IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i22p15455-d979369.html
   My bibliography  Save this article

Construction of Commuters’ Multi-Mode Choice Model Based on Public Transport Operation Data

Author

Listed:
  • Lingjuan Chen

    (School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Yijing Zhao

    (School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Zupeng Liu

    (School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Xinran Yang

    (School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430065, China)

Abstract

Travel mode selection is a crucial aspect of traffic distribution and forecasting in a comprehensive transportation system, which has significant implications for resource allocation and optimal management. As commuters are the main part of urban travel, studying the factors that affect their choice of transport mode plays a crucial role in urban traffic management and planning. Based on public transport operation data, a travel chain is created by identifying boarding stations, alighting stations, and transfer behaviors, and includes detailed travel information. The regression and correlation coefficients of departures and arrivals at stations are confirmed to be 0.98 and 0.92 in the presented data, indicating the viability of the recognition method. Then, multiple travel modes are identified based on the origin and destination, and the proportion of mode selection is determined by the actual travel chain. Using maximum likelihood estimation (MLS) and NLOGIT software, the random parameter logit (RPL) mode is used to estimate the relationship between travel mode selection and characteristic variables such as travel time, distance, cost, comfort, walking distance, and waiting time. The results indicate that walking distance, travel distance, and comfort have a greater influence on travel choice, and that walking distance is a random parameter with a normal distribution, reflecting the diversity of commuters. In addition, this paper discusses the influence degree of the change of characteristic variables of a transport mode on the choice between it and other modes. These results can be used as reference for relevant departments to make measures to improve the overall efficiency of the urban transportation system.

