Abstract—As a fundamental transportation service, ride-hailing has greatly improved the city mobility efficiency and served millions of passengers in big metropolitan cities. However, due to the imbalance between the limited supply caused by the strict car-buying policy and the increasing travelling demand, ride-hailing services are far from satisfactory. A better prediction of travel demand is one possible solution of improving ride-hailing service efficiency and quality and the idle drivers can be scheduled to hotspots with more potential ride requests. In this paper, we explore the usage of deep learning technique, i.e., ConvLSTM networks, for ride-hailing service prediction. Experiment results on a real-world ride-hailing dataset provided by Didi Chuxing show the superiority of ConvLSTM over baseline methods including Multi-Layer Perceptron and two simple historical methods.
Index Terms—Ride-hailing service, prediction, deep learning.
Lingye Tan and Ziyang Zhang are with the Faculty of Engineering, University of New South Wales, Sydney, 2032, Australia (e-mail: tanlingyelynn@163.com, zzy967230@163.com). Weiwei Jiang is with the Department of Electronic Engineering, Tsinghua University, Beijing, People’s Republic of China (Corresponding author; e-mail: jwwthu@163.com).
Cite: Lingye Tan, Ziyang Zhang, and Weiwei Jiang, "Ride-Hailing Service Prediction Based on Deep Learning," International Journal of Machine Learning and Computing vol. 12, no. 1, pp. 1-6, 2022.
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