Forecasting urban traffic congestion conduction based on spatiotemporal association rule mining

ZHOU Huiyu, LI Ruimin, HUANG Anqiang, WANG Qiyan, HE Zefang, WANG Shouyang

Systems Engineering - Theory & Practice ›› 2022, Vol. 42 ›› Issue (8) : 2210-2224.

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Systems Engineering - Theory & Practice ›› 2022, Vol. 42 ›› Issue (8) : 2210-2224. DOI: 10.12011/SETP2020-2752

Forecasting urban traffic congestion conduction based on spatiotemporal association rule mining

  • ZHOU Huiyu1, LI Ruimin1, HUANG Anqiang1, WANG Qiyan1, HE Zefang2, WANG Shouyang3
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Abstract

Accurate prediction of urban traffic congestion is a significant research in the field of intelligent transportation. In order to more accurately predict the road congestion and mine congestion transmission rules, this paper proposes an urban traffic congestion conduction forecasting model based on spatiotemporal association rules. The model first constructs a new spatiotemporal association rule mining algorithm based on genetic network programming (GNP) to identify traffic congestion rules and forms a traffic congestion rule database. On this basis, the spatiotemporal conduction prediction of traffic congestion is generated. Finally, for the validation purpose, the empirical study based on the data of Beijing's traffic is carried out, and the results show high performance of the proposed model. This model breaks through the technical path of "prediction of traffic first, then analysis of state", and uses traffic congestion status as the direct research object, reveals the temporal and spatial dynamic transmission rules of traffic congestion. Thus, it can support urban transportation authorities to take more systematic response measures in advance to improve the forward-looking and dynamic handling capabilities of traffic congestion.

Key words

traffic congestion forecasting / genetic network programming (GNP) / spatiotemporal association rules / urban traffic / data mining

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ZHOU Huiyu , LI Ruimin , HUANG Anqiang , WANG Qiyan , HE Zefang , WANG Shouyang. Forecasting urban traffic congestion conduction based on spatiotemporal association rule mining. Systems Engineering - Theory & Practice, 2022, 42(8): 2210-2224 https://doi.org/10.12011/SETP2020-2752

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Funding

National Natural Science Foundation of China (71961137005-2);Beijing Social Science Foundation (21GLB033,B15HZ00210);Beijing Intelligent Logistics System Collaborative Innovation Center (BILSCIC-2019KF-24)
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