Model run by stephane.hess using Apollo 0.3.6 on R 4.5.1 for Darwin. Please acknowledge the use of Apollo by citing Hess & Palma (2019) DOI 10.1016/j.jocm.2019.100170 www.ApolloChoiceModelling.com Model name : HB_MMNL Model description : HB model on mode choice SP data, mix of random and non-random parameters Model run at : 2025-09-19 16:19:27.128826 Estimation method : Hierarchical Bayes Number of individuals : 500 Number of rows in database : 7000 Number of modelled outcomes : 7000 Number of cores used : 1 Estimation carried out using RSGHB Burn-in iterations : 50000 Post burn-in iterations : 20000 Classical model fit statistics were calculated at parameter values obtained using averaging across the post burn-in iterations. LL(start) : -8196.02 LL at equal shares, LL(0) : -8196.02 LL at observed shares, LL(C) : -6706.94 LL(final) : -4913.58 Rho-squared vs equal shares : 0.4005 Adj.Rho-squared vs equal shares : 0.3904 Rho-squared vs observed shares : 0.2674 Adj.Rho-squared vs observed shares : 0.255 AIC : 9993.16 BIC : 10562.01 Equiv. estimated parameters : 83 (non-random parameters : 6) (means of random parameters : 11) (covariance matrix terms : 66) Time taken (hh:mm:ss) : 00:07:37.43 pre-estimation : 00:00:0.21 estimation : 00:07:35.63 post-estimation : 00:00:1.6 Summary of parameter chains Non-random coefficients Mean SD asc_car 0.0000 NA asc_bus_interaction_female 0.1098 0.0186 asc_air_interaction_female 0.0722 0.0335 asc_rail_interaction_female 0.0419 0.0307 b_tt_interaction_business -0.0078 0.0007 b_cost_interaction_business 0.0282 0.0034 cost_income_elast -0.6480 0.0277 b_no_frills 0.0000 NA Results for posterior means for random coefficients Mean SD asc_bus -1.5723 0.0281 asc_air -0.7461 0.0486 asc_rail -1.6923 0.0561 b_tt_car -0.0109 0.0009 b_tt_bus -0.0155 0.0013 b_tt_air -0.0113 0.0007 b_tt_rail -0.0051 0.0003 b_access -0.0180 0.0019 b_cost -0.0753 0.0056 b_wifi 1.0160 0.1458 b_food 0.4176 0.0983 Summary of distributions of random coeffients (after distributional transforms) Mean SD asc_bus -1.5675 0.4587 asc_air -0.7411 0.4330 asc_rail -1.6988 0.3972 b_tt_car -0.0109 0.0025 b_tt_bus -0.0155 0.0041 b_tt_air -0.0112 0.0048 b_tt_rail -0.0051 0.0021 b_access -0.0180 0.0091 b_cost -0.0755 0.0161 b_wifi 1.0119 0.4355 b_food 0.4164 0.3694 Covariance matrix of random coeffients (after distributional transforms) asc_bus asc_air asc_rail b_tt_car b_tt_bus b_tt_air b_tt_rail asc_bus 0.2104 -0.0090 -0.0177 -1e-04 -6e-04 0e+00 0e+00 asc_air -0.0090 0.1875 0.0362 1e-04 1e-04 -5e-04 1e-04 asc_rail -0.0177 0.0362 0.1577 3e-04 4e-04 4e-04 -2e-04 b_tt_car -0.0001 0.0001 0.0003 0e+00 0e+00 0e+00 0e+00 b_tt_bus -0.0006 0.0001 0.0004 0e+00 0e+00 0e+00 0e+00 b_tt_air 0.0000 -0.0005 0.0004 0e+00 0e+00 0e+00 0e+00 b_tt_rail 0.0000 0.0001 -0.0002 0e+00 0e+00 0e+00 0e+00 b_access -0.0002 -0.0009 0.0002 0e+00 0e+00 0e+00 0e+00 