Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice—A Systematic Review
Source abstract
Abstract
Forecasting travel demand requires a grasp of individual decision-making behavior. However, transport mode choice (TMC) is determined by personal and contextual factors that vary from person to person. Numerous characteristic... | Find, read and cite all the research you need on Tech Science Press
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Research context
OpenAlex topics: Traffic Prediction and Management Techniques · Transportation Planning and Optimization · Vehicle emissions and performance
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
Show source-provided references
1. Stradling · Handbook of Traffic Psychology · 2011 · 48510.1016/B978-0-12-381984-0.10034-7 ↗
2. The role of personal norms in the choice of mode for commuting10.1016/j.retrec.2020.100966 ↗
3. Connectivity, accessibility, and mobility relationships in the context of low-volume road networks10.1177/0361198119854091 ↗
4. The impact of stop-making and travel time reliability on commute mode choice10.1016/j.trb.2005.09.008 ↗
5. Effects of cost adjustment on travel mode choice: Analysis and comparison of different logit models10.1016/j.trpro.2017.05.150 ↗
6. Time-use and spatio-temporal variables influence on physical activity intensity, physical and social health of travelers10.3390/su132112226 ↗
7. Travel mode choice of the elderly: Effects of personal, household, neighborhood, and trip characteristics10.3141/1894-13 ↗
8. Exploring the effect of the built environment, weather condition and departure time of travel on mode choice decision for different travel purposes: Evidence from Isfahan, Iran10.1016/j.cstp.2021.05.002 ↗
9. Perceived accessibility, mobility, and connectivity of public transportation systems
10. Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis
11. Determinants of transport mode choice: A comparison of Germany and the USA10.1016/j.jtrangeo.2010.07.005 ↗
12. The impact of parking pricing on mode choice10.1016/j.trpro.2023.02.175 ↗
13. Parking subsidies and travel choices: Assessing the evidence10.1007/BF02125333 ↗
14. Promoting walking and cycling as an alternative to using cars: Systematic review
15. Limits to active transport substitution of short car trips10.1016/j.trd.2013.03.001 ↗
16. Determinants of urban public transport efficiency: Case study of the Czech Republic10.1186/s12544-018-0311-y ↗
17. Determinants of transport mode choice in metropolitan areas the case of the metropolitan area of the Valley of Mexico10.1016/j.jtrangeo.2020.102766 ↗
18. Determinants of transport mode choice for non-commuting trips: The roles of transport, land use and socio-demographic characteristics10.3390/urbansci3030082 ↗
19. Travel mode choice prediction to pursue sustainable transportation and enhance health parameters using R10.3390/su16145908 ↗
20. Health enhancement through activity travel participation and physical activity intensity10.1016/j.jth.2024.101927 ↗
21. Fault detection and identification with a new feature selection based on mutual information10.1016/j.jprocont.2007.08.003 ↗
22. Classification of imbalanced travel mode choice to work data using adjustable SVM model10.3390/app112411916 ↗
23. Guthrie · NIST/SEMATECH e-Handbook of Statistical Methods (NIST Handbook 151) · 2020
24. Nonlinear relationships between vehicle ownership and household travel characteristics and built environment attributes in the US using the XGBT algorithm
25. Machine learning travel mode choices: Comparing the performance of an extreme gradient boosting model with a multinomial logit model10.1177/0361198118773556 ↗
26. Assessing the performance of gradient-boosting models for predicting the travel mode choice using household survey data10.1061/(ASCE)UP.1943-5444.0000830 ↗
27. Targeting sustainable transportation development: The support vector machine and the bayesian optimization algorithm for classifying household vehicle ownership
28. Travel mode choice modeling: Predictive efficacy between machine learning models and discrete choice model10.2174/1874447802115010241 ↗
29. How sustainable is people travel to reach public transit stations to go to work? A machine learning approach to reveal complex relationships
30. On hyperparameter optimization of machine learning methods using a bayesian optimization algorithm to predict work travel mode choice10.1109/ACCESS.2023.3247448 ↗
31. Predicting the travel mode choice with interpretable machine learning techniques: A comparative study10.1016/j.tbs.2022.07.003 ↗
32. Analysis of travel mode choice in seoul using an interpretable machine learning approach10.1155/2021/6685004 ↗
33. X. Zhao, X. Yan, and P. Van Hentenryck, “Modeling heterogeneity in mode-switching behavior under a mobility-on-demand transit system: An interpretable machine learning approach,” 2019, arXiv:1902.02904.
