On the use of machine learning techniques and discrete choice models in mode choice analysis

• Autorzy: Benjdiya Ouissam , Rouky Naoufal , Benmoussa Othmane , Fri Mouhsene

Identyfikatory

DOI

10.17270/J.LOG.2023.845

• Języki publikacji: EN

Abstrakty

EN

Background: The mode choice stage is a critical aspect that transportation experts rely on to develop a robust transportation system for a particular region. Various techniques are utilized to model mode choice behavior, including Discrete Choice Models (DCMs) and Machine Learning (ML) techniques. However, existing reviews typically focus on either DCMs or ML techniques, and reviews that cover both categories often concentrate on one category while merely mentioning some techniques from the other. This paper aims to address this gap by examining the principal DCMs and ML techniques published over the past four years, differentiating between models based on the granularity level, namely aggregate and disaggregate models. Additionally, a comprehensive discussion is conducted on the accuracy of the different models used in the reviewed articles. Methods: This paper provides a thorough and enhanced analysis of travel mode choice models and analysis techniques used in articles published on "ScienceDirect" from 2020 to 2023. To ensure a comprehensive coverage of the subject, a meticulous search strategy was employed, utilizing targeted keywords. As a result, a total of 38 articles were carefully selected for detailed examination and analysis. Results: The findings of this study highlight the suitability of different modeling approaches for varying levels of analysis. Discrete Choice Models demonstrate effectiveness in aggregate-level analyses, whereas Machine Learning Techniques prove more appropriate for disaggregate-level analyses. Moreover, the study suggests that employing hybrid models can potentially yield a promising solution to attain enhanced prediction accuracy without compromising interpretability. Conclusions: The examination of selected articles revealed several key points. Firstly, there is a concentration of studies on travel mode choice in European countries, China, and the USA, indicating a need for more research in developing countries. Secondly, the reviewed articles often lack in-depth analysis of individual behavior and fail to consider external factors like weather or seasons when employing disaggregate models. Thus, future studies should leverage technological advancements and explore new factors influencing mode choice behavior. Additionally, there is a need for further research on hybrid models that combine Discrete Choice Models (DCMs) with Machine Learning (ML) techniques or deep learning approaches. This research can provide guidance for practitioners unfamiliar with these methods and aid in the design of effective transportation policies. Lastly, considering the variety of models available, it is crucial to understand the extent to which these models can be generalized to different contexts, emphasizing the importance of studying model applicability and generalizability in diverse settings.

Słowa kluczowe

EN

travel mode choice; machine learning; discrete-choice model; hybrid models

PL

wybór trybu podróży; uczenie maszynowe; model wyboru dyskretnego; modele hybrydowe

• Wydawca: Wyższa Szkoła Logistyki
• Czasopismo: LogForum
• Rocznik: 2023
• Tom: Vol. 19, no. 3
• Strony: 331--345
• Opis fizyczny: Bibliogr. 52. poz., rys., tab., wykr.

Twórcy

autor

Benjdiya Ouissam

• Euromed Polytechnic School, Euromed Research Center, Euromed University of Fes, Fes, Morocco

autor

Rouky Naoufal

• Euromed Polytechnic School, Euromed Research Center, Euromed University of Fes, Fes, Morocco

autor

Benmoussa Othmane

• Euromed Polytechnic School, Euromed Research Center, Euromed University of Fes, Fes, Morocco

autor

Fri Mouhsene

• Euromed Polytechnic School, Euromed Research Center, Euromed University of Fes, Fes, Morocco