b_cost -0.0001 -0.0008 0.0005 0e+00 0e+00 0e+00 0e+00 b_wifi 0.0057 -0.0022 -0.0104 2e-04 -1e-04 -2e-04 1e-04 b_food 0.0031 0.0098 0.0018 1e-04 1e-04 -2e-04 0e+00 b_access b_cost b_wifi b_food asc_bus -2e-04 -1e-04 0.0057 0.0031 asc_air -9e-04 -8e-04 -0.0022 0.0098 asc_rail 2e-04 5e-04 -0.0104 0.0018 b_tt_car 0e+00 0e+00 0.0002 0.0001 b_tt_bus 0e+00 0e+00 -0.0001 0.0001 b_tt_air 0e+00 0e+00 -0.0002 -0.0002 b_tt_rail 0e+00 0e+00 0.0001 0.0000 b_access 1e-04 0e+00 0.0008 0.0002 b_cost 0e+00 3e-04 -0.0007 -0.0004 b_wifi 8e-04 -7e-04 0.1896 0.0388 b_food 2e-04 -4e-04 0.0388 0.1365 Correlation matrix of random coeffients (after distributional transforms) asc_bus asc_air asc_rail b_tt_car b_tt_bus b_tt_air b_tt_rail asc_bus 1.0000 -0.0455 -0.0973 -0.0641 -0.3372 -0.0021 -0.0223 asc_air -0.0455 1.0000 0.2105 0.1085 0.0749 -0.2481 0.1257 asc_rail -0.0973 0.2105 1.0000 0.3304 0.2691 0.1888 -0.1938 b_tt_car -0.0641 0.1085 0.3304 1.0000 0.2772 0.0689 0.2064 b_tt_bus -0.3372 0.0749 0.2691 0.2772 1.0000 0.0650 0.1176 b_tt_air -0.0021 -0.2481 0.1888 0.0689 0.0650 1.0000 0.0223 b_tt_rail -0.0223 0.1257 -0.1938 0.2064 0.1176 0.0223 1.0000 b_access -0.0550 -0.2277 0.0563 0.2571 0.0151 -0.0561 0.0943 b_cost -0.0107 -0.1156 0.0788 0.2191 0.3143 -0.0062 0.0666 b_wifi 0.0287 -0.0118 -0.0602 0.2114 -0.0504 -0.1047 0.0621 b_food 0.0185 0.0611 0.0120 0.1368 0.0491 -0.1177 0.0227 b_access b_cost b_wifi b_food asc_bus -0.0550 -0.0107 0.0287 0.0185 asc_air -0.2277 -0.1156 -0.0118 0.0611 asc_rail 0.0563 0.0788 -0.0602 0.0120 b_tt_car 0.2571 0.2191 0.2114 0.1368 b_tt_bus 0.0151 0.3143 -0.0504 0.0491 b_tt_air -0.0561 -0.0062 -0.1047 -0.1177 b_tt_rail 0.0943 0.0666 0.0621 0.0227 b_access 1.0000 -0.0538 0.2123 0.0648 b_cost -0.0538 1.0000 -0.0972 -0.0713 b_wifi 0.2123 -0.0972 1.0000 0.2412 b_food 0.0648 -0.0713 0.2412 1.0000 Upper level model results for mean parameters for underlying Normals Mean SD asc_bus -1.5723 0.3887 asc_air -0.7459 0.1557 asc_rail -1.6924 0.1337 b_tt_car -4.5442 0.0442 b_tt_bus -4.2010 0.0750 b_tt_air -4.5698 0.1073 b_tt_rail -5.3654 0.1570 b_access -4.1355 0.1556 b_cost -2.6074 0.0296 b_wifi 1.0136 0.0578 b_food 0.3648 0.0755 Upper level model results for covariance matrix for underlying Normals (means across iterations) asc_bus asc_air asc_rail b_tt_car b_tt_bus b_tt_air b_tt_rail asc_bus 0.2110 -0.0074 -0.0148 0.0055 0.0386 0.0002 0.0035 asc_air -0.0074 0.1888 0.0375 -0.0114 -0.0092 0.0455 -0.0254 asc_rail -0.0148 0.0375 0.1616 -0.0318 -0.0277 -0.0347 0.0308 b_tt_car 0.0055 -0.0114 -0.0318 0.0534 0.0165 0.0073 0.0200 b_tt_bus 0.0386 -0.0092 -0.0277 0.0165 0.0663 0.0070 0.0139 b_tt_air 0.0002 0.0455 -0.0347 0.0073 0.0070 0.1682 0.0055 b_tt_rail 0.0035 -0.0254 0.0308 0.0200 0.0139 0.0055 0.1557 b_access 0.0087 0.0498 -0.0119 0.0297 0.0011 -0.0108 0.0162 b_cost 0.0013 0.0098 -0.0067 0.0113 0.0173 -0.0014 0.0052 b_wifi 