Computer Science ›› 2021, Vol. 48 ›› Issue (7): 112-117.doi: 10.11896/jsjkx.201000089

• Database & Big Data & Data Science • Previous Articles     Next Articles

Multi-task Spatial-Temporal Graph Convolutional Network for Taxi Idle Time Prediction

SONG Long-ze, WAN Huai-yu, GUO Sheng-nan, LIN You-fang   

  1. School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,ChinaBeijing Key Lab of Traffic Data Analysis and Mining,Beijing 100044,China
  • Received:2020-10-17 Revised:2020-12-10 Online:2021-07-15 Published:2021-07-02
  • About author:SONG Long-ze,born in 1996,postgra-duate.His main research interests include spatial-temporal data mining and so on.(
    WAN Huai-yu,born in 1981,Ph.D,associate professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include social network mining,text mining,user behavior analysis and spatial-temporal data mining.
  • Supported by:
    Science Foundation of China Mobile-Ministry of Education(MCM20180202).

Abstract: The taxi idle time seriously affects the utilization efficiency of transportation resources and the driver’s income.Accurate taxi idle time prediction can effectively guide drivers to make reasonable path planning,and assist taxi platforms for efficient resource scheduling.However,in actual scenarios,the idle time in different areas of the city is affected by various factors such as regional traffic,passenger flow,and historical idle time.A spatial-temporal graph convolution network (MSTGCN) model based on multi-task framework is proposed to solve this problem.MSTGCN adopts a novel convolutional structure of spatial-temporal graph to comprehensively model the various spatial and temporal correlation factors that affect the idle time.A multi-task attention fusion mechanism is also proposed to improve the information acquisition ability and prediction performance of each task.Extensive experiments are carried out on two public data sets provided by Didi Chuxing GAIA Initiative,and the prediction results of the proposed model are better than that of other methods.

Key words: Attention mechanism, Graph convolutional network, Multi-task learning, Spatial-temporal data prediction, Taxi idle time

CLC Number: 

  • TP183
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