A combined short- and medium-term traffic flow prediction method for proactive traffic control at expressway toll stations

Author:

Xue Bingbing1,Wang Xu1ORCID,Han Zishuang2

Affiliation:

1. School of Qilu Transportation, Shandong University, China

2. Graduate School of Engineering, Kyushu University, Japan

Abstract

Traffic flow prediction can support proactive control strategies to alleviate queues and delays at expressway toll stations. The prediction should be enough accurate and advanced that leaves sufficient preparation time to implement control measures for upcoming congestions. The accuracy of prediction and the lead time for accurate prediction are contradictory. To balance both aspects, a control-oriented combined prediction method was proposed in this study. This method automatically tuned prediction horizons based on predicted level of congestion. It used long short-term memory (LSTM) neural networks to learn the spatial–temporal characteristics of historical traffic flow and predict short-term and medium-term traffic flow at toll stations. Then, the level of congestion was determined for graded congestion warning and dynamic tuning of prediction horizons. To validate the proposed method, the Ganggou toll station was selected as the study site. The proposed integrated model can facilitate reliable proactive control measures at expressway toll stations.

Funder

National Key Research and Development Program of China

Transportation Technology Plan of Shandong

Publisher

Canadian Science Publishing

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