0.0040 -0.0016 -0.0088 -0.0221 0.0042 0.0177 -0.0104 b_food 0.0032 0.0115 0.0026 -0.0144 -0.0080 0.0241 -0.0057 b_access b_cost b_wifi b_food asc_bus 0.0087 0.0013 0.0040 0.0032 asc_air 0.0498 0.0098 -0.0016 0.0115 asc_rail -0.0119 -0.0067 -0.0088 0.0026 b_tt_car 0.0297 0.0113 -0.0221 -0.0144 b_tt_bus 0.0011 0.0173 0.0042 -0.0080 b_tt_air -0.0108 -0.0014 0.0177 0.0241 b_tt_rail 0.0162 0.0052 -0.0104 -0.0057 b_access 0.2223 -0.0055 -0.0507 -0.0169 b_cost -0.0055 0.0449 0.0091 0.0070 b_wifi -0.0507 0.0091 0.1929 0.0487 b_food -0.0169 0.0070 0.0487 0.1960 Upper level model results for covariance matrix for underlying Normals (SD across iterations) asc_bus asc_air asc_rail b_tt_car b_tt_bus b_tt_air b_tt_rail asc_bus 0.0984 0.0490 0.0656 0.0247 0.0311 0.0557 0.0519 asc_air 0.0490 0.0823 0.0423 0.0201 0.0200 0.0480 0.0480 asc_rail 0.0656 0.0423 0.0586 0.0216 0.0264 0.0463 0.0382 b_tt_car 0.0247 0.0201 0.0216 0.0113 0.0105 0.0225 0.0191 b_tt_bus 0.0311 0.0200 0.0264 0.0105 0.0163 0.0228 0.0216 b_tt_air 0.0557 0.0480 0.0463 0.0225 0.0228 0.0574 0.0488 b_tt_rail 0.0519 0.0480 0.0382 0.0191 0.0216 0.0488 0.0595 b_access 0.0706 0.0686 0.0604 0.0231 0.0294 0.0627 0.0623 b_cost 0.0177 0.0159 0.0138 0.0065 0.0085 0.0145 0.0129 b_wifi 0.0622 0.0663 0.0461 0.0214 0.0236 0.0548 0.0422 b_food 0.0534 0.0576 0.0596 0.0227 0.0226 0.0540 0.0426 b_access b_cost b_wifi b_food asc_bus 0.0706 0.0177 0.0622 0.0534 asc_air 0.0686 0.0159 0.0663 0.0576 asc_rail 0.0604 0.0138 0.0461 0.0596 b_tt_car 0.0231 0.0065 0.0214 0.0227 b_tt_bus 0.0294 0.0085 0.0236 0.0226 b_tt_air 0.0627 0.0145 0.0548 0.0540 b_tt_rail 0.0623 0.0129 0.0422 0.0426 b_access 0.0754 0.0183 0.0601 0.0668 b_cost 0.0183 0.0076 0.0141 0.0139 b_wifi 0.0601 0.0141 0.0650 0.0574 b_food 0.0668 0.0139 0.0574 0.0811 Chain convergence report (Geweke test) Fixed (non random) parameters (t-test value for Geweke test) asc_bus_interaction_female asc_air_interaction_female 0.9630 1.2684 asc_rail_interaction_female b_tt_interaction_business 2.0720 -3.8308 b_cost_interaction_business cost_income_elast -3.0014 0.3489 Random parameters (t-test value for Geweke test) asc_bus asc_air asc_rail b_tt_car b_tt_bus b_tt_air b_tt_rail -2.4015 2.7797 0.9197 -2.1263 -8.8693 1.2097 -1.0074 b_access b_cost b_wifi b_food 3.8576 -2.0305 -1.8882 -3.3022 Covariances of random parameters (t-test value for Geweke test) asc_bus_asc_bus asc_air_asc_bus asc_air_asc_air 1.5026 0.0170 -1.4620 asc_rail_asc_bus asc_rail_asc_air asc_rail_asc_rail 2.0477 -0.9014 0.3141 b_tt_car_asc_bus b_tt_car_asc_air b_tt_car_asc_rail -2.5135 3.1771 2.2397 b_tt_car_b_tt_car b_tt_bus_asc_bus b_tt_bus_asc_air -0.4334 -0.4811 -0.3758 b_tt_bus_asc_rail b_tt_bus_b_tt_car b_tt_bus_b_tt_bus 1.3676 -2.3721 1.6355 b_tt_air_asc_bus b_tt_air_asc_air b_tt_air_asc_rail -1.2809 -2.5485 -0.8760 b_tt_air_b_tt_car b_tt_air_b_tt_bus b_tt_air_b_tt_air -2.0153 -0.5797 -1.4558 b_tt_rail_asc_bus b_tt_rail_asc_air b_tt_rail_asc_rail -0.8790 0.9794 2.1871 b_tt_rail_b_tt_car b_tt_rail_b_tt_bus b_tt_rail_b_tt_air -0.7188 -2.0294 -1.8841 b_tt_rail_b_tt_rail b_access_asc_bus b_access_asc_air -0.3767 -0.4898 1.1143 b_access_asc_rail b_access_b_tt_car b_access_b_tt_bus 0.7119 -0.0194 -1.4948 b_access_b_tt_air b_access_b_tt_rail b_access_b_access -0.9609 -0.1329 -1.8065 b_cost_asc_bus b_cost_asc_air b_cost_asc_rail -1.0507 -1.6939 -0.5798 b_cost_b_tt_car b_cost_b_tt_bus b_cost_b_tt_air 0.5513 0.9732 0.5619 b_cost_b_tt_rail b_cost_b_access b_cost_b_cost 0.1511 -1.8809 1.0401 b_wifi_asc_bus b_wifi_asc_air b_wifi_asc_rail 0.8269 -1.1513 -2.7089 b_wifi_b_tt_car b_wifi_b_tt_bus b_wifi_b_tt_air -1.0903 1.8416 1.5381 b_wifi_b_tt_rail b_wifi_b_access b_wifi_b_cost -1.5448 -2.2964 0.5714 b_wifi_b_wifi b_food_asc_bus b_food_asc_air 2.1419 1.1912 -2.0962 b_food_asc_rail b_food_b_tt_car b_food_b_tt_bus -2.1576 -1.5258 2.3512 b_food_b_tt_air b_food_b_tt_rail b_food_b_access 1.0662 -3.1253 -3.0585 b_food_b_cost b_food_b_wifi b_food_b_food 0.1407 1.9620 1.4467 Iteration details (overview) ---------------------------- Iteration Log-Likelihood RLH Parameter RMS Avg. Variance 1 -236457.772 0.03495361 0.8221838 0.4058661 500 -4957.086 0.52893240 4.3342664 18.7756846 1000 -4624.284 0.54175909 4.1267716 17.0263034 1500 -4607.364 0.54093437 1.7598279 3.1481384 2000 -4623.619 0.53987102 2.0921181 4.4515835 2500 -4602.722 0.54133508 2.2413332 5.1052538 3000 -4552.454 0.54434380 2.2819562 5.2935101 3500 -4558.069 0.54406078 1.2865881 1.7369897 4000 -4563.942 0.54263007 1.4883369 2.3095732 4500 -4553.731 0.54412973 2.9655362 8.8901676 5000 -4527.899 0.54578747 1.4397633 2.1521022 5500 -4579.564 0.54165205 1.1246666 1.3310179 6000 -4589.718 0.54083671 0.9673652 0.9839175 6500 -4573.568 0.54195640 0.9568422 0.9619556 7000 -4563.529 0.54174090 0.9669249 0.9764990 7500 -4562.655 0.54261764 0.7194346 0.5486673 8000 -4634.851 0.53525939 0.7911478 0.6714484 8500 -4667.290 0.53421341 0.8016117 0.6880456 9000 -4652.745 0.53502812 0.8259198 0.7290292 9500 -4614.428 0.53760278 0.7916849 0.6698494 10000 -4701.966 0.53139184 0.7836176 0.6622783 10500 -4618.869 0.53716140 0.7483884 0.6047183 11000 -4640.894 0.53524971 0.7519931 0.6089416 11500 -4675.530 0.53331822 0.7493946 0.6044901 12000 -4603.119 0.53881654 0.7407409 0.5865656 12500 -4625.767 0.53683554 0.7252717 0.5649539 13000 -4669.808 0.53404060 0.7066136 0.5373046 13500 -4650.819 0.53528664 0.7567939 0.6237820 14000 -4648.039 0.53542390 0.8002454 0.6851852 14500 -4612.806 0.53757206 0.7917862 0.6757082 15000 -4659.061 0.53530219 0.8086899 0.7037797 15500 -4609.524 0.53765263 0.7867861 0.6681675 16000 -4633.026 0.53657484 0.8011169 0.6969442 16500 -4626.763 0.53646607 0.7925845 0.6761264 17000 -4628.056 0.53570924 0.7367862 0.5861604 17500 -4636.232 0.53578117 