A clustering based traffic flow prediction method with dynamic spatiotemporal correlation analysis

There are significant spatiotemporal correlations among the traffic flows of neighboring road sections in the road network. Correctly identifying such correlations makes an essential contribution for improving the accuracy of traffic flow prediction. Many efforts have been made by several researcher...

Full description

Saved in:
Bibliographic Details
Published in:Transportation (Dordrecht) 2022-06, Vol.49 (3), p.951-988
Main Authors: Ryu, Unsok, Wang, Jian, Pak, Unjin, Kwak, Sonil, Ri, Kwangchol, Jang, Junhyok, Sok, Kyongjin
Format: Article
Language:English
Subjects:
Clustering
Correlation analysis
Economic Geography
Economics
Economics and Finance
Engineering Economics
Heterogeneity
Innovation/Technology Management
Logistics
Marketing
Matrices
Organization
Prediction models
Regional/Spatial Science
Roads
Roads & highways
Traffic
Traffic flow
Traffic information
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:There are significant spatiotemporal correlations among the traffic flows of neighboring road sections in the road network. Correctly identifying such correlations makes an essential contribution for improving the accuracy of traffic flow prediction. Many efforts have been made by several researchers to solve this issue, but they assume that the spatiotemporal correlations among traffic flows are stationary in both time and space, i.e., the degrees to which traffic flows affect each other are fixed. In this study, we propose a clustering based traffic flow prediction method that considers the dynamic nature of spatiotemporal correlations. In order to express the short-term dependence between the target road section and neighboring ones, the spatiotemporal correlation matrices are introduced. The historical traffic data are divided into several clusters according to the similarity between spatiotemporal correlation matrices. The spatiotemporal correlation analysis and the predictor selection based on the mutual information are performed in each cluster, and the multiple prediction models are trained separately. A prediction model corresponding to the cluster to which the current traffic pattern belongs is selected to output the prediction result. Experimental results on real traffic data show that the proposed method achieves good prediction accuracy by distinguishing the heterogeneity of spatiotemporal correlations among the traffic flows.
ISSN:0049-4488
1572-9435
DOI:10.1007/s11116-021-10200-9