Abstract
On-demand transportation services have been developing in an irresistible trend since their first launch in public. These services not only transform the urban mobility landscape, but also profoundly change individuals’ travel behavior and demand for cars. In this paper, we propose an integrated model structure which integrates empirical analysis into a discrete choice based analytical framework to investigate a heterogenous population’s choices on transportation mode and car ownership with the presence of ride-hailing. Distinguished from traditional discrete choice models where individuals’ choices are only affected by exogenous variables and are independent of other individuals’ choices, our model extends to capture the endogeneity of supply demand imbalance between ride-hailing service providers and users. Through equilibrium searching and counterfactual analysis, we further quantify the magnitude of impacts of platform operations and government policies on car demand, usage and traffic conditions. The structure of the model and managerial insights are explained in detail.
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- 1.
In order to participate in ride-hailing, platform drivers are required to possess a private car. They could either purchase a personal car or rent a car from the platform. In our model formulation we do not distinguish between these two ways as essentially they both represent a fixed ownership cost k.
- 2.
According to [15], Eq. (4) is derived based on deterministic fluid approximation of a multi-server loss queuing system with arrival rate D and total service rate S, in which, \(\alpha \) corresponds to the system utilization and \(\beta \) corresponds to the probability at which a job request could find an idle server upon arrival.
- 3.
See example of a major ride-hailing company’s incentive scheme on 2019: https://www.gojek.com/sg/blog/sg-driver-incentives-gojek-singapore/.
- 4.
Online Appendix available at: https://drive.google.com/file/d/1DQkbJSBWFoFIMmAEMxkfeo7qjJrT2CRI/view?usp=sharing.
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This research was partially funded by grant from the Singapore Ministry of Education (T2-1712).
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Su, Y., Xu, Y., Courcoubetis, C., Cheng, SF. (2021). Integrating Empirical Analysis into Analytical Framework: An Integrated Model Structure for On-Demand Transportation. In: Qiu, R., Lyons, K., Chen, W. (eds) AI and Analytics for Smart Cities and Service Systems. ICSS 2021. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-030-90275-9_25
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