/100 Biogeme parameters read from biogeme.toml. Model with 11 unknown parameters [max: 50] *** Estimate b07everything_000207 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost asc_car_ref asc_car_diff_ma asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho 0 -0.43 -0.72 0.31 -0.011 -0.9 2 -0.84 -0.31 -0.1 -0.23 -0.046 5.8e+03 0.12 1 0.6 + 1 -0.69 -1.3 0.68 0.032 -0.7 1.6 -1.8 -0.41 0.11 0.024 -0.11 5.2e+03 0.016 10 1.1 ++ 2 -0.69 -1.3 0.68 0.032 -0.7 1.6 -1.8 -0.41 0.11 0.024 -0.11 5.2e+03 0.016 5 -4e+03 - 3 -0.69 -1.3 0.68 0.032 -0.7 1.6 -1.8 -0.41 0.11 0.024 -0.11 5.2e+03 0.016 2.5 -24 - 4 -0.69 -1.3 0.68 0.032 -0.7 1.6 -1.8 -0.41 0.11 0.024 -0.11 5.2e+03 0.016 1.2 -1.4 - 5 -0.77 -1.2 0.78 0.11 -1.4 0.35 -2.2 -0.45 0.24 0.3 -0.13 5.1e+03 0.033 1.2 0.78 + 6 -0.45 -1.1 1 0.71 -1.7 0.43 -2.4 -0.4 0.36 0.18 -0.019 5e+03 0.0049 12 0.91 ++ 7 -0.44 -1.1 0.96 0.56 -1.7 0.41 -2.4 -0.4 0.37 0.18 -0.0041 5e+03 0.0001 1.2e+02 1 ++ 8 -0.44 -1.1 0.96 0.56 -1.7 0.41 -2.4 -0.4 0.37 0.18 -0.0041 5e+03 9.7e-08 1.2e+02 1 ++ Considering neighbor 0/20 for current solution Attempt 37/100 Biogeme parameters read from biogeme.toml. Model with 22 unknown parameters [max: 50] *** Estimate b07everything_000208 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.9e+03 0.041 10 1 ++ 1 5.5e+03 0.053 1e+02 0.97 ++ 2 5.5e+03 0.0043 1e+03 1 ++ 3 5.5e+03 0.00016 1e+04 1 ++ 4 5.5e+03 2.1e-07 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 38/100 Biogeme parameters read from biogeme.toml. Model with 13 unknown parameters [max: 50] *** Estimate b07everything_000209 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.083 -0.77 -0.62 0 0 0 -0.0027 0 0 -0.47 0.023 0 0 5.6e+03 2.9 10 1.1 ++ 1 0.34 -1.1 -1.1 0 0 0 -0.005 0 0 -0.38 0.33 0 0 5.4e+03 0.37 1e+02 1.1 ++ 2 0.41 -1.2 -1.1 0 0 0 -0.0057 0 0 -0.39 0.32 0 0 5.4e+03 0.018 1e+03 1 ++ 3 0.41 -1.2 -1.1 0 0 0 -0.0057 0 0 -0.39 0.32 0 0 5.4e+03 3.6e-05 1e+04 1 ++ 4 0.41 -1.2 -1.1 0 0 0 -0.0057 0 0 -0.39 0.32 0 0 5.4e+03 1.4e-10 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 39/100 Biogeme parameters read from biogeme.toml. Model with 8 unknown parameters [max: 50] *** Estimate b07everything_000210 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost b_headway asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho 0 -0.68 0.55 -1 1.8 -0.74 -0.0021 -0.46 -0.27 5.6e+03 2 1 0.69 + 1 -0.93 1.6 -0.93 1.3 -1.2 -0.0064 -0.47 -0.74 5.1e+03 0.24 10 1.1 ++ 2 -0.93 1.6 -0.93 1.3 -1.2 -0.0064 -0.47 -0.74 5.1e+03 0.24 4.9 -2.4e+03 - 3 -0.93 1.6 -0.93 1.3 -1.2 -0.0064 -0.47 -0.74 5.1e+03 0.24 2.5 -34 - 4 -0.93 1.6 -0.93 1.3 -1.2 -0.0064 -0.47 -0.74 5.1e+03 0.24 1.2 -2.9 - 5 -1.1 2.5 -1.9 0.12 -1.6 -0.003 -0.15 -1.4 5e+03 0.032 1.2 0.78 + 6 -0.87 2.2 -1.6 0.24 -1.5 -0.0064 -0.28 -1.8 4.9e+03 0.013 12 0.98 ++ 7 -0.9 2.2 -1.6 0.27 -1.5 -0.0061 -0.28 -1.9 4.9e+03 0.00024 1.2e+02 1 ++ 8 -0.9 2.2 -1.6 0.27 -1.5 -0.0061 -0.28 -1.9 4.9e+03 6.2e-07 1.2e+02 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 12 unknown parameters [max: 50] *** Estimate b07everything_000211 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_wi beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho 0 -1 -0.065 0 0 0 -0.72 0 0 -0.048 -0.46 0 0 5.9e+03 0.076 10 1.1 ++ 1 -2.1 2.3 0 0 0 -2.3 0 0 -0.87 1.3 0 0 5.3e+03 0.038 1e+02 1 ++ 2 -2.3 2.2 0 0 0 -2.7 0 0 -0.93 1.4 0 0 5.3e+03 0.0012 1e+03 1 ++ 3 -2.3 2.2 0 0 0 -2.7 0 0 -0.93 1.4 0 0 5.3e+03 5.7e-06 1e+03 1 ++ Considering neighbor 1/20 for current solution Attempt 40/100 Considering neighbor 0/20 for current solution Attempt 41/100 Biogeme parameters read from biogeme.toml. Model with 9 unknown parameters [max: 50] *** Estimate b07everything_000212 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost lambda_cost b_headway asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho 0 -0.66 0.5 -1 1.7 -0.73 1 -0.0022 -0.46 -0.25 5.5e+03 2 1 0.76 + 1 -0.9 1.5 -1.1 1.1 -1 0.9 -0.0058 -0.52 -0.65 5.1e+03 0.38 10 1.1 ++ 2 -0.45 2.1 -2.3 -0.068 -1.7 -0.26 -0.0061 -0.023 -1.7 5e+03 0.033 10 0.45 + 3 -0.92 2.2 -1.6 0.1 -1.5 -0.11 -0.006 -0.28 -1.8 4.9e+03 0.03 1e+02 1 ++ 4 -0.88 2.2 -1.6 0.29 -1.5 0.035 -0.0062 -0.27 -1.9 4.9e+03 0.0062 1e+03 0.97 ++ 5 -0.9 2.2 -1.6 0.28 -1.5 0.043 -0.0062 -0.28 -1.8 4.9e+03 0.00011 1e+04 1 ++ 6 -0.9 2.2 -1.6 0.28 -1.5 0.043 -0.0062 -0.28 -1.8 4.9e+03 8.1e-07 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 42/100 Considering neighbor 0/20 for current solution Attempt 43/100 Biogeme parameters read from biogeme.toml. Model with 17 unknown parameters [max: 50] *** Estimate b07everything_000213 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.7e+03 2.5 10 1 ++ 1 5.2e+03 0.86 1e+02 1 ++ 2 5.2e+03 0.085 1e+03 1.1 ++ 3 5.2e+03 0.002 1e+04 1 ++ 4 5.2e+03 1.4e-06 1e+04 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 22 unknown parameters [max: 50] *** Estimate b07everything_000214 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.9e+03 0.041 10 1 ++ 1 5.5e+03 0.053 1e+02 0.97 ++ 2 5.5e+03 0.0043 1e+03 1 ++ 3 5.5e+03 0.00016 1e+04 1 ++ 4 5.5e+03 2.1e-07 1e+04 1 ++ Considering neighbor 1/20 for current solution Attempt 44/100 Biogeme parameters read from biogeme.toml. Model with 7 unknown parameters [max: 50] *** Estimate b07everything_000215 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho 0 -0.72 0.44 -1 1.7 -0.56 -0.49 -0.24 5.6e+03 0.064 1 0.75 + 1 -1.2 1.4 -1.1 1.1 -1.2 -0.19 -0.7 5.1e+03 0.024 10 1.1 ++ 2 -1.2 1.4 -1.1 1.1 -1.2 -0.19 -0.7 5.1e+03 0.024 0.73 -0.33 - 3 -1.3 2.1 -1.5 0.34 -1.3 -0.26 -1.1 5e+03 0.016 7.3 1.1 ++ 4 -1.2 2.2 -1.6 0.27 -1.5 -0.15 -1.7 4.9e+03 0.0012 73 1 ++ 5 -1.2 2.2 -1.6 0.27 -1.5 -0.15 -1.9 4.9e+03 5.7e-05 7.3e+02 1 ++ 6 -1.2 2.2 -1.6 0.27 -1.5 -0.15 -1.9 4.9e+03 1.4e-07 7.3e+02 1 ++ Considering neighbor 0/20 for current solution Attempt 45/100 Biogeme parameters read from biogeme.toml. Model with 10 unknown parameters [max: 50] *** Estimate b07everything_000216 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho 0 -1 0 0 0 -1 0 0 -0.079 0 0 5.9e+03 0.076 10 1.1 ++ 1 -1.6 0 0 0 -1.8 0 0 -0.7 0 0 5.6e+03 0.0079 1e+02 1 ++ 2 -1.7 0 0 0 -2.2 0 0 -0.76 0 0 5.6e+03 0.0005 1e+03 1 ++ 3 -1.7 0 0 0 -2.2 0 0 -0.76 0 0 5.6e+03 1.8e-06 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 46/100 Biogeme parameters read from biogeme.toml. Model with 11 unknown parameters [max: 50] *** Estimate b07everything_000217 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.65 -0.65 0 0 0 -0.0026 0 0 -0.4 0 0 5.7e+03 2.5 10 1.1 ++ 1 -0.41 -1.1 0 0 0 -0.0047 0 0 -0.064 0 0 5.6e+03 0.22 1e+02 1.1 ++ 2 -0.39 -1.1 0 0 0 -0.0052 0 0 -0.062 0 0 5.6e+03 0.0049 1e+03 1 ++ 3 -0.39 -1.1 0 0 0 -0.0052 0 0 -0.062 0 0 5.6e+03 2.4e-06 1e+03 1 ++ Considering neighbor 0/20 for current solution Considering neighbor 1/20 for current solution Attempt 47/100 Biogeme parameters read from biogeme.toml. Model with 7 unknown parameters [max: 50] *** Estimate b07everything_000218 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho 0 -0.94 0.44 -1 -0.52 -0.0037 -0.72 -0.22 5.5e+03 2.6 10 1 ++ 1 -0.84 2 -2.7 -2.6 -0.0052 -0.24 0.95 5e+03 0.58 1e+02 1 ++ 2 -0.84 2.1 -3.1 -2.7 -0.0061 -0.27 1.1 4.9e+03 0.036 1e+03 1 ++ 3 -0.85 2.1 -3.1 -2.8 -0.0062 -0.28 1.2 4.9e+03 0.00012 1e+04 1 ++ 4 -0.85 2.1 -3.1 -2.8 -0.0062 -0.28 1.2 4.9e+03 2.6e-07 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 48/100 Biogeme parameters read from biogeme.toml. Model with 21 unknown parameters [max: 50] *** Estimate b07everything_000219 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.6e+03 3.3 10 1.1 ++ 1 5.4e+03 0.55 1e+02 1.1 ++ 2 5.4e+03 0.048 1e+03 1 ++ 3 5.4e+03 0.00034 1e+04 1 ++ 4 5.4e+03 1.7e-08 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 49/100 Biogeme parameters read from biogeme.toml. Model with 21 unknown parameters [max: 50] *** Estimate b07everything_000220 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.9e+03 2.5 10 1 ++ 1 5.8e+03 0.56 1e+02 1.1 ++ 2 5.7e+03 0.07 1e+03 1.1 ++ 3 5.7e+03 0.0016 1e+04 1 ++ 4 5.7e+03 9.4e-07 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 50/100 Biogeme parameters read from biogeme.toml. Model with 11 unknown parameters [max: 50] *** Estimate b07everything_000221 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.52 -1 0 0 0 -0.0026 0 0 -0.34 0 0 5.7e+03 2.5 10 1 ++ 1 -0.18 -1.5 0 0 0 -0.0048 0 0 0.1 0 0 5.6e+03 0.13 1e+02 1 ++ 2 -0.17 -1.6 0 0 0 -0.0053 0 0 0.091 0 0 5.6e+03 0.0022 1e+03 1 ++ 3 -0.17 -1.6 0 0 0 -0.0053 0 0 0.091 0 0 5.6e+03 5e-07 1e+03 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 10 unknown parameters [max: 50] *** Estimate b07everything_000222 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost lambda_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho 0 -0.92 -0.072 -0.011 -1 -0.7 1 -0.0024 -0.56 -0.27 -0.048 5.5e+03 2.3 10 1 ++ 1 -0.99 0.83 0.65 -2.7 -1.3 -0.15 -0.0047 -0.31 -0.031 -0.53 5.3e+03 0.65 10 0.88 + 2 -1.1 1.1 0.89 -3 -1.2 0.25 -0.0055 -0.25 -0.063 -0.64 5.3e+03 0.097 1e+02 1.2 ++ 3 -1.2 1.2 0.94 -3 -1.1 0.49 -0.0055 -0.22 -0.059 -0.63 5.2e+03 0.0061 1e+03 1.1 ++ 4 -1.2 1.2 0.95 -3 -1.1 0.56 -0.0055 -0.21 -0.059 -0.62 5.2e+03 0.00029 1e+04 1 ++ 5 -1.2 1.2 0.95 -3 -1.1 0.56 -0.0055 -0.21 -0.059 -0.62 5.2e+03 1.3e-06 1e+04 1 ++ Considering neighbor 1/20 for current solution Biogeme parameters read from biogeme.toml. Model with 8 unknown parameters [max: 50] *** Estimate b07everything_000223 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost lambda_cost b_headway asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho 0 -0.95 0.39 -1 -0.47 1 -0.0042 -0.84 -0.24 5.5e+03 2.5 10 1 ++ 1 -0.95 0.39 -1 -0.47 1 -0.0042 -0.84 -0.24 5.5e+03 2.5 1.6 -7.6 - 2 -1.3 2 -2 -1.2 0.85 -0.0031 -0.5 -0.7 5e+03 0.72 16 1 ++ 3 -1.3 2 -2 -1.2 0.85 -0.0031 -0.5 -0.7 5e+03 0.72 0.63 -0.076 - 4 -1 2.2 -2.4 -1.3 0.23 -0.0065 -0.36 -0.92 5e+03 0.035 6.3 1.1 ++ 5 -0.95 2.1 -2.9 -1.5 0.065 -0.0062 -0.32 -1.7 4.9e+03 0.034 63 1 ++ 6 -0.94 2.1 -3 -1.5 0.094 -0.0062 -0.31 -1.8 4.9e+03 0.0017 6.3e+02 1 ++ 7 -0.94 2.1 -3 -1.5 0.093 -0.0062 -0.31 -1.8 4.9e+03 7.4e-06 6.3e+03 1 ++ 8 -0.94 2.1 -3 -1.5 0.093 -0.0062 -0.31 -1.8 4.9e+03 7.1e-08 6.3e+03 1 ++ Considering neighbor 2/20 for current solution Attempt 51/100 Biogeme parameters read from biogeme.toml. Model with 16 unknown parameters [max: 50] *** Estimate b07everything_000224 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.4e+03 0.045 10 1.1 ++ 1 5.3e+03 0.012 1e+02 1.1 ++ 2 5.3e+03 0.0009 1e+03 1 ++ 3 5.3e+03 7.2e-06 1e+04 1 ++ 4 5.3e+03 6.1e-10 1e+04 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 13 unknown parameters [max: 50] *** Estimate b07everything_000225 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.53 0.32 -1 0 0 0 -0.0036 0 0 -0.41 -0.16 0 0 5.6e+03 2.6 10 1 ++ 1 -0.53 2 -1.6 0 0 0 -0.0051 0 0 0.14 -1 0 0 5.2e+03 0.87 1e+02 0.97 ++ 2 -0.61 1.9 -1.6 0 0 0 -0.0059 0 0 0.11 -1.3 0 0 5.2e+03 0.039 1e+03 1 ++ 3 -0.62 1.9 -1.6 0 0 0 -0.006 0 0 0.11 -1.3 0 0 5.2e+03 0.00019 1e+04 1 ++ 4 -0.62 1.9 -1.6 0 0 0 -0.006 0 0 0.11 -1.3 0 0 5.2e+03 4.2e-09 1e+04 1 ++ Considering neighbor 1/20 for current solution Attempt 52/100 Biogeme parameters read from biogeme.toml. Model with 8 unknown parameters [max: 50] *** Estimate b07everything_000226 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho 0 -1 -0.098 -0.023 -0.9 -0.46 -0.47 -0.33 -0.035 5.7e+03 0.05 10 1 ++ 1 -1.1 0.79 0.64 -2.9 -2.1 -0.076 0.2 -0.041 5.2e+03 0.02 1e+02 1 ++ 2 -1.3 1.1 0.91 -3.2 -2.4 -0.072 0.17 -0.21 5.2e+03 0.0017 1e+03 1.1 ++ 3 -1.4 1.1 0.95 -3.2 -2.4 -0.07 0.16 -0.23 5.2e+03 2.6e-05 1e+04 1 ++ 4 -1.4 1.1 0.95 -3.2 -2.4 -0.07 0.16 -0.23 5.2e+03 1e-08 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 53/100 Biogeme parameters read from biogeme.toml. Model with 16 unknown parameters [max: 50] *** Estimate b07everything_000227 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.8e+03 0.039 10 1.1 ++ 1 5.3e+03 0.058 1e+02 0.98 ++ 2 5.3e+03 0.0041 1e+03 1 ++ 3 5.3e+03 0.00015 1e+04 1 ++ 4 5.3e+03 1.7e-07 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 54/100 Biogeme parameters read from biogeme.toml. Model with 12 unknown parameters [max: 50] *** Estimate b07everything_000228 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.45 -0.7 -1 0 0 0 0 0 -0.43 -0.12 0 0 5.6e+03 0.044 10 1 ++ 1 0.32 -1.1 -2.8 0 0 0 0 0 -0.13 0.31 0 0 5.4e+03 0.013 1e+02 1 ++ 2 0.38 -1.2 -3 0 0 0 0 0 -0.12 0.32 0 0 5.4e+03 0.00038 1e+03 1 ++ 3 0.38 -1.2 -3 0 0 0 0 0 -0.12 0.32 0 0 5.4e+03 3.7e-07 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 55/100 Considering neighbor 0/20 for current solution Attempt 56/100 Biogeme parameters read from biogeme.toml. Model with 11 unknown parameters [max: 50] *** Estimate b07everything_000229 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost lambda_cost beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho 0 -1 0 0 0 -0.3 2 0 0 -0.13 0 0 6e+03 0.08 10 1 ++ 1 -1 0 0 0 -0.3 2 0 0 -0.13 0 0 6e+03 0.08 5 -8.3e+05 - 2 -1 0 0 0 -0.3 2 0 0 -0.13 0 0 6e+03 0.08 2.5 -42 - 3 -1.5 0 0 0 -1.7 -0.5 0 0 -1.3 0 0 5.9e+03 0.082 2.5 0.2 + 4 -1.5 0 0 0 -1.7 -0.5 0 0 -1.3 0 0 5.9e+03 0.082 1 -0.66 - 5 -1.4 0 0 0 -0.71 -0.45 0 0 -0.58 0 0 5.7e+03 0.026 1 0.82 + 6 -1.7 0 0 0 -1.4 0.59 0 0 -0.93 0 0 5.7e+03 0.026 1 0.38 + 7 -1.7 0 0 0 -1.1 0.38 0 0 -0.88 0 0 5.7e+03 0.0015 10 1.1 ++ 8 -1.7 0 0 0 -1.1 0.17 0 0 -0.88 0 0 5.7e+03 0.0012 1e+02 0.98 ++ 9 -1.7 0 0 0 -1.1 0.17 0 0 -0.88 0 0 5.7e+03 3.3e-06 1e+02 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 18 unknown parameters [max: 50] *** Estimate b07everything_000230 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 6e+03 0.073 10 1.1 ++ 1 5.5e+03 0.031 1e+02 0.98 ++ 2 5.5e+03 0.0011 1e+03 1 ++ 3 5.5e+03 4.7e-06 1e+03 1 ++ Considering neighbor 1/20 for current solution Attempt 57/100 Biogeme parameters read from biogeme.toml. Model with 15 unknown parameters [max: 50] *** Estimate b07everything_000231 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho 0 -1 -0.18 -0.017 0 0 0 -0.67 -0.005 0 0 -0.74 -0.36 -0.043 0 0 5.7e+03 2.3 10 1 ++ 1 -1.9 0.85 0.8 0 0 0 -0.94 -0.0052 0 0 -1 0.18 -0.054 0 0 5.5e+03 0.56 1e+02 1.1 ++ 2 -2.2 1.2 1.1 0 0 0 -0.96 -0.0055 0 0 -1 0.16 -0.097 0 0 5.5e+03 0.071 1e+03 1.1 ++ 3 -2.3 1.2 1.2 0 0 0 -0.96 -0.0056 0 0 -1 0.16 -0.097 0 0 5.5e+03 0.0017 1e+04 1 ++ 4 -2.3 1.2 1.2 0 0 0 -0.96 -0.0056 0 0 -1 0.16 -0.097 0 0 5.5e+03 1.1e-06 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 58/100 Biogeme parameters read from biogeme.toml. Model with 17 unknown parameters [max: 50] *** Estimate b07everything_000232 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.5e+03 2.7 10 1.1 ++ 1 5.4e+03 0.39 1e+02 1.1 ++ 2 5.4e+03 0.032 1e+03 1.1 ++ 3 5.4e+03 0.00027 1e+04 1 ++ 4 5.4e+03 2.3e-08 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 59/100 Biogeme parameters read from biogeme.toml. Model with 12 unknown parameters [max: 50] *** Estimate b07everything_000233 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost b_headway asc_car_ref asc_car_diff_ma asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho 0 -0.5 -0.79 0.093 -0.0049 -0.88 1.6 -1 0.0035 -0.21 0.00042 -0.13 -0.038 5.4e+03 2.2 1 0.9 + 1 -0.18 -1.2 1.1 0.056 -1.5 0.76 -1.1 -0.0049 -0.46 0.21 0.093 -0.099 5.1e+03 0.88 10 1 ++ 2 -0.051 -1.1 0.96 0.57 -1.8 0.39 -1.1 -0.0058 -0.41 0.3 0.068 -0.17 5.1e+03 0.034 1e+02 0.91 ++ 3 -0.12 -1.1 0.96 0.53 -1.7 0.44 -1.1 -0.0059 -0.47 0.31 0.077 -0.14 5.1e+03 0.00022 1e+03 1 ++ 4 -0.12 -1.1 0.96 0.53 -1.7 0.44 -1.1 -0.0059 -0.47 0.31 0.077 -0.14 5.1e+03 7.1e-07 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 60/100 Biogeme parameters read from biogeme.toml. Model with 10 unknown parameters [max: 50] *** Estimate b07everything_000234 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_ma asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho 0 -0.65 -0.72 0.42 0.13 -0.63 -0.9 -0.69 0.0066 0.24 0.016 5.3e+03 0.043 10 1.1 ++ 1 -0.58 -1 0.76 0.4 -1.1 -1.1 -0.56 0.3 0.12 -0.07 5.1e+03 0.01 1e+02 1.1 ++ 2 -0.68 -1.1 0.94 0.56 -1.2 -1.1 -0.56 0.33 0.12 -0.069 5.1e+03 0.00059 1e+03 1 ++ 3 -0.68 -1.1 0.94 0.56 -1.2 -1.1 -0.56 0.33 0.12 -0.069 5.1e+03 4.5e-06 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 61/100 Biogeme parameters read from biogeme.toml. Model with 19 unknown parameters [max: 50] *** Estimate b07everything_000235 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.8e+03 2.9 10 1 ++ 1 5.7e+03 0.32 1e+02 1.1 ++ 2 5.7e+03 0.014 1e+03 1 ++ 3 5.7e+03 2.1e-05 1e+04 1 ++ 4 5.7e+03 4.6e-11 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 62/100 Biogeme parameters read from biogeme.toml. Model with 12 unknown parameters [max: 50] *** Estimate b07everything_000236 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.24 -0.56 -1 0 0 0 0 0 -0.26 -0.026 0 0 5.6e+03 0.04 10 1 ++ 1 0.45 -1.2 -1.5 0 0 0 0 0 -0.062 0.28 0 0 5.4e+03 0.0081 1e+02 1 ++ 2 0.45 -1.3 -1.6 0 0 0 0 0 -0.062 0.27 0 0 5.4e+03 0.00022 1e+03 1 ++ 3 0.45 -1.3 -1.6 0 0 0 0 0 -0.062 0.27 0 0 5.4e+03 2.4e-07 1e+03 1 ++ Considering neighbor 0/20 for current solution Considering neighbor 1/20 for current solution Attempt 63/100 Biogeme parameters read from biogeme.toml. Model with 17 unknown parameters [max: 50] *** Estimate b07everything_000237 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.6e+03 2.3 10 1 ++ 1 5.3e+03 1.1 1e+02 1 ++ 2 5.3e+03 0.095 1e+03 1.1 ++ 3 5.3e+03 0.0024 1e+04 1 ++ 4 5.3e+03 1.9e-06 1e+04 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 14 unknown parameters [max: 50] *** Estimate b07everything_000238 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho 0 -1 -0.34 -0.026 0 0 0 -0.29 0 0 -0.35 -0.24 -0.019 0 0 5.9e+03 0.039 10 1.1 ++ 1 -2.1 0.81 0.76 0 0 0 -0.86 0 0 -0.88 0.18 0.015 0 0 5.6e+03 0.023 1e+02 1.1 ++ 2 -2.5 1.2 1.1 0 0 0 -0.95 0 0 -0.9 0.16 -0.097 0 0 5.5e+03 0.0038 1e+03 1.1 ++ 3 -2.5 1.2 1.2 0 0 0 -0.95 0 0 -0.9 0.16 -0.1 0 0 5.5e+03 0.00011 1e+04 1 ++ 4 -2.5 1.2 1.2 0 0 0 -0.95 0 0 -0.9 0.16 -0.1 0 0 5.5e+03 1e-07 1e+04 1 ++ Considering neighbor 1/20 for current solution Biogeme parameters read from biogeme.toml. Model with 10 unknown parameters [max: 50] *** Estimate b07everything_000239 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost lambda_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho 0 -0.53 -0.019 -0.005 -1 -0.46 1 -0.0034 -0.36 -0.21 -0.03 5.5e+03 2.4 10 1 ++ 1 -0.87 0.83 0.79 -1.5 -1.1 -0.38 -0.005 -0.15 -0.079 -0.65 5.3e+03 0.63 10 0.88 + 2 -1.2 1.1 0.97 -1.5 -1.4 0.29 -0.0055 -0.29 -0.069 -0.7 5.3e+03 0.046 1e+02 0.91 ++ 3 -1.2 1.2 0.99 -1.5 -1.1 0.36 -0.0055 -0.19 -0.072 -0.66 5.3e+03 0.0008 1e+03 0.97 ++ 4 -1.2 1.2 1 -1.6 -1.1 0.4 -0.0055 -0.2 -0.069 -0.65 5.3e+03 0.00023 1e+04 0.97 ++ 5 -1.2 1.2 1 -1.6 -1.1 0.4 -0.0055 -0.2 -0.069 -0.65 5.3e+03 6.3e-07 1e+04 1 ++ Considering neighbor 2/20 for current solution Biogeme parameters read from biogeme.toml. Model with 11 unknown parameters [max: 50] *** Estimate b07everything_000240 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho 0 -0.53 -0.061 -0.0074 0.31 -1 -0.5 -0.0042 -0.4 -0.22 -0.027 -0.14 5.4e+03 2.4 10 1 ++ 1 -0.93 0.41 0.2 2.1 -1.6 -0.99 -0.0052 -0.19 0.017 -0.2 -0.21 5e+03 1.1 1e+02 0.99 ++ 2 -1.2 0.67 0.4 2 -1.7 -1.1 -0.006 -0.2 0.0097 -0.3 -0.22 5e+03 0.082 1e+03 1 ++ 3 -1.2 0.72 0.45 2 -1.7 -1.1 -0.0062 -0.2 0.0086 -0.3 -0.2 5e+03 0.0016 1e+04 1 ++ 4 -1.2 0.72 0.45 2 -1.7 -1.1 -0.0062 -0.2 0.0086 -0.3 -0.2 5e+03 8.9e-07 1e+04 1 ++ Considering neighbor 3/20 for current solution Considering neighbor 4/20 for current solution Attempt 64/100 Biogeme parameters read from biogeme.toml. Model with 17 unknown parameters [max: 50] *** Estimate b07everything_000241 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.8e+03 0.087 1 0.74 + 1 5.3e+03 0.024 10 0.98 ++ 2 5.3e+03 0.043 10 0.36 + 3 5.2e+03 0.0014 1e+02 1.1 ++ 4 5.2e+03 0.0017 1e+03 0.99 ++ 5 5.2e+03 9.9e-06 1e+04 1 ++ 6 5.2e+03 