Bibliografia

• 1. Ali N. F. M., Sadullah A. F. M., Majeed A. P. A., Razman M. A. M., Musa R. M., 2022, The identification of significant features towards travel mode choice and its prediction via optimised random forest classifier: An evaluation for active commuting behavior, Journal of Transport & Health, 25, 101362. https://doi.org/10.1016/j.jth.2022.101362
• 2. Andani I.G.A., La Paix Puello L., Geurs K., 2021, Modelling effects of changes in travel time and costs of toll road usage on choices for residential location, route and travel mode across population segments in the Jakarta-Bandung region, Indonesia, Transportation Research Part A: Policy and Practice, 145, pp. 81–102. https://doi.org/10.1016/j.tra.2020.12.012
• 3. Barri E. Y., Farber S., Jahanshahi H., Beyazit E., 2022, Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms, Journal of Transport Geography, 105, 103482. https://doi.org/10.1016/j.jtrangeo.2022.103482
• 4. Barff R., Mackay D., Olshavsky R.W., 1982, A Selective Review of Travel-Mode Choice Models, Journal of Consumer Research, 8, 4, p. 370. https://doi.org/10.1086/208877
• 5. Ben-Akiva M.E., Lerman S.R., 1985, Discrete choice analysis: theory and application to travel demand, Cambridge, Mass: MIT Press (MIT Press series in transportation studies, 9).
• 6. Chang X. et al., 2020, Understanding user’s travel behavior and city region functions from station-free shared bike usage data, Transportation Research Part F: Traffic Psychology and Behaviour, 72, pp. 81–95. https://doi.org/10.1016/j.trf.2020.03.018
• 7. Cheng L. et al., 2019, Applying a random forest method approach to model travel mode choice behavior, Travel Behaviour and Society, 14, pp. 1–10. https://doi.org/10.1016/j.tbs.2018.09.002
• 8. De Waal, Alta et al., 2022, Explainable Bayesian networks applied to transport vulnerability, Expert Systems with Applications, vol. 209, p. 118348. https://doi.org/10.1016/j.eswa.2022.118348
• 9. Feng Y., Zhao J., Sun H., Wu J., Gao, Z., 2022, Choices of intercity multimodal passenger travel modes. Physica A: Statistical Mechanics and its Applications, 600, 127500. https://doi.org/10.1016/j.physa.2022.127500
• 10. Gao K. et al., 2021, Extrapolation-enhanced model for travel decision making: An ensemble machine learning approach considering behavioral theory, Knowledge- Based Systems, 218, p. 106882. https://doi.org/10.1016/j.knosys.2021.106882
• 11. Gupta A., Bivina G. R., Parida M., 2022, Does neighborhood design matter for walk access to metro stations? An integrated SEM-Hybrid discrete mode choice approach, Transport policy, 121, 61-77. https://doi.org/10.1016/j.tranpol.2022.03.010
• 12. Guo D., Yao E., Liu S., Chen R., Hong J., Zhang J., 2023, Exploring the role of passengers’ attitude in the integration of dockless bike-sharing and public transit: A hybrid choice modeling approach, Journal of Cleaner Production, 384, 135627. https://doi.org/10.1016/j.jclepro.2022.135627
• 13. Kashifi M. T., Jamal A., Kashefi M. S., Almoshaogeh M., Rahman S. M., 2022, Predicting the travel mode choice with interpretable machine learning techniques: A comparative study, Travel Behaviour and Society, 29, 279-296. https://doi.org/10.1016/j.tbs.2022.07.003
• 14. Kapitza J., 2022, How people get to work at night, A discrete choice model approach towards the influence of nighttime on the choice of transport mode for commuting to work, Journal of Transport Geography, 104, 103418. https://doi.org/10.1016/j.jtrangeo.2022.103418
• 15. Hamadneh J., Jaber A., 2023, Modeling of intra-city transport choice behaviour in Budapest, Hungary, Journal of Urban Mobility, 3, 100049. https://doi.org/10.1016/j.urbmob.2023.100049
• 16. Hagenauer J., Helbich M., 2017, A comparative study of machine learning classifiers for modeling travel mode choice, Expert Systems with Applications, 78, pp. 273– 282. https://doi.org/10.1016/j.eswa.2017.01.057
• 17. Harz J., Sommer C., 2022, Mode choice of city tourists: Discrete choice modeling based on survey data from a major German city, Transportation Research Interdisciplinary Perspectives, 16, 100704. https://doi.org/10.1016/j.trip.2022.100704