0.7772283 0.6498754 18000 -4610.879 0.53735495 0.8055602 0.6972062 18500 -4628.784 0.53672553 0.8066410 0.6952877 19000 -4633.234 0.53601962 0.8047318 0.6778351 19500 -4665.707 0.53374216 0.8388945 0.7336833 20000 -4623.249 0.53709017 0.8836708 0.8069125 20500 -4671.713 0.53395102 0.8639073 0.7735632 21000 -4624.373 0.53673717 0.7633025 0.6102226 21500 -4643.418 0.53543084 0.8143552 0.6767283 22000 -4648.238 0.53484606 0.8340747 0.7267444 22500 -4650.144 0.53422926 0.8185332 0.6843473 23000 -4636.453 0.53568773 0.8072953 0.6751201 23500 -4657.397 0.53459293 0.7951985 0.6621045 24000 -4643.616 0.53476396 0.7548899 0.5927728 24500 -4635.228 0.53621695 0.7914941 0.6473795 25000 -4641.404 0.53544190 0.7831961 0.6348063 25500 -4606.147 0.53761735 0.7683978 0.6132784 26000 -4596.324 0.53848147 0.7766539 0.6280933 26500 -4612.420 0.53688689 0.7506545 0.5846821 27000 -4632.313 0.53594389 0.7143203 0.5448138 27500 -4659.899 0.53358306 0.6943952 0.5069367 28000 -4626.671 0.53715220 0.7398585 0.5805504 28500 -4603.655 0.53844426 0.7657165 0.6054337 29000 -4640.332 0.53533172 0.7620839 0.5952680 29500 -4627.348 0.53636669 0.7325808 0.5446387 30000 -4662.845 0.53416042 0.7140558 0.5259947 30500 -4604.633 0.53815665 0.7553632 0.5819795 31000 -4628.508 0.53647658 0.7825358 0.6261142 31500 -4594.089 0.53918097 0.8389450 0.7039763 32000 -4639.679 0.53541064 0.8555733 0.7107401 32500 -4585.637 0.53970966 0.9200022 0.8414010 33000 -4619.548 0.53707877 0.9547281 0.8810948 33500 -4560.691 0.54134765 0.9916025 0.9710726 34000 -4605.020 0.53805755 1.0255399 1.0444784 34500 -4641.502 0.53519307 0.9422572 0.8635417 35000 -4636.015 0.53595120 0.9144887 0.8282225 35500 -4639.995 0.53567654 0.8344533 0.7077223 36000 -4632.384 0.53595588 0.8920013 0.8013843 36500 -4614.686 0.53659477 0.8533787 0.7378406 37000 -4612.766 0.53735413 0.9030492 0.8216203 37500 -4620.018 0.53735981 0.8649379 0.7537785 38000 -4645.883 0.53592064 0.8804801 0.7810781 38500 -4684.875 0.53211229 0.9215444 0.8452575 39000 -4659.513 0.53442217 0.8746733 0.7636105 39500 -4644.189 0.53483730 0.8502959 0.7194271 40000 -4587.638 0.53963588 0.8787011 0.7760372 40500 -4612.408 0.53732483 0.8902400 0.7901012 41000 -4617.868 0.53651645 0.8921433 0.7901344 41500 -4635.566 0.53607353 0.9048183 0.8144219 42000 -4651.171 0.53504036 0.9088425 0.8279802 42500 -4632.515 0.53488318 0.9298945 0.8571669 43000 -4654.747 0.53437955 0.9414350 0.8717215 43500 -4629.851 0.53688378 0.9438856 0.8912198 44000 -4662.318 0.53388607 0.9321930 0.8658470 44500 -4616.158 0.53714744 0.8947033 0.8097793 45000 -4644.044 0.53506888 0.9239994 0.8620482 45500 -4633.560 0.53598987 0.8901903 0.7908449 46000 -4637.906 0.53583854 0.8615722 0.7439112 46500 -4618.555 0.53721035 0.8249281 0.6738728 47000 -4613.732 0.53693827 0.8035553 0.6499868 47500 -4611.777 0.53793195 0.8222557 0.6743892 48000 -4594.988 0.53890297 