3.1e-10 1e+04 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 11 unknown parameters [max: 50] *** Estimate b07everything_000242 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho 0 -0.64 -0.021 -0.016 0.32 -1 1.4 -0.58 -0.4 -0.3 -0.026 -0.17 5.5e+03 0.068 10 0.91 ++ 1 -0.64 -0.021 -0.016 0.32 -1 1.4 -0.58 -0.4 -0.3 -0.026 -0.17 5.5e+03 0.068 5 -7.7e+02 - 2 -0.64 -0.021 -0.016 0.32 -1 1.4 -0.58 -0.4 -0.3 -0.026 -0.17 5.5e+03 0.068 2.5 -17 - 3 -0.64 -0.021 -0.016 0.32 -1 1.4 -0.58 -0.4 -0.3 -0.026 -0.17 5.5e+03 0.068 1.2 -1 - 4 -1.3 0.52 0.0085 1.6 -1.6 0.6 -1.2 -0.13 0.0032 -0.078 -0.38 5e+03 0.0097 12 1 ++ 5 -1.4 0.68 0.52 1.8 -1.7 0.37 -1.1 -0.067 0.027 -0.3 -0.29 5e+03 0.0013 1.2e+02 0.99 ++ 6 -1.5 0.71 0.51 1.9 -1.7 0.38 -1.1 -0.083 0.027 -0.31 -0.28 5e+03 1.5e-05 1.2e+03 1 ++ 7 -1.5 0.71 0.51 1.9 -1.7 0.38 -1.1 -0.083 0.027 -0.31 -0.28 5e+03 1.8e-09 1.2e+03 1 ++ Considering neighbor 1/20 for current solution Attempt 65/100 Biogeme parameters read from biogeme.toml. Model with 8 unknown parameters [max: 50] *** Estimate b07everything_000243 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho 0 -0.66 -0.044 -0.014 -1 -0.35 -0.29 -0.23 -0.024 5.6e+03 0.055 10 1 ++ 1 -1.1 0.82 0.68 -1.5 -2.1 -0.02 0.17 -0.0051 5.2e+03 0.015 1e+02 1 ++ 2 -1.3 1.1 0.97 -1.6 -2.3 -0.052 0.15 -0.18 5.2e+03 0.0012 1e+03 1.1 ++ 3 -1.4 1.2 1 -1.6 -2.3 -0.052 0.14 -0.2 5.2e+03 2.1e-05 1e+04 1 ++ 4 -1.4 1.2 1 -1.6 -2.3 -0.052 0.14 -0.2 5.2e+03 6.7e-09 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 66/100 Biogeme parameters read from biogeme.toml. Model with 11 unknown parameters [max: 50] *** Estimate b07everything_000244 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time lambda_tt b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.66 -1 2 0 0 0 0 0 -0.67 0 0 6.3e+03 0.14 1 0.42 + 1 -0.89 0 2.1 0 0 0 0 0 0.077 0 0 6.2e+03 0.29 1 0.19 + 2 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 1 0.43 + 3 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 0.5 -14 - 4 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 0.25 -6.5 - 5 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 0.12 -4.2 - 6 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 0.062 -1.6 - 7 -1.4 0.0095 3.1 0 0 0 0 0 -0.53 0 0 5.9e+03 1.1 0.062 0.5 + 8 -1.4 0.0095 3.1 0 0 0 0 0 -0.53 0 0 5.9e+03 1.1 0.031 -0.15 - 9 -1.5 -0.022 3.1 0 0 0 0 0 -0.54 0 0 5.9e+03 0.33 0.031 0.14 + 10 -1.5 -0.022 3.1 0 0 0 0 0 -0.54 0 0 5.9e+03 0.33 0.016 -0.32 - 11 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.16 0.94 ++ 12 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.078 -7.3 - 13 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.039 -5.4 - 14 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.02 -4.4 - 15 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.0098 -3.8 - 16 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.0049 -1.5 - 17 -1.5 -0.0013 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.0088 0.0049 0.86 + 18 -1.5 -0.0012 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.0032 0.049 1 ++ 19 -1.5 -0.0014 3 0 0 0 0 0 -0.58 0 0 5.9e+03 0.0027 0.49 1 ++ 20 -1.4 -0.0054 2.5 0 0 0 0 0 -0.54 0 0 5.9e+03 0.097 0.49 0.54 + 21 -1.5 -0.024 2 0 0 0 0 0 -0.57 0 0 5.8e+03 0.083 4.9 1.2 ++ 22 -1.2 -0.26 -0.73 0 0 0 0 0 -0.39 0 0 5.8e+03 0.038 4.9 0.65 + 23 -1.2 -0.26 -0.73 0 0 0 0 0 -0.39 0 0 5.8e+03 0.038 2.4 -2.5e+02 - 24 -1.2 -0.26 -0.73 0 0 0 0 0 -0.39 0 0 5.8e+03 0.038 1.2 -3.1 - 25 -0.48 -1.5 0.14 0 0 0 0 0 0.35 0 0 5.6e+03 0.032 12 1.3 ++ 26 -0.35 -1.6 0.53 0 0 0 0 0 0.24 0 0 5.6e+03 0.0079 12 0.84 + 27 -0.4 -1.6 0.46 0 0 0 0 0 0.2 0 0 5.5e+03 0.00061 1.2e+02 1.1 ++ 28 -0.41 -1.6 0.45 0 0 0 0 0 0.2 0 0 5.5e+03 6.9e-06 1.2e+03 1 ++ 29 -0.41 -1.6 0.45 0 0 0 0 0 0.2 0 0 5.5e+03 3.9e-10 1.2e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 67/100 Biogeme parameters read from biogeme.toml. Model with 5 unknown parameters [max: 50] *** Estimate b07everything_000245 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time lambda_tt b_cost asc_car Function Relgrad Radius Rho 0 -0.69 -1 1.8 -0.64 -0.56 5.8e+03 0.086 1 0.66 + 1 -0.41 -1.8 0.78 -1.6 -0.11 5.5e+03 0.034 1 0.87 + 2 -0.65 -1.4 0.6 -0.95 -0.11 5.4e+03 0.0035 10 0.98 ++ 3 -0.59 -1.5 0.39 -1 -0.097 5.4e+03 0.0015 1e+02 0.95 ++ 4 -0.61 -1.5 0.41 -1 -0.11 5.4e+03 2e-05 1e+03 1 ++ 5 -0.61 -1.5 0.41 -1 -0.11 5.4e+03 8.8e-09 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 68/100 Biogeme parameters read from biogeme.toml. Model with 16 unknown parameters [max: 50] *** Estimate b07everything_000246 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 6.1e+03 2 1 0.51 + 1 5.7e+03 0.5 10 0.92 ++ 2 5.7e+03 0.5 5 -2.2e+03 - 3 5.7e+03 0.5 2.5 -18 - 4 5.7e+03 0.5 1.2 -0.14 - 5 5.5e+03 0.19 1.2 0.86 + 6 5.4e+03 0.053 12 1 ++ 7 5.4e+03 0.00089 1.2e+02 0.97 ++ 8 5.4e+03 2.4e-06 1.2e+02 1 ++ Considering neighbor 0/20 for current solution Considering neighbor 1/20 for current solution Attempt 69/100 Biogeme parameters read from biogeme.toml. Model with 23 unknown parameters [max: 50] *** Estimate b07everything_000247 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.8e+03 2.6 10 1 ++ 1 5.5e+03 0.94 1e+02 1 ++ 2 5.5e+03 0.089 1e+03 1.1 ++ 3 5.5e+03 0.0022 1e+04 1 ++ 4 5.5e+03 1.6e-06 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 70/100 Considering neighbor 0/20 for current solution Attempt 71/100 Biogeme parameters read from biogeme.toml. Model with 9 unknown parameters [max: 50] *** Estimate b07everything_000248 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho 0 -1 0.34 0.05 -0.62 -0.97 -0.0032 -0.59 -0.089 -0.14 5.4e+03 2.2 10 1.1 ++ 1 -1.2 0.91 0.72 -0.99 -1.1 -0.005 -0.4 -0.033 -0.61 5.3e+03 0.38 1e+02 1.1 ++ 2 -1.4 1.1 0.93 -1 -1.1 -0.0055 -0.39 -0.036 -0.66 5.3e+03 0.027 1e+03 1.1 ++ 3 -1.4 1.2 0.96 -1 -1.1 -0.0055 -0.39 -0.036 -0.65 5.3e+03 0.0014 1e+04 1 ++ 4 -1.4 1.2 0.96 -1 -1.1 -0.0055 -0.39 -0.036 -0.64 5.3e+03 0.00022 1e+05 1 ++ 5 -1.4 1.2 0.96 -1 -1.1 -0.0055 -0.39 -0.036 -0.64 5.3e+03 3.7e-06 1e+05 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 8 unknown parameters [max: 50] *** Estimate b07everything_000249 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost b_headway asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho 0 -0.67 0.5 -1 1.7 -0.91 -0.0026 -0.46 -0.23 5.5e+03 1.9 1 0.77 + 1 -0.94 1.5 -1.2 1 -1 -0.0053 -0.5 -0.36 5.1e+03 0.39 10 1.2 ++ 2 -0.32 2 -2.3 0.00089 -1.1 -0.0061 0.085 -0.36 5e+03 0.076 10 0.29 + 3 -0.73 2.1 -1.7 0.21 -1.1 -0.0061 -0.17 -0.34 5e+03 0.003 1e+02 1.1 ++ 4 -0.73 2.1 -1.7 0.38 -1.1 -0.0062 -0.18 -0.3 5e+03 0.0038 1e+03 0.99 ++ 5 -0.74 2.1 -1.7 0.39 -1.1 -0.0062 -0.19 -0.29 5e+03 1.1e-05 1e+04 1 ++ 6 -0.74 2.1 -1.7 0.39 -1.1 -0.0062 -0.19 -0.29 5e+03 2.9e-07 1e+04 1 ++ Considering neighbor 1/20 for current solution Attempt 72/100 Considering neighbor 0/20 for current solution Attempt 73/100 Considering neighbor 0/20 for current solution Attempt 74/100 Biogeme parameters read from biogeme.toml. Model with 15 unknown parameters [max: 50] *** Estimate b07everything_000250 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.91 -0.0041 -0.0085 -1 0 0 0 -0.0013 0 0 -0.41 -0.18 -0.05 0 0 5.7e+03 2.5 10 1 ++ 1 -0.76 0.79 0.6 -2.8 0 0 0 -0.0044 0 0 0.096 -0.061 -0.67 0 0 5.5e+03 0.52 1e+02 1.1 ++ 2 -0.94 1.1 0.84 -3 0 0 0 -0.0053 0 0 0.12 -0.073 -0.76 0 0 5.4e+03 0.059 1e+03 1.1 ++ 3 -0.97 1.1 0.88 -3 0 0 0 -0.0053 0 0 0.12 -0.074 -0.76 0 0 5.4e+03 0.00097 1e+04 1 ++ 4 -0.97 1.1 0.88 -3 0 0 0 -0.0053 0 0 0.12 -0.074 -0.76 0 0 5.4e+03 3.1e-07 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 75/100 Biogeme parameters read from biogeme.toml. Model with 16 unknown parameters [max: 50] *** Estimate b07everything_000251 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 6.1e+03 0.07 10 1.1 ++ 1 5.9e+03 0.0023 1e+02 1 ++ 2 5.9e+03 1.1e-05 1e+03 1 ++ 3 5.9e+03 2.6e-10 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 76/100 Considering neighbor 0/20 for current solution Attempt 77/100 Biogeme parameters read from biogeme.toml. Model with 16 unknown parameters [max: 50] *** Estimate b07everything_000252 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 6.1e+03 0.07 10 1.1 ++ 1 5.9e+03 0.0023 1e+02 1 ++ 2 5.9e+03 1.1e-05 1e+03 1 ++ 3 5.9e+03 2.6e-10 1e+03 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 7 unknown parameters [max: 50] *** Estimate b07everything_000253 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho 0 -0.71 0.47 -1 1.7 -0.56 -0.51 -0.24 5.7e+03 0.08 1 0.7 + 1 -1.1 1.5 -1 1.3 -1.6 0.04 -0.42 5.1e+03 0.025 10 1 ++ 2 -1.1 1.5 -1 1.3 -1.6 0.04 -0.42 5.1e+03 0.025 0.81 -0.21 - 3 -1.2 1.8 -1.5 0.44 -2 -0.059 -0.32 5e+03 0.018 8.1 1.1 ++ 4 -1.1 2.1 -1.6 0.33 -2.7 -0.12 1.3 5e+03 0.0031 81 0.98 ++ 5 -1.1 2.1 -1.6 0.34 -2.8 -0.12 1.2 5e+03 0.00014 8.1e+02 1 ++ 6 -1.1 2.1 -1.6 0.34 -2.8 -0.12 1.2 5e+03 5.9e-07 8.1e+02 1 ++ Considering neighbor 1/20 for current solution Biogeme parameters read from biogeme.toml. Model with 11 unknown parameters [max: 50] *** Estimate b07everything_000254 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time lambda_tt b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.66 -1 2 0 0 0 0 0 -0.67 0 0 6.3e+03 0.14 1 0.42 + 1 -0.89 0 2.1 0 0 0 0 0 0.077 0 0 6.2e+03 0.29 1 0.19 + 2 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 1 0.43 + 3 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 0.5 -14 - 4 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 0.25 -6.5 - 5 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 0.12 -4.2 - 6 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 0.062 -1.6 - 7 -1.4 0.0095 3.1 0 0 0 0 0 -0.53 0 0 5.9e+03 1.1 0.062 0.5 + 8 -1.4 0.0095 3.1 0 0 0 0 0 -0.53 0 0 5.9e+03 1.1 0.031 -0.15 - 9 -1.5 -0.022 3.1 0 0 0 0 0 -0.54 0 0 5.9e+03 0.33 0.031 0.14 + 10 -1.5 -0.022 3.1 0 0 0 0 0 -0.54 0 0 5.9e+03 0.33 0.016 -0.32 - 11 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.16 0.94 ++ 12 