Predicting Short-Term Urban Traffics Based on Causality Analysis Graph
Wang Jie1,Gao Yuan2(),Zhang Lei1,Ma Liwen3,Feng Jun1
1School of Information Science & Technology, Northwest University, Xi’an 710127, China 2School of Economics & Management, Northwest University, Xi’an 710127, China 3Network and Data Center, Northwest University, Xi’an 710127, China
[Objective] This paper examines the complex spatial interaction mechanism between regions, aiming to effectively predict short-term traffic flow. [Methods] Based on the graph neural network, we proposed a new predictive model, which integrated the regional functional similarity matrix and the causality matrix. Then, we developed a training strategy of “Mining traffic time series causal relationship → Extracting Spatio-temporal features → Predicting traffic flows”. Third, we predicted the traffic flows by capturing the spatio-temporal dependence characteristics of regional traffic. [Results] We tested the proposed model with Didi Chuxing data set from Chengdu. Compared with the optimal baseline model, the RMSE and MAE values were reduced by 3.098% and 4.783%, respectively. [Conclusions] The causal diagram for traffic sequence can simultaneously integrate the features of spatial distance relationships, road connectivity, and function similarities. With the help of causal relationships, the proposed model could effectively predict regional traffic flows.
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