Chinese Journal of Intelligent Science and Technology ›› 2022, Vol. 4 ›› Issue (3): 380-395.doi: 10.11959/j.issn.2096-6652.202233

• Papers and Reports • Previous Articles     Next Articles

Short-term traffic state reasoning and precise prediction in urban networks

Yuanqi QIN1, Qingyuan JI2, Jun GE1, Xingyuan DAI3, Yuanyuan CHEN3, Xiao WANG3,4   

  1. 1 Zhejiang Lab, Hangzhou 310000, China
    2 Enjoyor Technology Co., Ltd., Hangzhou 310030, China
    3 The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
    4 Qingdao Academy of Intelligent Industries, Qingdao 266000, China
  • Revised:2022-07-10 Online:2022-09-15 Published:2022-09-01
  • Supported by:
    The National Key Research and Development Program of China(SQ2019YFE012476)


The structure of urban traffic network has a significant impact on the formation and spatio-temporal pattern propagation of traffic congestions.However, in studies based on traditional traffic models or deep learning models, the generation of traffic mode can only be described indirectly by traffic indicators, without considering the traffic network feature.This makes it very difficult to accurately describe the propagation dynamics both in temporal and spatial dimensions and lacks specificity.To tackle the above-mentioned problems, a novel traffic state prediction approach based on traffic pattern reasoning (TP2) framework was proposed.The framework modeled congestion propagation as a dynamically evolving temporal knowledge graph (TKG), and applied an inferencing framework (TPP-TKG) that was based on a novel aggregator called RGraAN.TPP-TKG captured the spatial-temporal propagation pattern of traffic congestion, and combined related road links to a given link, and constructed correlated sub region of the traffic network.Then a traffic state predicting based on graph neural network was employed to predict short-term speed evolution of road links in this sub region.Comparing to the state-of-the-art benchmark models, TP2 achieves 1% ~ 2% higher accuracy.

Key words: traffic network structure, propagation mode, traffic mode reasoning and prediction, temporal knowledge graph, graph neural network.

CLC Number: 

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