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.078 -7.3 - 13 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.039 -5.4 - 14 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.02 -4.4 - 15 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.0098 -3.8 - 16 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.0049 -1.5 - 17 -1.5 -0.0013 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.0088 0.0049 0.86 + 18 -1.5 -0.0012 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.0032 0.049 1 ++ 19 -1.5 -0.0014 3 0 0 0 0 0 -0.58 0 0 5.9e+03 0.0027 0.49 1 ++ 20 -1.4 -0.0054 2.5 0 0 0 0 0 -0.54 0 0 5.9e+03 0.097 0.49 0.54 + 21 -1.5 -0.024 2 0 0 0 0 0 -0.57 0 0 5.8e+03 0.083 4.9 1.2 ++ 22 -1.2 -0.26 -0.73 0 0 0 0 0 -0.39 0 0 5.8e+03 0.038 4.9 0.65 + 23 -1.2 -0.26 -0.73 0 0 0 0 0 -0.39 0 0 5.8e+03 0.038 2.4 -2.5e+02 - 24 -1.2 -0.26 -0.73 0 0 0 0 0 -0.39 0 0 5.8e+03 0.038 1.2 -3.1 - 25 -0.48 -1.5 0.14 0 0 0 0 0 0.35 0 0 5.6e+03 0.032 12 1.3 ++ 26 -0.35 -1.6 0.53 0 0 0 0 0 0.24 0 0 5.6e+03 0.0079 12 0.84 + 27 -0.4 -1.6 0.46 0 0 0 0 0 0.2 0 0 5.5e+03 0.00061 1.2e+02 1.1 ++ 28 -0.41 -1.6 0.45 0 0 0 0 0 0.2 0 0 5.5e+03 6.9e-06 1.2e+03 1 ++ 29 -0.41 -1.6 0.45 0 0 0 0 0 0.2 0 0 5.5e+03 3.9e-10 1.2e+03 1 ++ Considering neighbor 2/20 for current solution Biogeme parameters read from biogeme.toml. Model with 4 unknown parameters [max: 50] *** Estimate b07everything_000255 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time b_cost asc_car Function Relgrad Radius Rho 0 -0.95 -0.63 -0.93 -0.55 5.5e+03 0.036 10 1.1 ++ 1 -0.86 -1 -1 -0.29 5.4e+03 0.0047 1e+02 1.1 ++ 2 -0.85 -1.1 -1 -0.27 5.4e+03 7.3e-05 1e+03 1 ++ 3 -0.85 -1.1 -1 -0.27 5.4e+03 1.7e-08 1e+03 1 ++ Considering neighbor 3/20 for current solution Attempt 78/100 Considering neighbor 0/20 for current solution Attempt 79/100 Biogeme parameters read from biogeme.toml. Model with 19 unknown parameters [max: 50] *** Estimate b07everything_000256 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.9e+03 2.7 10 1 ++ 1 5.5e+03 0.93 1e+02 0.97 ++ 2 5.5e+03 0.053 1e+03 1 ++ 3 5.5e+03 0.00034 1e+04 1 ++ 4 5.5e+03 1.4e-08 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 80/100 Biogeme parameters read from biogeme.toml. Model with 20 unknown parameters [max: 50] *** Estimate b07everything_000257 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.6e+03 0.044 10 1.1 ++ 1 5.4e+03 0.01 1e+02 1.1 ++ 2 5.4e+03 0.00066 1e+03 1 ++ 3 5.4e+03 3.5e-06 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 81/100 Biogeme parameters read from biogeme.toml. Model with 14 unknown parameters [max: 50] *** Estimate b07everything_000258 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.65 0.022 -0.01 -1 0 0 0 0 0 -0.22 -0.2 -0.029 0 0 5.7e+03 0.045 10 1 ++ 1 -0.91 0.8 0.63 -1.5 0 0 0 0 0 0.25 -0.088 -0.72 0 0 5.5e+03 0.009 1e+02 1.1 ++ 2 -1.2 1.1 0.9 -1.6 0 0 0 0 0 0.24 -0.083 -0.77 0 0 5.5e+03 0.001 1e+03 1.1 ++ 3 -1.2 1.1 0.94 -1.6 0 0 0 0 0 0.24 -0.084 -0.77 0 0 5.5e+03 1.8e-05 1e+04 1 ++ 4 -1.2 1.1 0.94 -1.6 0 0 0 0 0 0.24 -0.084 -0.77 0 0 5.5e+03 5e-09 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 82/100 Biogeme parameters read from biogeme.toml. Model with 11 unknown parameters [max: 50] *** Estimate b07everything_000259 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho 0 -0.71 0.11 -0.0033 0.73 -0.88 -1 -0.0025 -0.25 -0.13 -0.05 -0.3 5.2e+03 2.4 10 1.1 ++ 1 -1.4 0.48 0.26 1.9 -0.98 -1.4 -0.0051 -0.52 0.11 -0.25 -1.3 5e+03 0.77 1e+02 1.1 ++ 2 -1.6 0.66 0.48 1.9 -1 -1.5 -0.0062 -0.55 0.12 -0.4 -1.8 5e+03 0.055 1e+03 1.1 ++ 3 -1.7 0.71 0.54 1.9 -1 -1.5 -0.0063 -0.55 0.12 -0.42 -1.9 5e+03 0.0009 1e+04 1 ++ 4 -1.7 0.71 0.54 1.9 -1 -1.5 -0.0063 -0.55 0.12 -0.42 -1.9 5e+03 8e-07 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 83/100 Biogeme parameters read from biogeme.toml. Model with 15 unknown parameters [max: 50] *** Estimate b07everything_000260 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.52 0.052 -0.0022 -1 0 0 0 -0.0028 0 0 -0.25 -0.14 -0.034 0 0 5.6e+03 2.5 10 1 ++ 1 -0.7 0.81 0.63 -1.5 0 0 0 -0.0047 0 0 0.15 -0.089 -0.71 0 0 5.5e+03 0.44 1e+02 1.1 ++ 2 -0.94 1.1 0.9 -1.6 0 0 0 -0.0053 0 0 0.13 -0.081 -0.76 0 0 5.5e+03 0.05 1e+03 1.1 ++ 3 -0.98 1.1 0.94 -1.6 0 0 0 -0.0053 0 0 0.13 -0.082 -0.76 0 0 5.5e+03 0.00074 1e+04 1 ++ 4 -0.98 1.1 0.94 -1.6 0 0 0 -0.0053 0 0 0.13 -0.082 -0.76 0 0 5.5e+03 2e-07 1e+04 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 12 unknown parameters [max: 50] *** Estimate b07everything_000261 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.9 1 -0.68 0 0 0 0 0 -0.38 -0.45 0 0 5.5e+03 0.041 10 1.1 ++ 1 -1 1.7 -1.1 0 0 0 0 0 0.053 -1.2 0 0 5.3e+03 0.0097 1e+02 1.1 ++ 2 -1.1 1.8 -1.1 0 0 0 0 0 0.057 -1.4 0 0 5.3e+03 0.00044 1e+03 1 ++ 3 -1.1 1.8 -1.1 0 0 0 0 0 0.057 -1.4 0 0 5.3e+03 1.2e-06 1e+03 1 ++ Considering neighbor 1/20 for current solution Attempt 84/100 Biogeme parameters read from biogeme.toml. Model with 8 unknown parameters [max: 50] *** Estimate b07everything_000262 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost b_headway asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho 0 -0.57 -0.93 -0.9 1.9 -1 0.0068 -0.25 0.035 5.7e+03 2.3 1 0.71 + 1 0.067 -1.7 0.034 2.1 -1 -0.0079 -1.3 0.42 5.6e+03 0.81 1 0.37 + 2 0.067 -1.7 0.034 2.1 -1 -0.0079 -1.3 0.42 5.6e+03 0.81 0.5 -1.9 - 3 0.067 -1.7 0.034 2.1 -1 -0.0079 -1.3 0.42 5.6e+03 0.81 0.25 -0.28 - 4 0.0011 -1.6 -0.2 2.3 -1 -0.0082 -1.3 0.37 5.5e+03 0.069 0.25 0.25 + 5 -0.2 -1.5 -0.16 2.1 -1.1 -0.0072 -1.2 0.3 5.4e+03 0.013 2.5 1.1 ++ 6 -0.2 -1.5 -0.16 2.1 -1.1 -0.0072 -1.2 0.3 5.4e+03 0.013 1.2 0.032 - 7 -0.33 -1.1 -0.68 0.82 -1.2 -0.0017 -0.87 0.44 5.3e+03 0.38 1.2 0.46 + 8 0.42 -1.3 -1.4 0.087 -1.1 -0.0055 -0.58 0.35 5.2e+03 0.056 12 0.94 ++ 9 0.54 -1.3 -1.6 0.41 -1.1 -0.0057 -0.54 0.36 5.2e+03 0.016 12 0.72 + 10 0.5 -1.3 -1.5 0.35 -1.1 -0.0057 -0.55 0.34 5.2e+03 0.00061 1.2e+02 1 ++ 11 0.5 -1.3 -1.5 0.34 -1.1 -0.0057 -0.55 0.34 5.2e+03 6.7e-06 1.2e+03 1 ++ 12 0.5 -1.3 -1.5 0.34 -1.1 -0.0057 -0.55 0.34 5.2e+03 3.9e-08 1.2e+03 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 7 unknown parameters [max: 50] *** Estimate b07everything_000263 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time lambda_tt b_cost lambda_cost b_headway asc_car Function Relgrad Radius Rho 0 -0.68 -1 1.9 -0.84 1 -0.0011 -0.46 5.8e+03 1.7 1 0.67 + 1 -0.69 -1.8 0.85 -1.5 0.34 0.00068 -0.23 5.5e+03 0.27 10 0.92 ++ 2 -0.28 -1.5 0.63 -1.1 0.45 -0.006 -0.23 5.4e+03 0.05 1e+02 1 ++ 3 -0.29 -1.6 0.45 -1.1 0.61 -0.0054 -0.21 5.3e+03 0.0017 1e+03 0.93 ++ 4 -0.31 -1.6 0.46 -1.1 0.58 -0.0054 -0.22 5.3e+03 4.7e-05 1e+04 1 ++ 5 -0.31 -1.6 0.46 -1.1 0.58 -0.0054 -0.22 5.3e+03 5.3e-07 1e+04 1 ++ Considering neighbor 1/20 for current solution Attempt 85/100 Biogeme parameters read from biogeme.toml. Model with 22 unknown parameters [max: 50] *** Estimate b07everything_000264 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.9e+03 0.041 10 1 ++ 1 5.5e+03 0.053 1e+02 0.97 ++ 2 5.5e+03 0.0043 1e+03 1 ++ 3 5.5e+03 0.00016 1e+04 1 ++ 4 5.5e+03 2.1e-07 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 86/100 Biogeme parameters read from biogeme.toml. Model with 10 unknown parameters [max: 50] *** Estimate b07everything_000265 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost lambda_cost b_headway asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho 0 -0.37 -0.71 0.82 -0.68 -1 1 -0.0024 -0.47 -0.17 -0.36 5.2e+03 2.6 10 1.1 ++ 1 -0.48 -0.93 1.9 -0.98 -1.9 -0.44 -0.0054 -0.89 0.34 -1.5 5e+03 0.88 10 0.6 + 2 -0.48 -1.1 2 -0.95 -1.1 -0.44 -0.0065 -0.88 0.49 -1.9 4.9e+03 0.088 1e+02 0.95 ++ 3 -0.42 -1.1 2 -1 -1.7 0.27 -0.0068 -0.96 0.47 -2 4.9e+03 0.05 1e+02 0.48 + 4 -0.38 -1.1 2 -1.1 -1.5 0.17 -0.0068 -0.89 0.45 -1.9 4.9e+03 0.0052 1e+03 1 ++ 5 -0.4 -1.1 2 -1 -1.5 0.1 -0.0068 -0.9 0.46 -1.9 4.9e+03 0.00016 1e+04 0.99 ++ 6 -0.4 -1.1 2 -1 -1.5 0.1 -0.0068 -0.9 0.46 -1.9 4.9e+03 3.8e-07 1e+04 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 11 unknown parameters [max: 50] *** Estimate b07everything_000266 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost lambda_cost b_headway asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho 0 -0.57 -0.89 0.63 -0.93 1.8 -1 1 0.0053 -0.28 -0.011 -0.28 5.5e+03 2.4 1 0.8 + 1 -0.23 -0.96 1.6 -0.93 1.3 -1 0.69 -0.0064 -0.55 0.085 -0.71 5e+03 0.63 10 1.1 ++ 2 -0.23 -0.96 1.6 -0.93 1.3 -1 0.69 -0.0064 -0.55 0.085 -0.71 5e+03 0.63 2.8 -73 - 3 -0.23 -0.96 1.6 -0.93 1.3 -1 0.69 -0.0064 -0.55 0.085 -0.71 5e+03 0.63 1.4 -5.8 - 4 -0.08 -1.1 2.3 -2.2 -0.078 -1.7 0.06 -0.0035 -0.57 0.61 -1.3 4.9e+03 0.13 1.4 0.58 + 5 -0.036 -1.2 2.1 -1.6 0.091 -1.5 0.017 -0.0068 -0.68 0.46 -1.9 4.8e+03 0.018 14 1 ++ 6 -0.057 -1.2 2.1 -1.6 0.23 -1.5 0.033 -0.0066 -0.69 0.47 -2 4.8e+03 0.0029 1.4e+02 0.97 ++ 7 -0.057 -1.2 2.1 -1.6 0.23 -1.5 0.033 -0.0066 -0.69 0.47 -2 4.8e+03 3.3e-06 1.4e+02 1 ++ Considering neighbor 1/20 for current solution Attempt 87/100 Biogeme parameters read from biogeme.toml. Model with 11 unknown parameters [max: 50] *** Estimate b07everything_000267 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho 0 -0.94 -0.12 -0.015 0.51 -1 -0.79 -0.0032 -0.59 -0.27 -0.042 -0.21 5.4e+03 2.3 10 1 ++ 1 -1 0.41 0.22 1.9 -2.8 -1 -0.005 -0.27 0.066 -0.21 -0.36 5e+03 0.97 1e+02 1 ++ 2 -1.2 0.66 0.45 1.9 -3.2 -1.1 -0.0061 -0.24 0.04 -0.3 -0.27 5e+03 0.084 1e+03 1.1 ++ 3 -1.2 0.71 0.5 1.9 -3.2 -1.1 -0.0062 -0.24 0.039 -0.3 -0.27 5e+03 0.0016 1e+04 1 ++ 4 -1.2 0.71 0.5 1.9 -3.2 -1.1 -0.0062 -0.24 0.039 -0.3 -0.27 5e+03 7.4e-07 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 88/100 Biogeme parameters read from biogeme.toml. Model with 12 unknown parameters [max: 50] *** Estimate b07everything_000268 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_ma beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho 0 -0.73 -0.88 0 0 0 -1 0 0 -0.6 -0.26 0 0 5.6e+03 0.039 10 1 ++ 1 -0.77 -1.3 0 0 0 -2.1 0 0 -0.87 0.14 0 0 5.4e+03 0.0055 1e+02 1 ++ 2 -0.78 -1.3 0 0 0 -2.2 0 0 -0.93 0.16 0 0 5.4e+03 0.00015 1e+03 1 ++ 3 -0.78 -1.3 0 0 0 -2.2 0 0 -0.93 0.16 0 0 5.4e+03 1e-07 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 89/100 Biogeme parameters read from biogeme.toml. Model with 16 unknown parameters [max: 50] *** Estimate b07everything_000269 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.6e+03 0.038 10 1.1 ++ 1 5.3e+03 0.013 1e+02 1.1 ++ 2 5.3e+03 0.0006 1e+03 1 ++ 3 5.3e+03 3.1e-06 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 90/100 Considering neighbor 0/20 for current solution Attempt 91/100 Biogeme parameters read from biogeme.toml. Model with 5 unknown parameters [max: 50] *** Estimate b07everything_000270 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time lambda_tt b_cost asc_car Function Relgrad Radius Rho 0 -0.68 -1 1.9 -0.64 -0.59 6e+03 0.11 1 0.59 + 1 -0.92 -0.75 1.6 -1.6 -0.024 5.4e+03 0.031 10 0.96 ++ 2 -0.92 -0.75 1.6 -1.6 -0.024 5.4e+03 0.031 1.2 -1.7 - 3 -0.68 -1.8 0.39 -2.3 0.03 5.3e+03 0.04 1.2 0.88 + 4 -0.48 -1.7 0.47 -2.3 0.054 5.2e+03 0.0029 12 0.95 ++ 5 -0.5 -1.7 0.48 -2.4 0.057 5.2e+03 2.1e-05 1.2e+02 1 ++ 6 -0.5 -1.7 0.48 -2.4 0.057 5.2e+03 1.2e-09 1.2e+02 1 ++ Considering neighbor 0/20 for current solution Attempt 92/100 Biogeme parameters read from biogeme.toml. Model with 9 unknown parameters [max: 50] *** Estimate b07everything_000271 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho 0 -0.61 -0.89 0.72 -1 -0.83 0.0016 -0.41 -0.079 -0.26 5.3e+03 3.1 10 1.1 ++ 1 -0.16 -1 1.8 -2.7 -2.4 -0.0045 -0.54 0.32 0.79 4.8e+03 0.68 1e+02 1.1 ++ 2 -0.044 -1.1 2 -3.1 -2.7 -0.0065 -0.65 0.42 1 4.8e+03 0.059 1e+03 1.1 ++ 3 -0.041 -1.1 2 -3.1 -2.8 -0.0068 -0.68 0.44 1 4.8e+03 0.00068 1e+04 1 ++ 4 -0.042 -1.1 2 -3.1 -2.8 -0.0068 -0.68 0.44 1 4.8e+03 1.8e-05 1e+05 1 ++ 5 -0.042 -1.1 2 -3.1 -2.8 -0.0068 -0.68 0.44 1 4.8e+03 6.5e-08 1e+05 1 ++ Considering neighbor 0/20 for current solution Attempt 93/100 Biogeme parameters read from biogeme.toml. Model with 12 unknown parameters [max: 50] *** Estimate b07everything_000272 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.24 -0.56 -1 0 0 0 0 0 -0.26 -0.026 0 0 5.6e+03 0.04 10 1 ++ 1 0.45 -1.2 -1.5 0 0 0 0 0 -0.062 0.28 0 0 5.4e+03 0.0081 1e+02 1 ++ 2 0.45 -1.3 -1.6 0 0 0 0 0 -0.062 0.27 0 0 5.4e+03 0.00022 1e+03 1 ++ 3 0.45 -1.3 -1.6 0 0 0 0 0 -0.062 0.27 0 0 5.4e+03 2.4e-07 1e+03 1 ++ Considering neighbor 0/20 for current solution Attempt 94/100 Biogeme parameters read from biogeme.toml. Model with 19 unknown parameters [max: 50] *** Estimate b07everything_000273 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.8e+03 2.9 10 1 ++ 1 5.7e+03 0.32 1e+02 1.1 ++ 2 5.7e+03 0.014 1e+03 1 ++ 3 5.7e+03 2.1e-05 1e+04 1 ++ 4 5.7e+03 4.6e-11 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 95/100 Considering neighbor 0/20 for current solution Attempt 96/100 Biogeme parameters read from biogeme.toml. Model with 14 unknown parameters [max: 50] *** Estimate b07everything_000274 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.33 -0.57 0.63 -1 0 0 0 0 0 -0.27 -0.067 -0.26 0 0 5.4e+03 0.048 10 1 ++ 1 -0.054 -0.99 1.7 -1.6 0 0 0 0 0 -0.078 0.37 -1.1 0 0 5.1e+03 0.016 1e+02 1 ++ 2 -0.058 -1.2 1.8 -1.6 0 0 0 0 0 -0.1 0.39 -1.4 0 0 5.1e+03 0.00086 1e+03 1 ++ 3 -0.06 -1.2 1.8 -1.6 0 0 0 0 0 -0.1 0.39 -1.4 0 0 5.1e+03 6.1e-06 1e+04 1 ++ 4 -0.06 -1.2 1.8 -1.6 0 0 0 0 0 -0.1 0.39 -1.4 0 0 5.1e+03 3.1e-10 1e+04 1 ++ Considering neighbor 0/20 for current solution Attempt 97/100 Biogeme parameters read from biogeme.toml. Model with 16 unknown parameters [max: 50] *** Estimate b07everything_000275 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 5.5e+03 0.039 10 1 ++ 1 5.4e+03 0.0087 1e+02 1.1 ++ 2 5.4e+03 0.00053 1e+03 1.1 ++ 3 5.4e+03 4.5e-06 1e+03 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 9 unknown parameters [max: 50] *** Estimate b07everything_000276 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost lambda_cost asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho 0 -0.43 -0.58 0.47 -1 -0.62 1 -0.37 -0.19 -0.21 5.3e+03 0.05 10 1 ++ 1 -0.43 -0.58 0.47 -1 -0.62 1 -0.37 -0.19 -0.21 5.3e+03 0.05 1.3 -3 - 2 -0.32 -1.1 1.8 -1.6 -1.1 0.81 -0.38 0.15 -0.66 4.9e+03 0.019 13 1.1 ++ 3 -0.32 -1.1 1.8 -1.6 -1.1 0.81 -0.38 0.15 -0.66 4.9e+03 0.019 0.82 -2.4 - 4 -0.26 -1.2 2.2 -1.7 -1.3 -0.011 -0.37 0.38 -1.1 4.8e+03 0.0087 8.2 0.99 ++ 5 -0.38 -1.2 2.1 -1.6 -1.5 -0.026 -0.55 0.46 -1.8 4.8e+03 0.0014 82 1.1 ++ 6 -0.38 -1.2 2.1 -1.6 -1.5 -0.04 -0.56 0.46 -2 4.8e+03 8.5e-05 8.2e+02 1 ++ 7 -0.38 -1.2 2.1 -1.6 -1.5 -0.04 -0.56 0.46 -2 4.8e+03 2.8e-07 8.2e+02 1 ++ Considering neighbor 1/20 for current solution Attempt 98/100 Biogeme parameters read from biogeme.toml. Model with 6 unknown parameters [max: 50] *** Estimate b07everything_000277 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho 0 -0.52 -0.74 -1 -0.94 -0.46 -0.16 5.4e+03 0.044 10 1 ++ 1 0.21 -1.2 -2.9 -2.1 -0.24 0.26 5.1e+03 0.016 1e+02 1.1 ++ 2 0.31 -1.3 -3.3 -2.4 -0.27 0.33 5.1e+03 0.00089 1e+03 1 ++ 3 0.31 -1.3 -3.3 -2.4 -0.27 0.33 5.1e+03 2.8e-06 1e+03 1 ++ Considering neighbor 0/20 for current solution Biogeme parameters read from biogeme.toml. Model with 12 unknown parameters [max: 50] *** Estimate b07everything_000278 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho 0 -0.22 -0.77 -0.62 0 0 0 0 0 -0.42 0.022 0 0 5.6e+03 0.038 10 1.1 ++ 1 0.1 -1.1 -1.1 0 0 0 0 0 -0.29 0.33 0 0 5.5e+03 0.0078 1e+02 1.1 ++ 2 0.13 -1.2 -1.1 0 0 0 0 0 -0.28 0.33 0 0 5.5e+03 0.00024 1e+03 1 ++ 3 0.13 -1.2 -1.1 0 0 0 0 0 -0.28 0.33 0 0 5.5e+03 3.2e-07 1e+03 1 ++ Considering neighbor 1/20 for current solution Attempt 99/100 Biogeme parameters read from biogeme.toml. Model with 7 unknown parameters [max: 50] *** Estimate b07everything_000279 As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho 0 -0.73 0.39 -1 1.6 -0.73 -0.45 -0.21 5.5e+03 0.049 1 0.83 + 1 -1.1 1.4 -1.3 0.9 -1.1 -0.2 -0.35 5.1e+03 0.022 10 1.1 ++ 2 -0.87 2 -1.9 0.26 -1.1 0.037 -0.36 5e+03 0.012 10 0.82 + 3 -1 2 -1.7 0.36 -1.1 -0.061 -0.31 5e+03 0.0012 1e+02 1 ++ 4 -1 2 -1.7 0.38 -1.1 -0.064 -0.31 5e+03 3.1e-05 1e+03 1 ++ 5 -1 2 -1.7 0.38 -1.1 -0.064 -0.31 5e+03 3.6e-09 1e+03 1 ++ Considering neighbor 0/20 for current solution Pareto file has been updated: b22multiple_models.pareto Before the algorithm: 465 models, with 7 Pareto. After the algorithm: 501 models, with 7 Pareto. VNS algorithm completed. Postprocessing of the Pareto optimal solutions Pareto set initialized from file with 474 elements [7 Pareto] and 0 invalid elements. Biogeme parameters provided by the user. *** Initial values of the parameters are obtained from the file __b22multiple_models_000000.iter Parameter values restored from __b22multiple_models_000000.iter Starting values for the algorithm: {'asc_train_ref': -0.07367671352474071, 'asc_train_diff_male': -1.1590679270761561, 'asc_train_diff_with_ga': 2.1085409793549172, 'b_time': -1.6114913593274356, 'lambda_tt': 0.2233736569872712, 'b_cost': -1.4884009465532348, 'b_headway': -0.006638084810181024, 'asc_car_ref': -0.6960052685126517, 'asc_car_diff_male': 0.47396665105972735, 'asc_car_diff_with_ga': -2.0020626594355013} As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho 0 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.5 -0.85 - 1 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.25 -0.84 - 2 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.12 -0.83 - 3 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.062 -0.83 - 4 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.031 -0.83 - 5 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.016 -0.83 - 6 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.0078 -0.83 - 7 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.0039 -0.83 - 8 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.002 -0.83 - 9 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.00098 -0.83 - 10 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.00049 -0.83 - 11 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.00024 -0.83 - 12 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.00012 -0.83 - 13 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 6.1e-05 -0.83 - 14 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 3.1e-05 -0.83 - 15 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 1.5e-05 -0.83 - 16 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 7.6e-06 -0.83 - 17 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 3.8e-06 -0.83 - 18 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 1.9e-06 -0.83 - 19 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 9.5e-07 -0.83 - 20 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 4.8e-07 -0.83 - 21 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 2.4e-07 -0.83 - 22 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 1.2e-07 -0.83 - 23 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 6e-08 -0.83 - 24 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 3e-08 -0.83 - 25 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 1.5e-08 -0.83 - Optimization algorithm has *not* converged. Algorithm: Newton with trust region for simple bound constraints Cause of termination: Trust region is too small: 1.4901161193847656e-08 Number of iterations: 26 Proportion of Hessian calculation: 1/1 = 100.0% Optimization time: 0:00:00.861311 Calculate second derivatives and BHHH It seems that the optimization algorithm did not converge. Therefore, the results may not correspond to the maximum likelihood estimator. Check the specification of the model, or the criteria for convergence of the algorithm. File b22multiple_models_000000~00.html has been generated. File b22multiple_models_000000~00.yaml has been generated. Biogeme parameters provided by the user. *** Initial values of the parameters are obtained from the file __b22multiple_models_000001.iter Parameter values restored from __b22multiple_models_000001.iter Starting