TY - JOUR
T1 - Joint predictions of multi-modal ride-hailing demands
T2 - A deep multi-task multi-graph learning-based approach
AU - Ke, Jintao
AU - Feng, Siyuan
AU - Zhu, Zheng
AU - Yang, Hai
AU - Ye, Jieping
N1 - Funding Information:
The work described in this paper was supported by a grant from Hong Kong Research Grants Council under project HKUST16208619 and a NSFC/RGC Joint Research grant N_HKUST627/18 (NSFC-RGC 71861167001). This work was also supported by the Hong Kong University of Science and Technology - DiDi Chuxing (HKUST-DiDi) Joint Laboratory.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/6
Y1 - 2021/6
N2 - Ride-hailing platforms generally provide various service options to customers, such as solo ride services, shared ride services, etc. It is generally expected that demands for different service modes are correlated, and the prediction of demand for one service mode can benefit from historical observations of demands for other service modes. Moreover, an accurate joint prediction of demands for multiple service modes can help the platforms better allocate and dispatch vehicle resources. Although there is a large stream of literature on ride-hailing demand predictions for one specific service mode, few efforts have been paid towards joint predictions of ride-hailing demands for multiple service modes. To address this issue, we propose a deep multi-task multi-graph learning approach, which combines two components: (1) multiple multi-graph convolutional (MGC) networks for predicting demands for different service modes, and (2) multi-task learning modules that enable knowledge sharing across multiple MGC networks. More specifically, two multi-task learning structures are established. The first one is the regularized cross-task learning, which builds cross-task connections among the inputs and outputs of multiple MGC networks. The second one is the multi-linear relationship learning, which imposes a prior tensor normal distribution on the weights of various MGC networks. Although there are no concrete bridges between different MGC networks, the weights of these networks are constrained by each other and subject to a common prior distribution. Evaluated with the for-hire-vehicle datasets in Manhattan, we show that our proposed approach outperforms the benchmark algorithms in prediction accuracy for different ride-hailing modes.
AB - Ride-hailing platforms generally provide various service options to customers, such as solo ride services, shared ride services, etc. It is generally expected that demands for different service modes are correlated, and the prediction of demand for one service mode can benefit from historical observations of demands for other service modes. Moreover, an accurate joint prediction of demands for multiple service modes can help the platforms better allocate and dispatch vehicle resources. Although there is a large stream of literature on ride-hailing demand predictions for one specific service mode, few efforts have been paid towards joint predictions of ride-hailing demands for multiple service modes. To address this issue, we propose a deep multi-task multi-graph learning approach, which combines two components: (1) multiple multi-graph convolutional (MGC) networks for predicting demands for different service modes, and (2) multi-task learning modules that enable knowledge sharing across multiple MGC networks. More specifically, two multi-task learning structures are established. The first one is the regularized cross-task learning, which builds cross-task connections among the inputs and outputs of multiple MGC networks. The second one is the multi-linear relationship learning, which imposes a prior tensor normal distribution on the weights of various MGC networks. Although there are no concrete bridges between different MGC networks, the weights of these networks are constrained by each other and subject to a common prior distribution. Evaluated with the for-hire-vehicle datasets in Manhattan, we show that our proposed approach outperforms the benchmark algorithms in prediction accuracy for different ride-hailing modes.
KW - Deep multi-task learning
KW - Demand prediction
KW - Multi-graph convolutional network
KW - Ride-hailing
UR - http://www.scopus.com/inward/record.url?scp=85103776909&partnerID=8YFLogxK
U2 - 10.1016/j.trc.2021.103063
DO - 10.1016/j.trc.2021.103063
M3 - Journal article
AN - SCOPUS:85103776909
SN - 0968-090X
VL - 127
JO - Transportation Research Part C: Emerging Technologies
JF - Transportation Research Part C: Emerging Technologies
M1 - 103063
ER -