Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice-A Systematic Review

被引:0
|
作者
Ali, Mujahid [1 ]
机构
[1] Silesian Tech Univ, Dept Transport Syst Traff Engn & Logist, Fac Transport & Aviat Engn, PL-40019 Katowice, Poland
来源
CMC-COMPUTERS MATERIALS & CONTINUA | 2024年 / 81卷 / 02期
关键词
Machine learning techniques; AI; transport mode choice; discrete choice model; sustainable transportation; SUPPORT VECTOR MACHINES; BUILT ENVIRONMENT; TRAVEL BEHAVIOR; ACCESSIBILITY; CONNECTIVITY; MOBILITY; HEALTH; IMPACT; TIME;
D O I
10.32604/cmc.2024.058888
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 characteristics have a substantial impact on travel behavior (TB), which makes it important to take into account while studying transport options. Traditional statistical techniques frequently presume linear correlations, but real-world data rarely follows these presumptions, which may make it harder to grasp the complex interactions. Thorough systematic review was conducted to examine how machine learning (ML) approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges. An in-depth analysis of discrete choice models (DCM) and several ML algorithms, datasets, model validation strategies, and tuning techniques employed in previous research is carried out in the present study. Besides, the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes. The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research. With a total of 39 studies, our findings shed important light on the significance of considering factors that influence the TMC. The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms. RF (random forest), SVM (support vector machine), ANN (artificial neural network), and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%; however, the adjusted kernel enhanced the accuracy of SVM 99.81% which shows that the interpretable algorithms outperformed the typical algorithms. The sensitivity analysis indicates that the most significant parameters influencing TMC are the age, total trip time, and the number of drivers.
引用
收藏
页码:2161 / 2194
页数:34
相关论文
共 50 条
  • [21] DETERMINANTS OF TRANSPORT MODE CHOICE IN DUTCH CITIES - SOME DISAGGREGATE STOCHASTIC MODELS - DEDONNEA,FX
    FLOWERDE.T
    ECONOMICA, 1973, 40 (158) : 235 - 235
  • [22] Artificial intelligence in radiation therapy treatment planning: A discrete choice experiment
    Lewandowska, Milena
    Street, Deborah
    Yim, Jackie
    Jones, Scott
    Viney, Rosalie
    JOURNAL OF MEDICAL RADIATION SCIENCES, 2024,
  • [23] Determinants of Food Choice in Athletes: A Systematic Scoping Review
    Pelly, Fiona E.
    Thurecht, Rachael L.
    Slater, Gary
    SPORTS MEDICINE-OPEN, 2022, 8 (01)
  • [24] Determinants of Food Choice in Athletes: A Systematic Scoping Review
    Fiona E. Pelly
    Rachael L. Thurecht
    Gary Slater
    Sports Medicine - Open, 2022, 8
  • [25] A systematic review of discrete choice experiments in stroke rehabilitation
    Jolliffe, Laura
    Christie, Lauren J.
    Fearn, Nicola
    Nohrenberg, Michael
    Liu, Rasia
    Williams, Julie F.
    Parsons, Mark W.
    Pearce, Alison M.
    TOPICS IN STROKE REHABILITATION, 2024, 31 (06) : 632 - 643
  • [26] Discrete choice experiments of pharmacy services: a systematic review
    Caroline Vass
    Ewan Gray
    Katherine Payne
    International Journal of Clinical Pharmacy, 2016, 38 : 620 - 630
  • [27] Discrete choice experiments of pharmacy services: a systematic review
    Vass, Caroline
    Gray, Ewan
    Payne, Katherine
    INTERNATIONAL JOURNAL OF CLINICAL PHARMACY, 2016, 38 (03) : 620 - 630
  • [28] A Systematic Review of Discrete Choice Experiments in Oncology Treatments
    Hannah Collacott
    Vikas Soekhai
    Caitlin Thomas
    Anne Brooks
    Ella Brookes
    Rachel Lo
    Sarah Mulnick
    Sebastian Heidenreich
    The Patient - Patient-Centered Outcomes Research, 2021, 14 : 775 - 790
  • [29] A Systematic Review of Discrete Choice Experiments in Oncology Treatments
    Collacott, Hannah
    Soekhai, Vikas
    Thomas, Caitlin
    Brooks, Anne
    Brookes, Ella
    Lo, Rachel
    Mulnick, Sarah
    Heidenreich, Sebastian
    PATIENT-PATIENT CENTERED OUTCOMES RESEARCH, 2021, 14 (06): : 775 - 790
  • [30] Predicting transport mode choice preferences in a university district with decision tree-based models
    Diaz-Ramirez, Jenny
    Estrada-Garcia, Juan Alberto
    Figueroa-Sayago, Juliana
    CITY AND ENVIRONMENT INTERACTIONS, 2023, 20