values for the algorithm: {'asc_train_ref': -0.06428873416454763, 'asc_train_diff_male': -1.1900399775238788, 'asc_train_diff_with_ga': 2.157415716942036, 'b_time': -1.6215306410844021, 'b_cost': -1.5023419723928042, 'b_headway': -0.006593342797065967, 'asc_car_ref': -0.683938377480246, 'asc_car_diff_male': 0.4537580026546972, 'asc_car_diff_with_ga': -1.9759512791566758} Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost b_headway asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho 0 0.94 -0.19 3.2 -2.6 5.6e-17 -0.5 0.99 -1.7 -0.55 -3 5.1e+04 0.62 1 0.9 + 1 0.94 -0.19 3.2 -2.6 5.6e-17 -0.5 0.99 -1.7 -0.55 -3 5.1e+04 0.62 0.5 -0.29 - 2 1.4 0.31 3.7 -2.1 0.5 -0.0023 1.5 -2.2 -1 -3.5 1.7e+04 1.1 0.5 0.62 + 3 1.4 0.27 3.7 -1.8 1 -0.041 1.5 -2.1 -0.99 -3.5 1.4e+04 0.049 0.5 0.53 + 4 1.4 0.27 3.7 -1.8 1 -0.041 1.5 -2.1 -0.99 -3.5 1.4e+04 0.049 0.25 -0.066 - 5 1.4 0.27 3.7 -1.8 1 -0.041 1.5 -2.1 -0.99 -3.5 1.4e+04 0.049 0.12 0.094 - 6 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.12 0.2 + 7 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.062 -0.017 - 8 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.031 0.022 - 9 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.016 0.039 - 10 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.0078 -0.71 - 11 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.0039 -0.4 - 12 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.002 -0.23 - 13 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.00098 -0.17 - 14 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.00049 -0.15 - 15 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.00024 -0.13 - 16 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.00012 -0.13 - 17 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 6.1e-05 -0.12 - 18 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 3.1e-05 -0.12 - 19 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 1.5e-05 -0.12 - 20 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 7.6e-06 -0.12 - 21 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 3.8e-06 -0.12 - 22 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 1.9e-06 -0.12 - 23 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 9.5e-07 -0.12 - 24 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 4.8e-07 -0.12 - 25 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 2.4e-07 -0.12 - 26 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 1.2e-07 -0.12 - 27 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 6e-08 -0.12 - 28 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 3e-08 -0.12 - 29 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 1.5e-08 -0.12 - Optimization algorithm has *not* converged. Algorithm: Newton with trust region for simple bound constraints Cause of termination: Trust region is too small: 1.4901161193847656e-08 Number of iterations: 30 Proportion of Hessian calculation: 5/5 = 100.0% Optimization time: 0:00:02.145363 Calculate second derivatives and BHHH It seems that the optimization algorithm did not converge. Therefore, the results may not correspond to the maximum likelihood estimator. Check the specification of the model, or the criteria for convergence of the algorithm. File b22multiple_models_000001~00.html has been generated. File b22multiple_models_000001~00.yaml has been generated. Biogeme parameters provided by the user. *** Initial values of the parameters are obtained from the file __b22multiple_models_000002.iter Parameter values restored from __b22multiple_models_000002.iter Starting values for the algorithm: {'asc_train_ref': -0.9179504404134234, 'asc_train_diff_with_ga': 2.2435709636922936, 'b_time': -1.5947261592634554, 'b_cost': -1.5032629167922742, 'b_headway': -0.0061230125995098954, 'asc_car_ref': -0.28199124501588113, 'asc_car_diff_with_ga': -1.8290175405420466} Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time b_cost b_headway asc_car Function Relgrad Radius Rho 0 0 -1.6 -1.5 -0.0061 0 6e+03 9.2 0.5 -21 - 1 0 -1.6 -1.5 -0.0061 0 6e+03 9.2 0.25 -80 - 2 0 -1.6 -1.5 -0.0061 0 6e+03 9.2 0.12 -43 - 3 0 -1.6 -1.5 -0.0061 0 6e+03 9.2 0.062 -19 - 4 0 -1.6 -1.5 -0.0061 0 6e+03 9.2 0.031 -5.9 - 5 0 -1.6 -1.5 -0.0061 0 6e+03 9.2 0.016 -0.46 - 6 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.16 1 ++ 7 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.078 -85 - 8 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.039 -56 - 9 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.02 -34 - 10 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.0098 -18 - 11 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.0049 -8.9 - 12 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.0024 -4.1 - 13 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.0012 -1.9 - 14 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.00061 -1.3 - 15 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.00031 -1.1 - 16 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.00015 -0.98 - 17 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 7.6e-05 -0.94 - 18 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 3.8e-05 -0.91 - 19 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 1.9e-05 -0.9 - 20 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 9.5e-06 -0.9 - 21 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 4.8e-06 -0.9 - 22 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 2.4e-06 -0.89 - 23 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 1.2e-06 -0.89 - 24 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 6e-07 -0.89 - 25 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 3e-07 -0.89 - 26 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 1.5e-07 -0.89 - 27 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 7.5e-08 -0.89 - 28 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 3.7e-08 -0.89 - 29 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 1.9e-08 -0.89 - 30 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 9.3e-09 -0.89 - Optimization algorithm has *not* converged. Algorithm: Newton with trust region for simple bound constraints Cause of termination: Trust region is too small: 9.313225746154779e-09 Number of iterations: 31 Proportion of Hessian calculation: 2/2 = 100.0% Optimization time: 0:00:00.579574 Calculate second derivatives and BHHH It seems that the optimization algorithm did not converge. Therefore, the results may not correspond to the maximum likelihood estimator. Check the specification of the model, or the criteria for convergence of the algorithm. File b22multiple_models_000002~00.html has been generated. File b22multiple_models_000002~00.yaml has been generated. Biogeme parameters provided by the user. *** Initial values of the parameters are obtained from the file __b22multiple_models_000003.iter Parameter values restored from __b22multiple_models_000003.iter Starting values for the algorithm: {'asc_train_ref': -0.37489922058477093, 'asc_train_diff_male': -1.171233665542124, 'asc_train_diff_with_ga': 2.1303933360865916, 'b_time': -1.6276384300357265, 'b_cost': -1.5013784331569677, 'asc_car_ref': -0.553122728238362, 'asc_car_diff_male': 0.4598529086351089, 'asc_car_diff_with_ga': -1.99738873512378} Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train b_time b_cost asc_car Function Relgrad Radius Rho 0 0 -1.6 -1.5 0 6.9e+03 0.24 0.5 0.067 - 1 0 -1.6 -1.5 0 6.9e+03 0.24 0.25 0.088 - 2 -0.25 -1.9 -1.3 -0.12 6.8e+03 0.23 0.25 0.18 + 3 -0.5 -2.1 -1 -0.24 6.7e+03 0.25 0.25 0.17 + 4 -0.75 -2.4 -0.75 -0.34 6.6e+03 0.27 0.25 0.15 + 5 -1 -2.6 -0.5 -0.43 6.6e+03 0.29 0.25 0.14 + 6 -1.2 -2.9 -0.25 -0.51 6.5e+03 0.31 0.25 0.13 + 7 -1.5 -3.1 -0.0014 -0.59 6.4e+03 0.33 0.25 0.11 + 8 -1.8 -3.4 0.25 -0.66 6.4e+03 0.35 0.25 0.1 + 9 -1.8 -3.4 0.25 -0.66 6.4e+03 0.35 0.12 0.092 - 10 -1.9 -3.5 0.37 -0.73 6.3e+03 0.37 0.12 0.2 + 11 -2 -3.6 0.5 -0.8 6.3e+03 0.39 0.12 0.18 + 12 -2.1 -3.8 0.62 -0.86 6.3e+03 0.4 0.12 0.17 + 13 -2.2 -3.9 0.75 -0.91 6.2e+03 0.42 0.12 0.15 + 14 -2.4 -4 0.87 -0.96 6.2e+03 0.44 0.12 0.13 + 15 -2.5 -4.1 1 -1 6.2e+03 0.46 0.12 0.12 + 16 -2.6 -4.3 1.1 -1 6.1e+03 0.47 0.12 0.11 + 17 -2.6 -4.3 1.1 -1 6.1e+03 0.47 0.062 0.093 - 18 -2.7 -4.3 1.2 -1.1 6.1e+03 0.48 0.062 0.22 + 19 -2.8 -4.4 1.2 -1.1 6.1e+03 0.48 0.062 0.2 + 20 -2.8 -4.4 1.3 -1.2 6.1e+03 0.49 0.062 0.17 + 21 -2.9 -4.5 1.4 -1.2 6.1e+03 0.49 0.062 0.16 + 22 -2.9 -4.6 1.4 -1.2 6e+03 0.5 0.062 0.14 + 23 -3 -4.6 1.5 -1.2 6e+03 0.51 0.062 0.12 + 24 -3.1 -4.7 1.6 -1.2 6e+03 0.51 0.062 0.11 + 25 -3.1 -4.7 1.6 -1.2 6e+03 0.51 0.031 0.099 - 26 -3.1 -4.7 1.6 -1.3 6e+03 0.52 0.031 0.15 + 27 -3.1 -4.8 1.6 -1.3 6e+03 0.52 0.031 0.14 + 28 -3.2 -4.8 1.7 -1.3 6e+03 0.52 0.031 0.12 + 29 -3.2 -4.8 1.7 -1.3 6e+03 0.53 0.031 0.11 + 30 -3.2 -4.8 1.7 -1.3 6e+03 0.53 0.016 0.099 - 31 -3.2 -4.8 1.7 -1.3 6e+03 0.53 0.016 0.2 + 32 -3.2 -4.8 1.7 -1.3 6e+03 0.53 0.016 0.18 + 33 -3.2 -4.9 1.7 -1.3 6e+03 0.53 0.016 0.16 + 34 -3.2 -4.9 1.7 -1.3 6e+03 0.53 0.016 0.14 + 35 -3.3 -4.9 1.8 -1.3 6e+03 0.53 0.016 0.13 + 36 -3.3 -4.9 1.8 -1.3 6e+03 0.53 0.016 0.11 + 37 -3.3 -4.9 1.8 -1.4 6e+03 0.53 0.016 0.1 + 38 -3.3 -4.9 1.8 -1.4 6e+03 0.53 0.0078 0.089 - 39 -3.3 -4.9 1.8 -1.4 6e+03 0.54 0.0078 0.18 + 40 -3.3 -4.9 1.8 -1.4 6e+03 0.54 0.0078 0.16 + 41 -3.3 -4.9 1.8 -1.4 6e+03 0.54 0.0078 0.14 + 42 -3.3 -5 1.8 -1.4 6e+03 0.54 0.0078 0.13 + 43 -3.3 -5 1.8 -1.4 6e+03 0.54 0.0078 0.11 + 44 -3.3 -5 1.8 -1.4 6e+03 0.54 0.0078 0.1 + 45 -3.3 -5 1.8 -1.4 6e+03 0.54 0.0039 0.09 - 46 -3.3 -5 1.8 -1.4 6e+03 0.54 0.0039 0.18 + 47 -3.4 -5 1.9 -1.4 6e+03 0.54 0.0039 0.16 + 48 -3.4 -5 1.9 -1.4 6e+03 0.54 0.0039 0.14 + 49 -3.4 -5 1.9 -1.4 6e+03 0.54 0.0039 0.13 + 50 -3.4 -5 1.9 -1.4 6e+03 0.54 0.0039 0.11 + 51 -3.4 -5 1.9 -1.4 6e+03 0.54 0.0039 0.1 + 52 -3.4 -5 1.9 -1.4 6e+03 0.54 0.002 0.09 - 53 -3.4 -5 1.9 -1.4 6e+03 0.54 0.002 0.18 + 54 -3.4 -5 1.9 -1.4 6e+03 0.54 0.002 0.16 + 55 -3.4 -5 1.9 -1.4 6e+03 0.54 0.002 0.14 + 56 