• 18. Hillel T. et al., 2021, A systematic review of machine learning classification methodologies for modelling passenger mode choice, Journal of Choice Modelling, 38, p. 100221. https://doi.org/10.1016/j.jocm.2020.100221
• 19. Ilahi A. et al., 2021, Understanding travel and mode choice with emerging modes; a pooled SP and RP model in Greater Jakarta, Indonesia, Transportation Research Part A: Policy and Practice, 150, pp. 398–422. https://doi.org/10.1016/j.tra.2021.06.023
• 20. Jing, P. et al., 2018, Travel Mode and Travel Route Choice Behavior Based on Random Regret Minimization: A Systematic Review, Sustainability, 10, 4, p. 1185. https://doi.org/10.3390/su10041185
• 21. Jochem P., Lisson C., Khanna A.A., 2021, The role of coordination costs in mode choice decisions: A case study of German cities, Transportation Research Part A: Policy and Practice, 149, pp. 31–44. https://doi.org/10.1016/j.tra.2021.04.001
• 22. Li L. et al., 2020, Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data, Transportation Research Part A: Policy and Practice, 136, pp. 282–292. https://doi.org/10.1016/j.tra.2020.04.005
• 23. Mo B. et al., 2021, Impacts of subjective evaluations and inertia from existing travel modes on adoption of autonomous mobility-on-demand, Transportation Research Part C: Emerging Technologies, 130, p. 103281. https://doi.org/10.1016/j.trc.2021.103281
• 24. McFadden D., Reid F., 1975, Aggregate travel demand forecasting from disaggregated behavioral models, Institute of Transportation and Traffic Engineering, University of California.
• 25. Mohammadian A., Miller E.J., 2002, Nested Logit Models and Artificial Neural Networks for Predicting Household Automobile Choices: Comparison of Performance, Transportation Research Record: Journal of the Transportation Research Board, 1807, 1, pp. 92– 100. https://doi.org/10.3141/1807-12
• 26. Nasrin S., Bunker J., 2021, Analyzing significant variables for choosing different modes by female travelers, Transport Policy, 114, pp. 312–329. https://doi.org/10.1016/j.tranpol.2021.10.017
• 27. Nguyen M.H., Armoogum J., 2020, Hierarchical process of travel mode imputation from GPS data in a motorcycle-dependent area, Travel Behaviour and Society, 21, pp. 109–120. https://doi.org/10.1016/j.tbs.2020.06.006
• 28. Okami S., Matsuyuki M., Sarmiento-Ordosgoitia I., Nakamura F., 2022, Residents’ travel behavior in a low-income settlement with aerial cable cars in Medellin, Colombia. Case studies on transport policy, 10, 2, 1332-1342. https://doi.org/10.1016/j.cstp.2022.04.016
• 29. Parmar J., Saiyed G., Dave S., 2023, Analysis of taste heterogeneity in commuters’ travel decisions using joint parking–and mode–choice model: A case from urban India, Transportation Research Part A: Policy and Practice, 170, 103610. https://doi.org/10.1016/j.tra.2023.103610
• 30. Salas P., De la Fuente R., Astroza S., Carrasco J. A., 2022, A systematic comparative evaluation of machine learning classifiers and discrete choice models for travel mode choice in the presence of response heterogeneity, Expert Systems with Applications, 193, 116253. https://doi.org/10.1016/j.eswa.2021.116253
• 31. Ratrout N. T., Gazder U., Al-Madani H. M., 2014, A review of mode choice modelling techniques for intra-city and border transport, World Review of Intermodal Transportation Research, 5, 1, 39-58. https://doi.org/10.1504/WRITR.2014.065055
• 32. Saiyad G., Srivastava M., Rathwa D., 2022, Exploring determinants of feeder mode choice behavior using Artificial Neural Network: Evidences from Delhi metro, Physica A: Statistical Mechanics and its Applications, 598, 127363. https://doi.org/10.1016/j.physa.2022.127363
• 33. Sanko N., 2020, Activity-end access/egress modal choices between stations and campuses located on a hillside, Research in Transportation Economics, 83, p. 100931. https://doi.org/10.1016/j.retrec.2020.100931
• 34. Sekhar Ch. R., 2014, Mode choice analysis: The data, the models and future ahead, International Journal for Traffic and Transport Engineering, 4, 3, pp. 269–285. http://dx.doi.org/10.7708/ijtte.2014.4(3).03
