Multitask Learning and GCN-Based Taxi Demand Prediction for a Traffic Road Network

Chen, Zhe; Zhao, Bin; Wang, Yuehan; Duan, Zongtao; Zhao, Xin

doi:10.3390/s20133776

Open AccessArticle

Multitask Learning and GCN-Based Taxi Demand Prediction for a Traffic Road Network

by

Zhe Chen

,

Bin Zhao

,

Yuehan Wang

,

Zongtao Duan

^* and

Xin Zhao

School of Information Engineering, Chang’an University, Xi’an 710064, China

^*

Author to whom correspondence should be addressed.

Sensors 2020, 20(13), 3776; https://doi.org/10.3390/s20133776

Submission received: 30 April 2020 / Revised: 1 July 2020 / Accepted: 3 July 2020 / Published: 5 July 2020

(This article belongs to the Special Issue Intelligent Sensors for Smart City)

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Abstract

The accurate forecasting of urban taxi demands, which is a hot topic in intelligent transportation research, is challenging due to the complicated spatial-temporal dependencies, the dynamic nature, and the uncertainty of traffic. To make full use of the global and local correlations between traffic flows on road sections, this paper presents a deep learning model based on a graph convolutional network, long short-term memory (LSTM), and multitask learning. First, an undirected graph model was formed by considering the spatial pattern distribution of taxi trips on road networks. Then, LSTMs were used to extract the temporal features of traffic flows. Finally, the model was trained using a multitask learning strategy to improve the model’s generalizability. In the experiments, the efficiency and accuracy were verified with real-world taxi trajectory data. The experimental results showed that the model could effectively forecast the short-term taxi demands on the traffic network level and outperform state-of-the-art traffic prediction methods.

Keywords: taxi demand prediction; graph neural network; GPS trajectory of taxis; spatial-temporal model; deep learning

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MDPI and ACS Style

Chen, Z.; Zhao, B.; Wang, Y.; Duan, Z.; Zhao, X. Multitask Learning and GCN-Based Taxi Demand Prediction for a Traffic Road Network. Sensors 2020, 20, 3776. https://doi.org/10.3390/s20133776

AMA Style

Chen Z, Zhao B, Wang Y, Duan Z, Zhao X. Multitask Learning and GCN-Based Taxi Demand Prediction for a Traffic Road Network. Sensors. 2020; 20(13):3776. https://doi.org/10.3390/s20133776

Chicago/Turabian Style

Chen, Zhe, Bin Zhao, Yuehan Wang, Zongtao Duan, and Xin Zhao. 2020. "Multitask Learning and GCN-Based Taxi Demand Prediction for a Traffic Road Network" Sensors 20, no. 13: 3776. https://doi.org/10.3390/s20133776

APA Style

Chen, Z., Zhao, B., Wang, Y., Duan, Z., & Zhao, X. (2020). Multitask Learning and GCN-Based Taxi Demand Prediction for a Traffic Road Network. Sensors, 20(13), 3776. https://doi.org/10.3390/s20133776

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Multitask Learning and GCN-Based Taxi Demand Prediction for a Traffic Road Network

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