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.002 0.13 + 57 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.002 0.11 + 58 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.002 0.1 + 59 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00098 0.091 - 60 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00098 0.18 + 61 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00098 0.16 + 62 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00098 0.14 + 63 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00098 0.13 + 64 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00098 0.12 + 65 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00098 0.1 + 66 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00049 0.092 - 67 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00049 0.18 + 68 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00049 0.16 + 69 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00049 0.15 + 70 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00049 0.13 + 71 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00049 0.12 + 72 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00049 0.1 + 73 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00024 0.092 - 74 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00024 0.18 + 75 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00024 0.16 + 76 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00024 0.15 + 77 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00024 0.13 + 78 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00024 0.12 + 79 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00024 0.1 + 80 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00012 0.093 - 81 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00012 0.19 + 82 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00012 0.17 + 83 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00012 0.15 + 84 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00012 0.13 + 85 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00012 0.12 + 86 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00012 0.1 + 87 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6.1e-05 0.093 - 88 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6.1e-05 0.19 + 89 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6.1e-05 0.17 + 90 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6.1e-05 0.15 + 91 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6.1e-05 0.13 + 92 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6.1e-05 0.12 + 93 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6.1e-05 0.11 + 94 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.1e-05 0.094 - 95 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.1e-05 0.19 + 96 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.1e-05 0.17 + 97 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.1e-05 0.15 + 98 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.1e-05 0.13 + 99 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.1e-05 0.12 + 100 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.1e-05 0.11 + 101 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.5e-05 0.095 - 102 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.5e-05 0.19 + 103 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.5e-05 0.17 + 104 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.5e-05 0.15 + 105 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.5e-05 0.13 + 106 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.5e-05 0.12 + 107 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.5e-05 0.11 + 108 -3.4 -5 1.9 -1.4 5.9e+03 0.54 7.6e-06 0.095 - 109 -3.4 -5 1.9 -1.4 5.9e+03 0.54 7.6e-06 0.19 + 110 -3.4 -5 1.9 -1.4 5.9e+03 0.54 7.6e-06 0.17 + 111 -3.4 -5 1.9 -1.4 5.9e+03 0.54 7.6e-06 0.15 + 112 -3.4 -5 1.9 -1.4 5.9e+03 0.54 7.6e-06 0.14 + 113 -3.4 -5 1.9 -1.4 5.9e+03 0.54 7.6e-06 0.12 + 114 -3.4 -5 1.9 -1.4 5.9e+03 0.54 7.6e-06 0.11 + 115 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.8e-06 0.096 - 116 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.8e-06 0.19 + 117 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.8e-06 0.17 + 118 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.8e-06 0.15 + 119 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.8e-06 0.14 + 120 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.8e-06 0.12 + 121 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.8e-06 0.11 + 122 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.9e-06 0.097 - 123 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.9e-06 0.19 + 124 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.9e-06 0.17 + 125 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.9e-06 0.15 + 126 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.9e-06 0.14 + 127 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.9e-06 0.12 + 128 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.9e-06 0.11 + 129 -3.4 -5 1.9 -1.4 5.9e+03 0.54 9.5e-07 0.097 - 130 -3.4 -5 1.9 -1.4 5.9e+03 0.54 9.5e-07 0.19 + 131 -3.4 -5 1.9 -1.4 5.9e+03 0.54 9.5e-07 0.17 + 132 -3.4 -5 1.9 -1.4 5.9e+03 0.54 9.5e-07 0.15 + 133 -3.4 -5 1.9 -1.4 5.9e+03 0.54 9.5e-07 0.14 + 134 -3.4 -5 1.9 -1.4 5.9e+03 0.54 9.5e-07 0.12 + 135 -3.4 -5 1.9 -1.4 5.9e+03 0.54 9.5e-07 0.11 + 136 -3.4 -5 1.9 -1.4 5.9e+03 0.54 4.8e-07 0.098 - 137 -3.4 -5 1.9 -1.4 5.9e+03 0.54 4.8e-07 0.2 + 138 -3.4 -5 1.9 -1.4 5.9e+03 0.54 4.8e-07 0.17 + 139 -3.4 -5 1.9 -1.4 5.9e+03 0.54 4.8e-07 0.16 + 140 -3.4 -5 1.9 -1.4 5.9e+03 0.54 4.8e-07 0.14 + 141 -3.4 -5 1.9 -1.4 5.9e+03 0.54 4.8e-07 0.12 + 142 -3.4 -5 1.9 -1.4 5.9e+03 0.54 4.8e-07 0.11 + 143 -3.4 -5 1.9 -1.4 5.9e+03 0.54 2.4e-07 0.098 - 144 -3.4 -5 1.9 -1.4 5.9e+03 0.54 2.4e-07 0.2 + 145 -3.4 -5 1.9 -1.4 5.9e+03 0.54 2.4e-07 0.18 + 146 -3.4 -5 1.9 -1.4 5.9e+03 0.54 2.4e-07 0.16 + 147 -3.4 -5 1.9 -1.4 5.9e+03 0.54 2.4e-07 0.14 + 148 -3.4 -5 1.9 -1.4 5.9e+03 0.54 2.4e-07 0.12 + 149 -3.4 -5 1.9 -1.4 5.9e+03 0.54 2.4e-07 0.11 + 150 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.2e-07 0.099 - 151 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.2e-07 0.2 + 152 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.2e-07 0.18 + 153 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.2e-07 0.16 + 154 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.2e-07 0.14 + 155 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.2e-07 0.13 + 156 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.2e-07 0.11 + 157 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6e-08 0.1 - 158 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6e-08 0.2 + 159 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6e-08 0.18 + 160 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6e-08 0.16 + 161 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6e-08 0.14 + 162 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6e-08 0.13 + 163 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6e-08 0.11 + 164 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6e-08 0.32 + 165 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3e-08 0.066 - 166 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3e-08 0.13 + 167 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3e-08 0.12 + 168 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3e-08 0.11 + 169 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3e-08 0.32 + 170 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3e-08 0.32 + 171 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.5e-08 0 + Optimization algorithm has *not* converged. Algorithm: Newton with trust region for simple bound constraints Cause of termination: Trust region is too small: 1.4901161193847656e-08 Number of iterations: 172 Proportion of Hessian calculation: 147/147 = 100.0% Optimization time: 0:00:01.829430 Calculate second derivatives and BHHH It seems that the optimization algorithm did not converge. Therefore, the results may not correspond to the maximum likelihood estimator. Check the specification of the model, or the criteria for convergence of the algorithm. File b22multiple_models_000003~00.html has been generated. File b22multiple_models_000003~00.yaml has been generated. Biogeme parameters provided by the user. *** Initial values of the parameters are obtained from the file __b22multiple_models_000004.iter Parameter values restored from __b22multiple_models_000004.iter Starting values for the algorithm: {'asc_train': -0.25828520695319407, 'b_time': -3.317406744595399, 'b_cost': -2.35293555588282, 'b_headway': -0.005287158125190783, 'asc_car': -0.054528515119256514} Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho 0 -0.42 -0.58 0.49 -4.3 -3 -0.36 -0.18 -0.22 6e+03 0.35 1 0.56 + 1 -0.42 -0.58 0.49 -4.3 -3 -0.36 -0.18 -0.22 6e+03 0.35 0.5 -0.31 - 2 -0.42 -0.58 0.49 -4.3 -3 -0.36 -0.18 -0.22 6e+03 0.35 0.25 0.00087 - 3 -0.42 -0.58 0.49 -4.3 -3 -0.36 -0.18 -0.22 6e+03 0.35 0.12 -0.21 - 4 -0.42 -0.58 0.49 -4.3 -3 -0.36 -0.18 -0.22 6e+03 0.35 0.062 -0.1 - 5 -0.42 -0.58 0.49 -4.3 -3 -0.36 -0.18 -0.22 6e+03 0.35 0.031 0.072 - 6 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.031 0.11 + 7 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.016 0.017 - 8 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.0078 0.023 - 9 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.0039 0.012 - 10 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.002 -0.15 - 11 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.00098 -0.15 - 12 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.00049 -0.15 - 13 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.00024 -0.15 - 14 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.00012 -0.15 - 15 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 6.1e-05 -0.15 - 16 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 3.1e-05 -0.15 - 17 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 1.5e-05 -0.15 - 18 