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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (11): 111-125    DOI: 10.11925/infotech.2096-3467.2022.0090
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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
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Abstract  

[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.
Key words： Short-Term Traffic Flow Prediction      Traffic Time Series Cause and Effect Diagram      Spatio-Temporal Data      Graph Neural Network     
Received: 30 January 2022      Published: 13 January 2023
ZTFLH:  U491  
Fund:National Social Science Fund of China(20BTJ047)
Corresponding Authors: Gao Yuan     E-mail: Yuangao@nwu.edu.cn
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Jie Wang
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Cite this article:

Wang Jie,Gao Yuan,Zhang Lei,Ma Liwen,Feng Jun. Predicting Short-Term Urban Traffics Based on Causality Analysis Graph. Data Analysis and Knowledge Discovery, 2022, 6(11): 111-125.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0090     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I11/111

Spatial Dependence of Urban Traffic Flow Between Regions
Causal Effects in Traffic of Urban Regional
Traffic Time-Series Directed Acyclic Graph
ST-CAG Framework Model
Graph Convolution Neural Network
Gated Recurrent Unit
方法 时间特征 空间特征 因果特征
ARIMA √
SARIMA √
SVR √
LSTM √
GRU √
ConvLSTM √ √
DCRNN √ √
T-GCN √ √
ST-CAG（本文） √ √ √
Comparison of Characteristics Considered by Various Models
The Visualization Results of Regions with Causal Relationship
Comparison Between Target Time Series and Its Cause / Non-cause Time Series
区域 v g r e e n v y e l l o w
v 227 0.847 0.642
v 388 0.816 0.922
Functional Similarity Between Different Regions
Difference Statistics for the Distribution of Each Type of POI Between Regions with Causality
Adjacency Matrix Heatmap Corresponding to TCG Generated under Different Threshold Combinations
方法 5分钟 10分钟 15分钟
MAE RMSE MAE RMSE MAE RMSE
ARIMA 29.105 32.827 29.301 32.982 29.511 33.010
SARIMA 28.235 32.297 28.239 32.298 28.240 32.298
SVR 12.079 17.195 12.582 17.524 12.915 17.957
LSTM 11.558 15.932 11.984 16.532 12.289 16.991
GRU 11.165 15.915 11.581 16.447 11.805 16.862
ConvLSTM 11.359 16.583 11.735 16.905 11.955 16.997
DCRNN 11.263 16.465 11.619 16.617 11.892 16.918
T-GCN 13.525 18.578 13.531 18.602 13.551 18.630
ST-CAG（本文） 10.631 15.422 10.955 15.951 11.361 16.419
Accuracy Between ST-CAG Model and Other Models in Different Prediction Time Lengths
Ablation Experiment
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