0.7583523 0.5812832 48500 -4614.482 0.53710577 0.7852307 0.6219638 49000 -4633.790 0.53599478 0.7859040 0.6268488 49500 -4576.785 0.54033649 0.8443157 0.7283718 50000 -4626.051 0.53633232 0.8350980 0.7038025 50500 -4625.314 0.53736475 0.8404150 0.7197767 51000 -4653.844 0.53453203 0.8450268 0.7007794 51500 -4658.871 0.53417326 0.9064650 0.8143190 52000 -4665.929 0.53414688 0.9670475 0.9207452 52500 -4604.980 0.53792488 0.9533176 0.8983339 53000 -4586.346 0.53971955 0.9642125 0.9125105 53500 -4668.375 0.53281752 0.9146556 0.8051158 54000 -4623.166 0.53668723 0.9270191 0.8459705 54500 -4650.807 0.53530741 0.9722466 0.9256754 55000 -4646.094 0.53600015 0.9407359 0.8594192 55500 -4631.881 0.53651956 0.9361472 0.8714893 56000 -4628.232 0.53583592 0.9209793 0.8426694 56500 -4621.654 0.53690723 0.9414562 0.8882012 57000 -4623.844 0.53712951 0.8931438 0.7898801 57500 -4626.525 0.53677178 0.8755259 0.7639297 58000 -4630.804 0.53605839 0.8698240 0.7446551 58500 -4613.218 0.53803080 0.8713593 0.7543132 59000 -4616.185 0.53711127 0.8230863 0.6729788 59500 -4632.930 0.53640773 0.8915804 0.7774144 60000 -4597.337 0.53824444 0.8073225 0.6638008 60500 -4655.455 0.53451217 0.7901993 0.6365727 61000 -4633.106 0.53587837 0.8041843 0.6472409 61500 -4610.598 0.53703312 0.8012289 0.6469558 62000 -4606.829 0.53816953 0.8313746 0.6756684 62500 -4593.694 0.53913376 0.9143394 0.8445280 63000 -4633.621 0.53546319 0.9403799 0.8745809 63500 -4613.570 0.53677209 0.8921721 0.7833129 64000 -4679.373 0.53185567 0.8856421 0.7658833 64500 -4588.306 0.53856199 0.8777413 0.7639885 65000 -4629.252 0.53633922 0.8701160 0.7615281 65500 -4629.411 0.53621787 0.8607449 0.7364580 66000 -4660.102 0.53383693 0.8553436 0.7289735 66500 -4626.548 0.53585974 0.7821685 0.6159055 67000 -4601.227 0.53806992 0.7469973 0.5645328 67500 -4572.266 0.54043712 0.7131825 0.5233996 68000 -4634.040 0.53573171 0.7154081 0.5182908 68500 -4627.179 0.53638646 0.7515348 0.5782680 69000 -4580.541 0.53945112 0.7419348 0.5555922 69500 -4621.298 0.53731290 0.7422512 0.5620041 70000 -4637.294 0.53566785 0.7902717 0.6300758 Acceptance Rate (Fixed) Acceptance Rate (Normal) 0.00 0.492 0.04 0.266 0.02 0.272 0.04 0.282 0.03 0.278 0.03 0.310 0.03 0.320 0.01 0.268 0.05 0.294 0.06 0.288 0.06 0.338 0.02 0.264 0.05 0.320 0.07 0.270 0.03 0.246 0.05 0.344 0.09 0.274 0.05 0.292 0.05 0.296 0.06 0.346 0.09 0.294 0.05 0.276 0.07 0.282 0.07 0.302 0.07 0.268 0.09 0.280 0.12 0.286 0.10 0.336 0.16 0.326 0.09 0.332 0.09 0.328 0.15 0.320 0.24 0.292 0.16 0.286 0.15 0.302 0.20 0.284 0.21 0.264 0.19 0.336 0.22 0.310 0.25 0.314 0.13 0.264 0.25 0.308 0.22 0.274 0.26 0.286 0.32 0.318 0.27 0.288 0.31 0.264 0.24 0.348 0.35 0.312 0.27 0.210 0.33 0.324 0.40 0.270 0.38 0.298 0.25 0.288 0.38 0.304 0.27 0.272 0.27 0.304 0.33 0.306 0.27 0.278 0.27 0.292 0.31 0.308 0.44 0.262 0.18 0.314 0.33 0.272 0.31 0.304 0.22 0.278 