34. Understanding travel mode choice behavior: Influencing factors analysis and prediction with machine learning method
35. The determinants of travel mode choice: The case of Łódź, Poland
36. Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance
37. Ali · Advanced Solutions for Mobility in Urban Areas, Lecture Notes in Networks and Systems · 2024 · 907 · 65
38. Dora · Transport, Environment and Health (No. 89) · 2000
39. Aijaz · Smart Technologies for Energy and Environmental Sustainability · 2022 · 131
40. Travel behaviour and health: A conceptual model and research agenda10.1016/j.jth.2016.07.003 ↗
41. Urban forms and health promotion: An evaluation based on health-related QOL indicators
42. Interaction of activity travel, GHG emissions, and health parameters using R—A step towards sustainable transportation system10.1016/j.asej.2024.103050 ↗
43. NextGen National Household Travel Survey · 2022
44. Greenhouse gas emissions of electric vehicles in Europe considering different charging strategies10.1016/j.trd.2020.102534 ↗
45. Hollevoet · Urban Transport XVII: Urban Transport and the Environment in the 21st Century · 2011 · 116 · 12910.2495/UT110121 ↗
46. Improving health through policies that promote active travel: A review of evidence to support integrated health impact assessment10.1016/j.envint.2011.02.003 ↗
47. Dynamic accessibility analysis in location-based service using an incremental parallel algorithm10.1068/b33118 ↗
48. An advanced machine learning approach to predicting pedestrian fatality caused by road crashes: A step toward sustainable pedestrian safety10.3390/su14042436 ↗
49. The main determinants of the demand for public transport: A comparative analysis of England and France using shrinkage estimators
50. The role of accessibility and connectivity in mode choice. A structural equation modeling approach10.1016/j.trpro.2015.09.036 ↗
51. The effect of neighbourhood and urban center structures on active travel in small cities10.1016/j.cities.2022.104050 ↗
52. Sultana · Factors Affecting Parents’ Choice of Active Transport Modes for Children’s Commute to School: Evidence from 2017 NHTS Data · 2019
53. Investigating the use of active transportation modes among university employees through an advanced decision tree algorithm10.53623/csue.v1i1.28 ↗
54. Boosting support vector machines for imbalanced data sets10.1007/s10115-009-0198-y ↗
55. FSVM-CIL: Fuzzy support vector machines for class imbalance learning10.1109/TFUZZ.2010.2042721 ↗
56. Class-boundary alignment for imbalanced dataset learning
57. Machine learning techniques for evaluating the nonlinear link between built-environment characteristics and travel behaviors: A systematic review
58. Artificial intelligent techniques for prediction of rock strength and deformation properties—A review10.1016/j.istruc.2023.06.131 ↗
59. Assessing impacts of the built environment on mobility: A joint choice model of travel mode and duration
60. A Random Effect Bayesian Neural Network (RE-BNN) for travel mode choice analysis across multiple regions10.1016/j.tbs.2022.08.011 ↗
61. Noorbakhsh · Procedia Computer Science · 2023 · 220 · 62410.1016/j.procs.2023.03.079 ↗
62. Mode shift behaviour and user willingness to adopt the electric two-wheeler: A study based on Indian road user preferences10.1016/j.ijtst.2022.03.008 ↗
63. A prediction and behavioural analysis of machine learning methods for modelling travel mode choice10.1016/j.trc.2023.104318 ↗
64. How rail transit makes a difference in people’s multimodal travel behaviours: An analysis with the XGBoost method
65. Testing and enhancing spatial transferability of artificial neural networks based travel behavior models10.1080/19427867.2022.2130150 ↗
66. Non-linear associations between the urban built environment and commuting modal split: A random forest approach and SHAP evaluation10.1109/ACCESS.2023.3241627 ↗
67. Joint prediction of travel mode choice and purpose from travel surveys: A multitask deep learning approach
68. Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms10.1016/j.jtrangeo.2022.103482 ↗
