Data-Driven Modeling of Ride-Hailing Trajectories

4 Pages Posted: 12 Jun 2018

See all articles by Hao Yuan

Hao Yuan

University of Michigan at Ann Arbor - Department of Industrial and Operations Engineering

Qi Luo

University of Michigan at Ann Arbor - Department of Industrial and Operations Engineering

Robert Hampshire

University of Michigan at Ann Arbor - Transportation Research Institute

Date Written: May 28, 2018

Abstract

Predicting the drivers' movements in a spatial ride-hailing network accurately and promptly is essential for developing both ride dispatching and dynamic pricing algorithms. In this paper, we construct a data-driven model based on recurrent neural networks. We test the performance of our model using a real-world trajectory database. We find that our approach outperforms the base model (with an accuracy of 74%~78%). We also conclude from the analysis that drivers only look ahead a short distance when making route choice decisions. This model is useful for developing operational algorithms, traffic simulators, and qualitative studies.

Keywords: ride-hailing, trajectory prediction, recurrent neural network

Suggested Citation

Yuan, Hao and Luo, Qi and Hampshire, Robert, Data-Driven Modeling of Ride-Hailing Trajectories (May 28, 2018). Available at SSRN: https://ssrn.com/abstract=3186125 or http://dx.doi.org/10.2139/ssrn.3186125

Hao Yuan

University of Michigan at Ann Arbor - Department of Industrial and Operations Engineering ( email )

1205 Beal Avenue
Ann Arbor, MI 48109
United States

Qi Luo

University of Michigan at Ann Arbor - Department of Industrial and Operations Engineering ( email )

1205 Beal Avenue
Ann Arbor, MI 48109
United States

Robert Hampshire (Contact Author)

University of Michigan at Ann Arbor - Transportation Research Institute ( email )

2901 Baxter Road
Ann Arbor, MI 48109
United States

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