Analysis of Propagation Mechanism of Recurrent Congestion Based on Dynamic Bayesian Network

doi:10.12141/j.issn.1000-565X.210744

Abstract

Abstract:

In order to accurately identify the propagation path of recurrent congestion and analyze its propagation mechanism to alleviate the traffic congestion at the source and block the propagation path, this study proposed a method of analyzing the congestion propagation mechanism based on taxi GPS data. Firstly, the number of vehicle trajectories and travel speed were used to identify the traffic congestion area based on the space-time cube model of urban road network. According to the relative spatio-temporal stability of recurrent congestion, a time-section recognition method of recurrent traffic congestion grid was proposed. Secondly, the spatio-temporal congestion propagation trees was constructed. Aiming at the dynamics of traffic congestion propagation, a method of mining frequency-weighted recurrent propagation relation set was proposed to construct recurrent congestion propagation subtrees. Thirdly, the Dynamic Bayesian Network was introduced to obtain the congestion propagation probability through Bayesian estimation. Finally, taking the eastern section of the South Second Ring Road in Xi'an as an example, the proposed method was used to conduct an empirical analysis to explore the congestion propagation path and its probability. The research results show that based on the space-time cube model, the recurrent congestion grids in each time frame identified by the number of vehicle trajectories and travel speed lay the foundation for the accurate analysis of the congestion propagation mechanism. The congestion propagation trees constructed by using the STC algorithm, and the proposed frequent itemsets mining method considering temporal reproducibility characteristics of congestion propagation can be used to reconstruct the recurrent congestion propagation subtrees and clarify the propagation path of recurrent congestion. The possibility of congestion propagation between grids was analyzed based on the Dynamic Bayesian Network. It provides a theoretical basis for dynamically finding the key segment in the congestion propagation network, scientifically and reasonably formulating the congestion alleviation scheme and the task timeline.

Key words: identification of recurrent congestion area, frequency-weighted frequent itemset, recurrent congestion propagation mechanism, Dynamic Bayesian Network, taxi GPS trajectory

CLC Number:

U491.112

CHENG Xiaoyun, QU Xiaping, ZHANG Xueyu, et al. Analysis of Propagation Mechanism of Recurrent Congestion Based on Dynamic Bayesian Network[J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(11): 25-34.

Figures/Tables 8

Fig.1

Table 1

Table 2

Main fields of data table after trajectory splitting"

轨迹点	车辆ID	经度	纬度	时间戳	网格编号	时间帧编号
p₁	C₁	Lng₁	Lat₁	t₁	G（5，5）	$T i, 1$
p₂	C₁	Lng₂	Lat₂	t₂	G（5，5）	$T i, 1$
…	…	…	…	…	…	…

Table 2

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

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