Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models

Travel Behaviour and Society - Tập 20 - Trang 22-35 - 2020
Xilei Zhao1, Xiang Yan2, Alan Yu3, Pascal Van Hentenryck4
1Department of Civil and Coastal Engineering, University of Florida, USA
2Department of Urban and Regional Planning, University of Florida, USA
3Department of Electrical Engineering and Computer Science, University of Michigan, USA
4H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, USA

Tài liệu tham khảo

Abrantes, 2011, Meta-analysis of UK values of travel time: an update, Transp. Res. Part A: Policy Practice, 45, 1 Athey, 2017, Beyond prediction: using big data for policy problems, Science, 355, 483, 10.1126/science.aal4321 Ben-Akiva, 1985, vol. 9 Breiman, 1996, Bagging predictors, Mach. Learn., 24, 123, 10.1007/BF00058655 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Breiman, 2017 Brownstone, 1998, Forecasting new product penetration with flexible substitution patterns, J. Econometrics, 89, 109, 10.1016/S0304-4076(98)00057-8 Cawley, 2010, On over-fitting in model selection and subsequent selection bias in performance evaluation, J. Mach. Learn. Res., 11, 2079 Chen, 2017, Understanding ridesplitting behavior of on-demand ride services: an ensemble learning approach, Transp. Res. Part C: Emerg. Technol., 76, 51, 10.1016/j.trc.2016.12.018 Cheng, 2019, Applying a random forest method approach to model travel mode choice behavior, Travel Behav. Soc., 14, 1, 10.1016/j.tbs.2018.09.002 Cherchi, 2010, Validation and forecasts in models estimated from multiday travel survey, Transp. Res. Rec.: J. Transp. Res. Board, 2175, 57, 10.3141/2175-07 Christopher, 2016 Doshi-Velez, F., Kim, B., 2017. Towards a rigorous science of interpretable machine learning. arXiv:1702.08608. Farrar, 1967, Multicollinearity in regression analysis: the problem revisited, Rev. Econ. Stat., 92, 10.2307/1937887 Friedman, 2001, Greedy function approximation: a gradient boosting machine, Ann. Stat., 1189 Garcia-Martinez, 2018, Transfer penalties in multimodal public transport networks, Transp. Res. Part A: Policy Practice, 114, 52 Gevrey, 2003, Review and comparison of methods to study the contribution of variables in artificial neural network models, Ecol. Model., 160, 249, 10.1016/S0304-3800(02)00257-0 Goldstein, 2015, Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation, J. Comput. Graphical Stat., 24, 44, 10.1080/10618600.2014.907095 Golshani, 2018, Modeling travel mode and timing decisions: comparison of artificial neural networks and copula-based joint model, Travel Behav. Soc., 10, 21, 10.1016/j.tbs.2017.09.003 Hagenauer, 2017, A comparative study of machine learning classifiers for modeling travel mode choice, Expert Syst. Appl., 78, 273, 10.1016/j.eswa.2017.01.057 Hastie, 2001, vol. 1 Hensher, 2003, The mixed logit model: the state of practice, Transportation, 30, 133, 10.1023/A:1022558715350 Hensher, 2005 Ho, 1998, The random subspace method for constructing decision forests, IEEE Trans. Pattern Anal. Mach. Intell., 20, 832, 10.1109/34.709601 Hsu, 2002, A comparison of methods for multiclass support vector machines, IEEE Trans. Neural Netw., 13, 415, 10.1109/72.991427 Iseki, 2009, Not all transfers are created equal: towards a framework relating transfer connectivity to travel behaviour, Transp. Rev., 29, 777, 10.1080/01441640902811304 Jenkins, K., 2018. New app reinvents University bus system to be more like Uber. The Michigan Daily.https://www.michigandaily.com/section/research/new-app-helps-turns-university-bus-system-demand-service. Karlaftis, 2011, Statistical methods versus neural networks in transportation research: differences, similarities and some insights, Transp. Res. Part C: Emerg. Technol., 19, 387, 10.1016/j.trc.2010.10.004 Klaiber, 2011, Do random coefficients and alternative specific constants improve policy analysis? An