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Mode choice modelling for hailable rides: An investigation of the competition of Uber with other modes by using an integrated non-compensatory choice model with probabilistic choice set formation

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  • Habib, Khandker Nurul

Abstract

This paper presents an empirical investigation on demand for TNC services (e.g., Uber) in the Greater Toronto and Hamilton Areas (GTHA) through the application of an innovative discrete choice model. The proposed model combines Independent Availability Logit (IAL) and Constrained Multinomial Logit (CMNL) model formulation to reap the unique features of both. The proposed model is thus a Semi Compensatory Independent Availability Logit (SCIAL) model. For the empirical investigation, it uses a dataset of trip mode choices that suitable to represent ride-hailing service (e.g., Uber). Such trips are named as hailable trips in the dataset, which is drawn from a large scale household travel survey conducted in the region in 2016. To have a clear understanding of behavioural processes involved in the choice of travel mode of hailable trips, the proposed SCIAL model jointly models probabilistic choice set formation and conditional semi-compensatory choice. The empirical model does not reveal any evident competition between Uber and the private car, public transit, or non-motorized modes. It indicates that urban taxi is its main competitor, but there are notable differences in socio-demographic profiles of taxi and Uber users. For example, a taxi is preferred by older people, but younger people prefer uber, and there is no gender difference in such a pattern. In terms of the relationship between considering Uber as a feasible mode and choosing it for a trip, Uber has similarities to the car passenger mode. Merely accepting it as a feasible option has a significant influence on the final choice to use it. This indicates a potential new segment of the travel market, generated primarily for the advent of TNC service, e.g., Uber in Toronto.

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  • Habib, Khandker Nurul, 2019. "Mode choice modelling for hailable rides: An investigation of the competition of Uber with other modes by using an integrated non-compensatory choice model with probabilistic choice set formation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 205-216.
    • Handle: RePEc:eee:transa:v:129:y:2019:i:c:p:205-216
    DOI: 10.1016/j.tra.2019.08.014
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    References listed on IDEAS

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    Cited by:

    1. Wang, Kaili & Gao, Ya & Liu, Yicong & Nurul Habib, Khandker, 2023. "Exploring the choice between in-store versus online grocery shopping through an application of Semi-Compensatory Independent Availability Logit (SCIAL) model with latent variables," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    2. 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.
    3. Bi, Hui & Ye, Zhirui & Hu, Liyang & Zhu, He, 2021. "Why they don't choose bus service? Understanding special online car-hailing behavior near bus stops," Transport Policy, Elsevier, vol. 114(C), pages 280-297.
    4. Zgheib, Najib & Abou-Zeid, Maya & Kaysi, Isam, 2020. "Modeling demand for ridesourcing as feeder for high capacity mass transit systems with an application to the planned Beirut BRT," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 70-91.
    5. Gang Li & Ruining Zhang & Shujuan Guo & Junyi Zhang, 2022. "Analysis of Ride-Hailing Passenger Satisfaction and Life Satisfaction Based on a MIMIC Model," Sustainability, MDPI, vol. 14(17), pages 1-18, September.
    6. Risdiyanto Risdiyanto & Ahmad Munawar & Muhammad Zudhy Irawan & Miftahul Fauziah & Prawira Fajarindra Belgiawan, 2022. "Why Do Students Choose Buses over Private Motorcycles and Motorcycle-Based Ride-Sourcing? A Hybrid Choice Approach," Sustainability, MDPI, vol. 14(9), pages 1-19, April.
    7. Xiong, Ziyue & Jian Li, & Wu, Hangbin, 2021. "Understanding operation patterns of urban online ride-hailing services: A case study of Xiamen," Transport Policy, Elsevier, vol. 101(C), pages 100-118.
    8. Rico Krueger & Michel Bierlaire & Prateek Bansal, 2022. "A Data Fusion Approach for Ride-sourcing Demand Estimation: A Discrete Choice Model with Sampling and Endogeneity Corrections," Papers 2212.02178, arXiv.org.
    9. Misra, Aditi & Shirgaokar, Manish & Weinstein Agrawal, Asha & Dobbs, Bonnie & Wachs, Martin, 2022. "How older adults use Ride-hailing booking technology in California," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 11-30.
    10. Hasnine, Md Sami & Hawkins, Jason & Habib, Khandker Nurul, 2021. "Effects of built environment and weather on demands for transportation network company trips," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 171-185.
    11. Zhang, Zhaolin & Zhai, Guocong & Xie, Kun & Xiao, Feng, 2022. "Exploring the nonlinear effects of ridesharing on public transit usage: A case study of San Diego," Journal of Transport Geography, Elsevier, vol. 104(C).
    12. Aguilera-García, Álvaro & Gomez, Juan & Velázquez, Guillermo & Vassallo, Jose Manuel, 2022. "Ridesourcing vs. traditional taxi services: Understanding users’ choices and preferences in Spain," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 161-178.
    13. Assele, Samson Yaekob & Meulders, Michel & Vandebroek, Martina, 2022. "The value of consideration data in a discrete choice experiment," Journal of choice modelling, Elsevier, vol. 45(C).
    14. Narayanan, Santhanakrishnan & Antoniou, Constantinos, 2023. "Shared mobility services towards Mobility as a Service (MaaS): What, who and when?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 168(C).

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