sensors Article A Hybrid Method for Predicting Traffic Congestion during Peak Hours in the Subway System of Shenzhen Zhenwei Luo 1, Yu Zhang 1, Lin Li 1,2,* , Biao He 3, Chengming Li 4, Haihong Zhu 1,2,*, Wei Wang 1, Shen Ying 1,2 and Yuliang Xi 1 1 School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China;
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[email protected] (H.Z.); Tel.: +86-27-6877-8879 (L.L. & H.Z.) Received: 11 October 2019; Accepted: 23 December 2019; Published: 25 December 2019 Abstract: Traffic congestion, especially during peak hours, has become a challenge for transportation systems in many metropolitan areas, and such congestion causes delays and negative effects for passengers. Many studies have examined the prediction of congestion; however, these studies focus mainly on road traffic, and subway transit, which is the main form of transportation in densely populated cities, such as Tokyo, Paris, and Beijing and Shenzhen in China, has seldom been examined. This study takes Shenzhen as a case study for predicting congestion in a subway system during peak hours and proposes a hybrid method that combines a static traffic assignment model with an agent-based dynamic traffic simulation model to estimate recurrent congestion in this subway system.