empirical investigation of model fit and prediction, Environ. Resource Econ., 1 Kuhn, 2008, Building predictive models in r using the caret package, J. Stat. Software, 28, 1, 10.18637/jss.v028.i05 Last, 2002, Improving stability of decision trees, Int. J. Pattern Recogn. Artif. Intell., 16, 145, 10.1142/S0218001402001599 Lhéritier, 2018, Airline itinerary choice modeling using machine learning, J. Choice Model. Liaw, 2002, Classification and regression by randomForest, R News, 2, 18 Lindner, 2017, Estimating motorized travel mode choice using classifiers: An application for high-dimensional multicollinear data, Travel Behav. Soc., 6, 100, 10.1016/j.tbs.2016.08.003 Mahéo, 2017, Benders decomposition for the design of a hub and shuttle public transit system, Transp. Sci. McCallum, A., Nigam, K., et al., 1998. A comparison of event models for naive bayes text classification. In: AAAI-98 Workshop on Learning for Text Categorization. vol. 752. Citeseer. pp. 41–48. McFadden, 1973, Conditional logit analysis of qualitative choice behaviour, 105 McFadden, 2000, Mixed MNL models for discrete response, J. Appl. Econometrics, 15, 447, 10.1002/1099-1255(200009/10)15:5<447::AID-JAE570>3.0.CO;2-1 Menard, 2004, Six approaches to calculating standardized logistic regression coefficients, Am. Stat., 58, 218, 10.1198/000313004X946 Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., 2017. e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.6-8. URL:https://CRAN.R-project.org/package=e1071. Molnar, C., 2018. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Mullainathan, 2017, Machine learning: an applied econometric approach, J. Econ. Perspectives, 31, 87, 10.1257/jep.31.2.87 Murdoch, W.J., Singh, C., Kumbier, K., Abbasi-Asl, R., Yu, B., 2019. Interpretable machine learning: definitions, methods, and applications. arXiv:1901.04592. Omrani, 2015, Predicting travel mode of individuals by machine learning, Transp. Res. Proc., 10, 840, 10.1016/j.trpro.2015.09.037 Omrani, 2013, Prediction of individual travel mode with evidential neural network model, Transp. Res. Rec.: J. Transp. Res. Board, 2399, 1, 10.3141/2399-01 Quinlan, 2014 Rasouli, 2014, Using ensembles of decision trees to predict transport mode choice decisions: effects on predictive success and uncertainty estimates, Eur. J. Transp. Infrastruct. Res., 14 Ridgeway, G., 2017. gbm: Generalized Boosted Regression Models. R package version 2.1.3. URL:https://CRAN.R-project.org/package=gbm. Ripley, B., 2016. tree: Classification and Regression Trees. R package version 1.0-37. URL:https://CRAN.R-project.org/package=tree. Train, 2009 Venables, W.N., Ripley, B.D., 2002. Modern Applied Statistics with S. fourth ed. Springer, New York. iSBN 0-387-95457-0. URL:http://www.stats.ox.ac.uk/pub/MASS4. Wang, 2018, Machine learning travel mode choices: comparing the performance of an extreme gradient boosting model with a multinomial logit model, Transp. Res. Rec.: J. Transp. Res. Board Wong, 2018, Discriminative conditional restricted boltzmann machine for discrete choice and latent variable modelling, J. Choice Model., 29, 152, 10.1016/j.jocm.2017.11.003 Wu, 2004, Probability estimates for multi-class classification by pairwise coupling, J. Mach. Learn. Res., 5, 975 Xie, 2003, Work travel mode choice modeling with data mining: decision trees and neural networks, Transp. Res. Rec.: J. Transp. Res. Board, 1854, 50, 10.3141/1854-06 Yan, 2019, Integrating ridesourcing services with public transit: An evaluation of traveler responses combining revealed and stated preference data, Transp. Res. Part C: Emerg. Technol., 105, 683, 10.1016/j.trc.2018.07.029 Zhang, 2008, Travel mode choice modeling with support vector machines, Transp. Res. Rec.: J. Transp. Res. Board, 2076, 141, 10.3141/2076-16 Zhao, 2019, Causal interpretations of black-box models, J. Business Econ. Stat., 1