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 7.6e-06 -0.15 - 19 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 3.8e-06 -0.15 - 20 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 1.9e-06 -0.15 - 21 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 9.5e-07 -0.15 - 22 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 4.8e-07 -0.15 - 23 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 2.4e-07 -0.15 - 24 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 1.2e-07 -0.15 - 25 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 6e-08 -0.15 - 26 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 3e-08 -0.15 - 27 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 1.5e-08 -0.15 - Optimization algorithm has *not* converged. Algorithm: Newton with trust region for simple bound constraints Cause of termination: Trust region is too small: 1.4901161193847656e-08 Number of iterations: 28 Proportion of Hessian calculation: 3/3 = 100.0% Optimization time: 0:00:00.907065 Calculate second derivatives and BHHH It seems that the optimization algorithm did not converge. Therefore, the results may not correspond to the maximum likelihood estimator. Check the specification of the model, or the criteria for convergence of the algorithm. File b22multiple_models_000004~00.html has been generated. File b22multiple_models_000004~00.yaml has been generated. Biogeme parameters provided by the user. *** Initial values of the parameters are obtained from the file __b22multiple_models_000005.iter Parameter values restored from __b22multiple_models_000005.iter Starting values for the algorithm: {'asc_train_ref': -1.2363986219196865, 'asc_train_diff_with_ga': 2.116104799141088, 'b_time': -2.936778941355896, 'b_cost': -1.472890077438725, 'asc_car_ref': -0.18981688978327133, 'asc_car_diff_with_ga': -1.8968233567325306} Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho 0 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.5 -0.94 - 1 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.25 -0.94 - 2 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.12 -0.94 - 3 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.062 -0.94 - 4 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.031 -0.94 - 5 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.016 -0.94 - 6 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.0078 -0.94 - 7 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.0039 -0.94 - 8 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.002 -0.94 - 9 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.00098 -0.94 - 10 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.00049 -0.94 - 11 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.00024 -0.94 - 12 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.00012 -0.94 - 13 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 6.1e-05 -0.94 - 14 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 3.1e-05 -0.94 - 15 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 1.5e-05 -0.94 - 16 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 7.6e-06 -0.94 - 17 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 3.8e-06 -0.94 - 18 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 1.9e-06 -0.94 - 19 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 9.5e-07 -0.94 - 20 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 4.8e-07 -0.94 - 21 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 2.4e-07 -0.94 - 22 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 1.2e-07 -0.94 - 23 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 6e-08 -0.94 - 24 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 3e-08 -0.94 - 25 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 1.5e-08 -0.94 - Optimization algorithm has *not* converged. Algorithm: Newton with trust region for simple bound constraints Cause of termination: Trust region is too small: 1.4901161193847656e-08 Number of iterations: 26 Proportion of Hessian calculation: 1/1 = 100.0% Optimization time: 0:00:00.763071 Calculate second derivatives and BHHH It seems that the optimization algorithm did not converge. Therefore, the results may not correspond to the maximum likelihood estimator. Check the specification of the model, or the criteria for convergence of the algorithm. File b22multiple_models_000005~00.html has been generated. File b22multiple_models_000005~00.yaml has been generated. Biogeme parameters provided by the user. *** Initial values of the parameters are obtained from the file __b22multiple_models_000006.iter Parameter values restored from __b22multiple_models_000006.iter Starting values for the algorithm: {'asc_train': -0.5046852334057811, 'b_time': -3.3215566932569422, 'b_cost': -2.3520626243030707, 'asc_car': 0.05192169085145166} Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. asc_train_ref asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho 0 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.5 -0.27 - 1 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.25 -0.16 - 2 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.12 -0.11 - 3 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.062 -0.09 - 4 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.031 -0.08 - 5 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.016 -0.074 - 6 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.0078 -0.072 - 7 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.0039 -0.07 - 8 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.002 -0.07 - 9 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.00098 -0.069 - 10 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.00049 -0.069 - 11 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.00024 -0.069 - 12 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.00012 -0.069 - 13 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 6.1e-05 -0.069 - 14 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 3.1e-05 -0.069 - 15 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 1.5e-05 -0.069 - 16 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 7.6e-06 -0.069 - 17 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 3.8e-06 -0.069 - 18 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 1.9e-06 -0.069 - 19 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 9.5e-07 -0.069 - 20 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 4.8e-07 -0.069 - 21 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 2.4e-07 -0.069 - 22 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 1.2e-07 -0.069 - 23 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 6e-08 -0.069 - 24 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 3e-08 -0.069 - 25 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 1.5e-08 -0.069 - Optimization algorithm has *not* converged. Algorithm: Newton with trust region for simple bound constraints Cause of termination: Trust region is too small: 1.4901161193847656e-08 Number of iterations: 26 Proportion of Hessian calculation: 1/1 = 100.0% Optimization time: 0:00:00.637017 Calculate second derivatives and BHHH It seems that the optimization algorithm did not converge. Therefore, the results may not correspond to the maximum likelihood estimator. Check the specification of the model, or the criteria for convergence of the algorithm. File b22multiple_models_000006~00.html has been generated. File b22multiple_models_000006~00.yaml has been generated. Pareto: 7 Considered: 474 Removed: 26 .. GENERATED FROM PYTHON SOURCE LINES 62-67 .. code-block:: Python summary, description = compile_estimation_results( non_dominated_models, use_short_names=True ) print(summary) .. rst-class:: sphx-glr-script-out .. code-block:: none Model_000000 ... Model_000006 Number of estimated parameters 9 ... 6 Sample size 6768 ... 6768 Final log likelihood -4810.59 ... -5837.478 Akaike Information Criterion 9639.18 ... 11686.96 Bayesian Information Criterion 9700.56 ... 11727.88 asc_train_ref (t-test) -0.0737 (-0.705) ... 0 (0) asc_train_diff_male (t-test) -1.16 (-13.7) ... asc_train_diff_with_ga (t-test) 2.11 (23.1) ... 0 (0) b_time (t-test) -1.61 (-20.2) ... -3.32 (-17.8) b_cost (t-test) -1.49 (-18.4) ... -2.35 (-20.1) b_headway (t-test) -0.00664 (-6.15) ... asc_car_ref (t-test) -0.696 (-6.52) ... 0 (0) asc_car_diff_male (t-test) 0.474 (4.39) ... asc_car_diff_with_ga (t-test) -2 (-9.32) ... 0 (0) lambda_tt (t-test) ... asc_train (t-test) ... asc_car (t-test) ... [17 rows x 7 columns] .. GENERATED FROM PYTHON SOURCE LINES 68-69 Explanation of the short names of the model. .. GENERATED FROM PYTHON SOURCE LINES 69-72 .. code-block:: Python for k, v in description.items(): if k != v: print(f'{k}: {v}') .. rst-class:: sphx-glr-script-out .. code-block:: none Model_000000: asc:MALE-GA;train_cost_catalog:log;train_headway_catalog:with_headway;train_tt_catalog:log Model_000001: asc:MALE-GA;train_cost_catalog:log;train_headway_catalog:with_headway;train_tt_catalog:boxcox Model_000002: asc:no_seg;train_cost_catalog:sqrt;train_headway_catalog:with_headway;train_tt_catalog:sqrt Model_000003: asc:no_seg;train_cost_catalog:sqrt;train_headway_catalog:without_headway;train_tt_catalog:sqrt Model_000004: asc:MALE-GA;train_cost_catalog:log;train_headway_catalog:without_headway;train_tt_catalog:log Model_000005: asc:GA;train_cost_catalog:log;train_headway_catalog:with_headway;train_tt_catalog:log Model_000006: asc:GA;train_cost_catalog:log;train_headway_catalog:without_headway;train_tt_catalog:sqrt .. rst-class:: sphx-glr-timing **Total running time of the script:** (4 minutes 35.728 seconds) .. _sphx_glr_download_auto_examples_swissmetro_plot_b22multiple_models.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_b22multiple_models.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_b22multiple_models.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_b22multiple_models.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_