A Hybrid Method for Predicting Traffic Congestion during Peak Hours in the Subway System of Shenzhen

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. The homes and work places of the residents in this city are collected and taken to represent the traffic demand for the subway system of Shenzhen. An origin-destination (OD) matrix derived from the data is used as an input in this method of predicting traffic, and the traffic congestion is presented in simulations. To evaluate the predictions, data on the congestion condition of subway segments that are released daily by the Shenzhen metro operation microblog are used as a reference, and a comparative analysis indicates the appropriateness of the proposed method. This study could be taken as an example for similar studies that model subway traffic in other cities.

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• http://dx.doi.org/10.3390/s20010150 under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.3390/s20010150 under the license http://creativecommons.org/licenses/by/3.0/

• http://europepmc.org/articles/PMC6982792 under the license https://creativecommons.org/licenses/by/4.0

• https://www.mdpi.com/1424-8220/20/1/150/pdf

• https://dblp.uni-trier.de/db/journals/sensors/sensors20.html#LuoZLHLZWYX20,

https://www.mdpi.com/1424-8220/20/1/150/pdf,

https://www.mdpi.com/1424-8220/20/1/150,

https://www.ncbi.nlm.nih.gov/pubmed/31881726,

https://doi.org/10.3390/s20010150,

https://academic.microsoft.com/#/detail/2998336146 under the license cc-by

• https://www.mdpi.com/1424-8220/20/1/150,

https://doaj.org/toc/1424-8220

• https://www.mdpi.com/1424-8220/20/1/150/pdf,

http://dx.doi.org/10.3390/s20010150

under the license https://creativecommons.org/licenses/by/4.0/