0.32 0.286 0.34 0.256 0.25 0.272 0.40 0.320 0.27 0.346 0.24 0.298 0.24 0.282 0.26 0.306 0.25 0.340 0.26 0.334 0.36 0.244 0.27 0.320 0.24 0.316 0.35 0.286 0.28 0.300 0.24 0.274 0.34 0.314 0.32 0.294 0.31 0.276 0.31 0.318 0.30 0.248 0.24 0.294 0.24 0.302 0.24 0.298 0.32 0.288 0.24 0.340 0.29 0.274 0.27 0.314 0.23 0.320 0.45 0.302 0.31 0.330 0.33 0.326 0.23 0.276 0.28 0.342 0.30 0.348 0.25 0.348 1.06 0.272 0.46 0.306 2.23 0.314 0.43 0.338 0.27 0.338 0.30 0.280 2.09 0.266 0.83 0.312 0.64 0.342 0.59 0.368 0.59 0.290 0.59 0.290 3.82 0.284 0.53 0.324 0.53 0.292 2.20 0.278 0.51 0.316 1.48 0.266 0.22 0.340 1.08 0.296 1.08 0.296 3.11 0.312 0.40 0.274 0.40 0.340 1.34 0.298 1.34 0.278 0.44 0.280 0.25 0.238 0.25 0.288 0.47 0.318 0.47 0.358 0.47 0.296 3.44 0.286 0.90 0.288 0.36 0.252 1.05 0.292 1.01 0.302 1.01 0.276 1.01 0.274 Settings and functions used in model definition: apollo_control -------------- Value modelDescr "HB model on mode choice SP data, mix of random and non-random parameters" indivID "ID" HB "TRUE" outputDirectory "output/" debug "FALSE" modelName "HB_MMNL" nCores "1" workInLogs "FALSE" seed "13" mixing "FALSE" noValidation "FALSE" noDiagnostics "FALSE" calculateLLC "TRUE" analyticHessian "FALSE" memorySaver "FALSE" panelData "TRUE" analyticGrad "FALSE" analyticGrad_manualSet "FALSE" overridePanel "FALSE" preventOverridePanel "FALSE" noModification "FALSE" apollo_HB --------- $hbDist asc_car asc_bus "NR" "N" asc_air asc_rail "N" "N" asc_bus_interaction_female asc_air_interaction_female "NR" "NR" asc_rail_interaction_female b_tt_car "NR" "LN-" b_tt_bus b_tt_air "LN-" "LN-" b_tt_rail b_tt_interaction_business "LN-" "NR" b_access b_cost "LN-" "LN-" b_cost_interaction_business cost_income_elast "NR" "NR" b_no_frills b_wifi "NR" "CN+" b_food "CN+" $gNCREP [1] 50000 $gNEREP [1] 20000 $gINFOSKIP [1] 500 $nodiagnostics [1] TRUE $modelname [1] "HB_MMNL" $gVarNamesFixed [1] "asc_bus_interaction_female" "asc_air_interaction_female" [3] "asc_rail_interaction_female" "b_tt_interaction_business" [5] "b_cost_interaction_business" "cost_income_elast" $gVarNamesNormal [1] "asc_bus" "asc_air" "asc_rail" "b_tt_car" "b_tt_bus" "b_tt_air" [7] "b_tt_rail" "b_access" "b_cost" "b_wifi" "b_food" $gDIST asc_bus asc_air asc_rail b_tt_car b_tt_bus b_tt_air b_tt_rail 1 1 1 3 3 3 3 b_access b_cost b_wifi b_food 3 3 4 4 $svN asc_bus asc_air asc_rail b_tt_car b_tt_bus b_tt_air b_tt_rail 0 0 0 0 0 0 0 b_access b_cost b_wifi b_food 0 0 0 0 $FC asc_bus_interaction_female asc_air_interaction_female 0 0 asc_rail_interaction_female b_tt_interaction_business 0 0 b_cost_interaction_business cost_income_elast 0 0 $gFULLCV [1] TRUE Non-random parameters: ----------------------nasc_bus_interaction_female asc_air_interaction_female asc_rail_interaction_female b_tt_interaction_business b_cost_interaction_business cost_income_elast Random parameters (Distribution): ---------------------------------nasc_bus ( ) asc_air ( ) asc_rail ( ) b_tt_car ( ) b_tt_bus ( ) b_tt_air ( ) b_tt_rail ( ) b_access ( ) b_cost ( ) b_wifi ( ) b_food ( ) Prior Variance-Covariance Matrix: --------------------------------- asc_bus asc_air asc_rail b_tt_car b_tt_bus b_tt_air b_tt_rail asc_bus 2 0 0 0 0 0 0 asc_air 0 2 0 0 0 0 0 asc_rail 0 0 2 0 0 0 0 b_tt_car 0 0 0 2 0 0 0 b_tt_bus 0 0 0 0 2 0 0 b_tt_air 0 0 0 0 0 2 0 b_tt_rail 0 0 0 0 0 0 2 b_access 0 0 0 0 0 0 0 b_cost 0 0 0 0 0 0 0 b_wifi 0 0 0 0 0 0 0 b_food 0 0 0 0 0 0 0 b_access b_cost b_wifi b_food asc_bus 0 0 0 0 asc_air 0 0 0 0 asc_rail 0 0 0 0 b_tt_car 0 0 0 0 b_tt_bus 0 0 0 0 b_tt_air 0 0 0 0 b_tt_rail 0 0 0 0 b_access 2 0 0 0 b_cost 0 2 0 0 b_wifi 0 0 2 0 b_food 0 0 0 2 apollo_probabilities ---------------------- function(apollo_beta, apollo_inputs, functionality="estimate"){ ### Attach inputs and detach after function exit apollo_attach(apollo_beta, apollo_inputs) on.exit(apollo_detach(apollo_beta, apollo_inputs)) ### Create list of probabilities P P = list() ### Create alternative specific constants and coefficients using interactions with socio-demographics asc_bus_value = asc_bus + asc_bus_interaction_female * female asc_air_value = asc_air + asc_air_interaction_female * female asc_rail_value = asc_rail + asc_rail_interaction_female * female b_tt_car_value = b_tt_car + b_tt_interaction_business * business b_tt_bus_value = b_tt_bus + b_tt_interaction_business * business b_tt_air_value = b_tt_air + b_tt_interaction_business * business b_tt_rail_value = b_tt_rail + b_tt_interaction_business * business b_cost_value = ( b_cost + b_cost_interaction_business * business ) * ( income / mean_income ) ^ cost_income_elast ### List of utilities: these must use the same names as in mnl_settings, order is irrelevant V = list() V=list() V[["car"]] = asc_car + b_tt_car_value * time_car + b_cost_value * cost_car V[["bus"]] = asc_bus_value + b_tt_bus_value * time_bus + b_access * access_bus + b_cost_value * cost_bus V[["air"]] = asc_air_value + b_tt_air_value * time_air + b_access * access_air + b_cost_value * cost_air + b_no_frills * ( service_air == 1 ) + b_wifi * ( service_air == 2 ) + b_food * ( service_air == 3 ) V[["rail"]] = asc_rail_value + b_tt_rail_value * time_rail + b_access * access_rail + b_cost_value * cost_rail + b_no_frills * ( service_rail == 1 ) + b_wifi * ( service_rail == 2 ) + b_food * ( service_rail == 3 ) ### Define settings for MNL model component mnl_settings = list( alternatives = c(car=1, bus=2, air=3, rail=4), avail = list(car=av_car, bus=av_bus, air=av_air, rail=av_rail), choiceVar = choice, utilities = V ) ### Compute probabilities using MNL model P[["model"]] = apollo_mnl(mnl_settings, functionality) ### Prepare and return outputs of function P = apollo_prepareProb(P, apollo_inputs, functionality) return(P) }