Suggested Citation

  • Lingjuan Chen & Yijing Zhao & Zupeng Liu & Xinran Yang, 2022. "Construction of Commuters’ Multi-Mode Choice Model Based on Public Transport Operation Data," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15455-:d:979369
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/22/15455/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/22/15455/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dong-Gyun Ku & Jung-Sik Um & Young-Ji Byon & Joo-Young Kim & Seung-Jae Lee, 2021. "Changes in Passengers’ Travel Behavior Due to COVID-19," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    2. Jianchuan Xianyu & Soora Rasouli & Harry Timmermans, 2017. "Analysis of variability in multi-day GPS imputed activity-travel diaries using multi-dimensional sequence alignment and panel effects regression models," Transportation, Springer, vol. 44(3), pages 533-553, May.
    3. Andersson, David & Nässén, Jonas, 2016. "The Gothenburg congestion charge scheme: A pre–post analysis of commuting behavior and travel satisfaction," Journal of Transport Geography, Elsevier, vol. 52(C), pages 82-89.
    4. Regier, Dean A. & Ryan, Mandy & Phimister, Euan & Marra, Carlo A., 2009. "Bayesian and classical estimation of mixed logit: An application to genetic testing," Journal of Health Economics, Elsevier, vol. 28(3), pages 598-610, May.
    5. Phattarasuda Witchayaphong & Surachet Pravinvongvuth & Kunnawee Kanitpong & Kazushi Sano & Suksun Horpibulsuk, 2020. "Influential Factors Affecting Travelers’ Mode Choice Behavior on Mass Transit in Bangkok, Thailand," Sustainability, MDPI, vol. 12(22), pages 1-18, November.
    6. Wusheng Liu & Qian Tan & Lisheng Liu, 2020. "Destination Estimation for Bus Passengers Based on Data Fusion," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, September.
    7. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    8. Sarrias, Mauricio & Daziano, Ricardo, 2017. "Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i02).
    9. Bhat, Chandra R., 1995. "A heteroscedastic extreme value model of intercity travel mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 29(6), pages 471-483, December.
    10. Marcel Paulssen & Dirk Temme & Akshay Vij & Joan Walker, 2014. "Values, attitudes and travel behavior: a hierarchical latent variable mixed logit model of travel mode choice," Transportation, Springer, vol. 41(4), pages 873-888, July.
    11. Fuhrer, Jeffrey C. & Moore, George R. & Schuh, Scott D., 1995. "Estimating the linear-quadratic inventory model Maximum likelihood versus generalized method of moments," Journal of Monetary Economics, Elsevier, vol. 35(1), pages 115-157, February.
    12. Sun, Lian-Ju & Gao, Zi-You, 2007. "An equilibrium model for urban transit assignment based on game theory," European Journal of Operational Research, Elsevier, vol. 181(1), pages 305-314, August.
    13. Yong, Juan & Zheng, Linjiang & Mao, Xiaowen & Tang, Xi & Gao, Ang & Liu, Weining, 2021. "Mining metro commuting mobility patterns using massive smart card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    14. Shen, Qing & Chen, Peng & Pan, Haixiao, 2016. "Factors affecting car ownership and mode choice in rail transit-supported suburbs of a large Chinese city," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 31-44.
    15. Hasnine, Md Sami & Lin, TianYang & Weiss, Adam & Habib, Khandker Nurul, 2018. "Determinants of travel mode choices of post-secondary students in a large metropolitan area: The case of the city of Toronto," Journal of Transport Geography, Elsevier, vol. 70(C), pages 161-171.
    16. Link, Heike, 2015. "Is car drivers’ response to congestion charging schemes based on the correct perception of price signals?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 71(C), pages 96-109.
    17. Mariel, Petr & Meyerhoff, Jürgen, 2018. "A More Flexible Model or Simply More Effort? On the Use of Correlated Random Parameters in Applied Choice Studies," Ecological Economics, Elsevier, vol. 154(C), pages 419-429.
    18. Huang, Yuqiao & Gao, Linjie & Ni, Anning & Liu, Xiaoning, 2021. "Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 93(C).
    19. Ilahi, Anugrah & Belgiawan, Prawira F. & Balac, Milos & Axhausen, Kay W., 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, Elsevier, vol. 150(C), pages 398-422.
    20. Wissam Qassim Al-Salih & Domokos Esztergár-Kiss, 2021. "Linking Mode Choice with Travel Behavior by Using Logit Model Based on Utility Function," Sustainability, MDPI, vol. 13(8), pages 1-18, April.
    21. Albert, Gila & Mahalel, David, 2006. "Congestion tolls and parking fees: A comparison of the potential effect on travel behavior," Transport Policy, Elsevier, vol. 13(6), pages 496-502, November.
    22. Yang, Zijun & Wang, Bowen & Jiao, Kui, 2020. "Life cycle assessment of fuel cell, electric and internal combustion engine vehicles under different fuel scenarios and driving mileages in China," Energy, Elsevier, vol. 198(C).
    23. Ha, Jaehyun & Lee, Sugie & Ko, Joonho, 2020. "Unraveling the impact of travel time, cost, and transit burdens on commute mode choice for different income and age groups," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 147-166.
    24. Hensher,David A. & Rose,John M. & Greene,William H., 2015. "Applied Choice Analysis," Cambridge Books, Cambridge University Press, number 9781107465923, October.
    25. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    26. Kang, Hejun & Scott, Darren M., 2010. "Exploring day-to-day variability in time use for household members," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(8), pages 609-619, October.