69. A systematic comparative evaluation of machine learning classifiers and discrete choice models for travel mode choice in the presence of response heterogeneity10.1016/j.eswa.2021.116253 ↗
70. Application of machine learning to child mode choice with a novel technique to optimize hyperparameters10.3390/ijerph192416844 ↗
71. Modeling the behavior in choosing the travel mode for long-distance travel using supervised machine learning algorithms
72. The identification of significant features towards travel mode choice and its prediction via optimised random forest classifier: An evaluation for active commuting behavior10.1016/j.jth.2022.101362 ↗
73. Transportation mode detection by using smartphones and smartwatches with machine learning10.1007/s12205-022-1281-0 ↗
74. A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling10.1016/j.trpro.2022.02.047 ↗
75. ResLogit: A residual neural network logit model for data-driven choice modelling10.1016/j.trc.2021.103050 ↗
76. Transportation mode choice behavior with recommender systems: A case study on Beijing
77. Extrapolation-enhanced model for travel decision making: An ensemble machine learning approach considering behavioral theory10.1016/j.knosys.2021.106882 ↗
78. Using neural nets to predict transportation mode choice: Amsterdam network change analysis10.1007/s12652-020-02855-6 ↗
79. Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models10.1016/j.tbs.2020.02.003 ↗
80. Slik · Procedia Computer Science · 2020 · 170 · 16910.1016/j.procs.2020.03.022 ↗
81. Machine learning applications in activity-travel behaviour research: A review10.1080/01441647.2019.1704307 ↗
82. Clustering life course to understand the heterogeneous effects of life events, gender, and generation on habitual travel modes10.1109/ACCESS.2020.3032328 ↗
83. Using neural nets to predict transportation mode choice: An Amsterdam case study10.1016/j.procs.2020.03.015 ↗
84. W. Yan, W. Zhou, C. Tan, and L. Fan, “Employee ridesharing: Reinforcement learning and choice modeling,” presented at the 25th Am. Conf. Inf. Syst., AMCIS 2019, Jun. 15–19, 2019.
85. Gender and active travel: A qualitative data synthesis informed by machine learning10.1186/s12966-019-0904-4 ↗
86. Applying a random forest method approach to model travel mode choice behavior10.1016/j.tbs.2018.09.002 ↗
87. Travel mode choice: A data fusion model using machine learning methods and evidence from travel diary survey data10.1080/23249935.2019.1620380 ↗
88. Travel-to-school mode choice modelling employing artificial intelligence techniques: A comparative study10.3390/su11164484 ↗
89. A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice10.1007/s11116-017-9770-6 ↗
90. Modelling latent travel behaviour characteristics with generative machine learning
91. A comparative study of machine learning classifiers for modeling travel mode choice10.1016/j.eswa.2017.01.057 ↗
92. Influence of activity-travel participation, travel mode choice, and multitasking activities on subjective well-being using R10.3390/su152316338 ↗
93. The influence of travel mode choice on subjective wellbeing—A case study10.20858/tp.2023.18.4.01 ↗
94. Mode choice of mothers travelling with young children in malaysia
95. Private or public transport? The determinants of travel behaviour in post-industrial city-the case of Łódź
96. Built environment influences commute mode choice in a global south megacity context: Insights from explainable machine learning approach10.1016/j.jtrangeo.2024.103828 ↗
97. Examination of the effects of the pandemic process on the E-scooter Usage Behaviours of individuals with machine learning10.5507/tots.2023.016 ↗
98. A review of Machine Learning (ML) algorithms used for modeling travel mode choice10.15446/dyna.v86n211.79743 ↗
99. Exploring passengers’ choice of transfer city in air-to-rail intermodal travel using an interpretable ensemble machine learning approach10.1007/s11116-023-10375-3 ↗
100. Travel behaviour and health: Interaction of activity-travel pattern, travel parameter and physical intensity