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

机译：深圳地铁高峰期交通拥堵的混合预测方法

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摘要

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.
机译：交通拥堵，特别是在高峰时段，已经成为许多大城市地区交通系统的挑战，这种拥堵会导致交通延误和对乘客的不利影响。许多研究已经检查了拥塞的预测。但是，这些研究主要集中在道路交通和地铁运输上，而这是人口稠密城市（如中国东京，巴黎，北京和深圳）的主要交通方式，因此很少进行研究。本研究以深圳为例，预测高峰时段地铁系统的拥堵情况，并提出了一种混合方法，将静态交通分配模型与基于代理的动态交通仿真模型相结合，以估算该地铁系统中的经常性拥堵。收集并提取该城市居民的住房和工作地点，以代表深圳地铁系统的交通需求。在这种预测交通量的方法中，将从数据得出的起点-目的地（OD）矩阵用作输入，并且在模拟中显示了交通拥堵。为了评估预测结果，以深圳地铁运营微博每天发布的地铁路段拥堵状况数据为参考，比较分析表明了该方法的适当性。该研究可以作为对其他城市地铁交通进行建模的类似研究的示例。

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