    27. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, vol. 30(2), pages 133-176, May.
    28. Tang, Xinyi & Wang, Dianhai & Sun, Yilin & Chen, Mengwei & Waygood, E. Owen D., 2020. "Choice behavior of tourism destination and travel mode: A case study of local residents in Hangzhou, China," Journal of Transport Geography, Elsevier, vol. 89(C).
    29. Timothy Otim & Leandro Dörfer & Dina Bousdar Ahmed & Estefania Munoz Diaz, 2022. "Modeling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories from Beijing," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Hongtai & Ping, An & Wei, Hongmin & Zhai, Guocong, 2023. "Unique in the metro system: The likelihood to re-identify a metro user with limited trajectory points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    2. Xudong Li & Zhongzhen Yang & Feng Lian, 2023. "Optimizing On-Demand Bus Services for Remote Areas," Sustainability, MDPI, vol. 15(9), pages 1-20, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. John V. Colias & Stella Park & Elizabeth Horn, 2021. "Optimizing B2B product offers with machine learning, mixed logit, and nonlinear programming," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(3), pages 157-172, September.
    2. Coppola, Pierluigi & De Fabiis, Francesco & Silvestri, Fulvio, 2024. "Urban Air Mobility (UAM): Airport shuttles or city-taxis?," Transport Policy, Elsevier, vol. 150(C), pages 24-34.
    3. Sharma, Reema Bera & Majumdar, Bandhan Bandhu & Maitra, Bhargab, 2024. "Commuter and non-commuter preferences for plug-in hybrid electric vehicle: A case study of Delhi and Kolkata, India," Research in Transportation Economics, Elsevier, vol. 103(C).
    4. Haghani, Milad & Bliemer, Michiel C.J. & Hensher, David A., 2021. "The landscape of econometric discrete choice modelling research," Journal of choice modelling, Elsevier, vol. 40(C).
    5. Schueftan, Alejandra & Aravena, Claudia & Reyes, René, 2021. "Financing energy efficiency retrofits in Chilean households: The role of financial instruments, savings and uncertainty in energy transition," Resource and Energy Economics, Elsevier, vol. 66(C).
    6. Bing Han & Shuang Ren & Jingjing Bao, 2020. "Mixed Logit Model Based on Improved Nonlinear Utility Functions: A Market Shares Solution Method of Different Railway Traffic Modes," Sustainability, MDPI, vol. 12(4), pages 1-25, February.
    7. Cordera, Rubén & Luigi dell’Olio, & Sipone, Silvia & Moura, José Luis, 2024. "Modeling airport choice for a multi-airport area using a random parameter logit model," Research in Transportation Economics, Elsevier, vol. 104(C).
    8. Campbell, Danny, 2007. "Combining mixed logit models and random effects models to identify the determinants of willingness to pay for rural landscape improvements," 81st Annual Conference, April 2-4, 2007, Reading University, UK 7975, Agricultural Economics Society.
    9. Immerzeel, Bart & Vermaat, Jan E. & Juutinen, Artti & Pouta, Eija & Artell, Janne, 2022. "Appreciation of Nordic landscapes and how the bioeconomy might change that: Results from a discrete choice experiment," Land Use Policy, Elsevier, vol. 113(C).
    10. Grammatikopoulou, Ioanna & Badura, Tomas & Vačkářová, Davina, 2020. "Public preferences for post 2020 agri-environmental policy in the Czech Republic: A choice experiment approach," Land Use Policy, Elsevier, vol. 99(C).
    11. Ogoudélé S. Codjo & Alvaro Durand‐Morat & Grant H. West & Lawton Lanier Nalley & Rodolfo M. Nayga & Eric J. Wailes, 2021. "Estimating demand elasticities for rice in Benin," Agricultural Economics, International Association of Agricultural Economists, vol. 52(2), pages 343-361, March.
    12. Kassie, Girma T. & Zeleke, Fresenbet & Birhanu, Mulugeta Y. & Scarpa, Riccardo, 2020. "Reminder Nudge, Attribute Nonattendance, and Willingness to Pay in a Discrete Choice Experiment," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304208, Agricultural and Applied Economics Association.
    13. John V. Colias & Stella Park & Elizabeth Horn, 2023. "Optimizing B2B Product Offers with Machine Learning, Mixed Logit, and Nonlinear Programming," Papers 2308.07830, arXiv.org.
    14. Ioanna Grammatikopoulou & Janne Artell & Turo Hjerppe & Eija Pouta, 2020. "A Mire of Discount Rates: Delaying Conservation Payment Schedules in a Choice Experiment," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 77(3), pages 615-639, November.
    15. Sarrias, Mauricio & Daziano, Ricardo A., 2018. "Individual-specific point and interval conditional estimates of latent class logit parameters," Journal of choice modelling, Elsevier, vol. 27(C), pages 50-61.
    16. Fosgerau, Mogens & Bierlaire, Michel, 2007. "A practical test for the choice of mixing distribution in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 784-794, August.
    17. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    18. Jianhua Wang & Jiaye Ge & Yuting Ma, 2018. "Urban Chinese Consumers’ Willingness to Pay for Pork with Certified Labels: A Discrete Choice Experiment," Sustainability, MDPI, vol. 10(3), pages 1-14, February.
    19. Qian, Lixian & Grisolía, Jose M. & Soopramanien, Didier, 2019. "The impact of service and government-policy attributes on consumer preferences for electric vehicles in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 122(C), pages 70-84.
    20. Scaccia, Luisa & Marcucci, Edoardo & Gatta, Valerio, 2023. "Prediction and confidence intervals of willingness-to-pay for mixed logit models," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 54-78.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15455-:d:979369. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.