• 35. Song Y., Li D., Cao Q., Yang M., Ren G., 2021, The whole day path planning problem incorporating mode chains modeling in the era of mobility as a service, Transportation Research Part C: Emerging Technologies, 132, 103360. https://doi.org/10.1016/j.trc.2021.103360
• 36. Sun X., Wandelt S., 2021, Transportation mode choice behavior with recommender systems: A case study on Beijing, Transportation Research Interdisciplinary Perspectives, 11, p. 100408. https://doi.org/10.1016/j.trip.2021.100408
• 37. Tao T., Næss P., 2022, Exploring nonlinear built environment effects on driving with a mixed-methods approach, Transportation research part D: transport and environment, 111, 103443. https://doi.org/10.1016/j.trd.2022.103443
• 38. Ton D. et al., 2020, The experienced mode choice set and its determinants: Commuting trips in the Netherlands, Transportation Research Part A: Policy and Practice, 132, pp. 744–758. https://doi.org/10.1016/j.tra.2019.12.027
• 39. Tu M. et al., 2021, Exploring nonlinear effects of the built environment on ridesplitting: Evidence from Chengdu, Transportation Research Part D: Transport and Environment, 93, p. 102776. https://doi.org/10.1016/j.trd.2021.102776
• 40. Varghese V., Chikaraishi M., Jana A., 2022. Joint analysis of mode and travel-based multitasking choices in Mumbai, India, Travel Behaviour and Society, 27, 148-161. https://doi.org/10.1016/j.tbs.2022.01.006
• 41. Wu F., Lyu C., Liu Y., 2022, A personalized recommendation system for multi-modal transportation systems. Multimodal transportation, 1, 2, 100016. https://doi.org/10.1016/j.multra.2022.100016
• 42. Wang F., Ross C. L., 2018, Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model, Transportation Research Record: Journal of the Transportation Research Board, 2672, 47, pp. 35– 45. https://doi.org/10.1177/0361198118773556
• 43. Wang S., Mo B., Zhao J., 2020, Deep neural networks for choice analysis: Architecture design with alternative- specific utility functions, Transportation Research Part C: Emerging Technologies, 112, pp. 234–251. https://doi.org/10.1016/j.trc.2020.01.012
• 44. Wong M., Farooq B., 2021, ResLogit: A residual neural network logit model for data-driven choice modelling, Transportation Research Part C: Emerging Technologies, 126, p. 103050. https://doi.org/10.1016/j.trc.2021.103050
• 45. Xia Y., Chen H., Zimmermann R., 2023, A Random Effect Bayesian Neural Network (RE-BNN) for travel mode choice analysis across multiple regions, Travel Behaviour and Society, 30, 118-134. https://doi.org/10.1016/j.tbs.2022.08.011
• 46. Xie C., Lu J., Parkany E., 2003, Work Travel Mode Choice Modeling with Data Mining: Decision Trees and Neural Networks, Transportation Research Record: Journal of the Transportation Research Board, 1854, 1, pp. 50–61. https://doi.org/10.3141/1854-06
• 47. Yang, Liya et al., 2021, Driving as a commuting travel mode choice of car owners in urban China: Roles of the built environment, Cities, 112, p. 103114. https://doi.org/10.1016/j.cities.2021.103114
• 48. Yang, Linchuan et al., 2021, To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults, Journal of Transport Geography, 94, p. 103099. https://doi.org/10.1016/j.jtrangeo.2021.103099
• 49. Yu J.J.Q., 2020, Semi-supervised deep ensemble learning for travel mode identification, Transportation Research Part C: Emerging Technologies, 112, pp. 120–135. https://doi.org/10.1016/j.trc.2020.01.003
• 50. Zhang Y., Xie Y., 2008, Travel Mode Choice Modeling with Support Vector Machines, Transportation Research Record: Journal of the Transportation Research Board, 2076, 1, pp. 141–150. https://doi.org/10.3141/2076-16
• 51. Zhang J., Feng T., Timmermans H. J., Lin Z., 2023, Association rules and prediction of transportation mode choice: Application to national travel survey data. Transportation Research Part C: Emerging Technologies, 150, 104086. https://doi.org/10.1016/j.trc.2023.104086
• 52. Zhao X. et al., 2020, Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models, Travel Behaviour and Society, 20, pp. 22–35. https://doi.org/10.1016/j.tbs.2020.02.003