An expert system to discover key congestion points for urban traffic
An expert system to discover key congestion points for urban traffic
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发现城市交通关键拥堵点的专家系统
DOI：
10.1016/j.eswa.2020.113544
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发表时间：
2020
影响因子：
8.5
通讯作者：
Dong Yuanxiang
中科院分区：
文献类型：
--
作者：
Gong Ke;Zhang Li;Ni Du;Li Huamin;Xu Maozeng;Wang Yong;Dong Yuanxiang
在交通拥堵中定期发现关键拥堵点是一个关键问题。它支持道路管理人员了解情况，并经济有效地排除拥堵。然而，城市规模和同步的交通数据给这类分析带来了困难。随着数据科学的最新发展，由不断增长的数字地图应用程序生成的交通状况数据的可用性使得这个问题变得可行。因此，我们首先提出了一个数字地图数据驱动的专家系统来发现和测量城市规模的关键拥堵点。它基于最先进的特征选择方法BSSReduce（基于双射软集的特征选择）。本文以百度地图重庆和北京数据为例进行研究。结果表明，我们所提出的方法可以帮助道路管理人员从每月超过10，000和50，000个城市道路点中识别出75和300个关键拥堵点。可视化的结果以及显著性测量为道路管理者提供了一个专家系统，可以快速排除拥堵，并为未来的交通管理制定新的解决方案。
Discovering key congestion points periodically in traffic jams is a critical issue. It supports road managers to make sense of the situations, and rule out the congestion economically and efficiently. However, city-scale and synchronal traffic data bring hardships for such kind of analyses. With recent developments in data science, the availability of traffic conditions data generated by the rising digital map applications makes this issue feasible. Therefore, we firstly propose a digital map data-driven expert system to discover and measure the city-scale key congestion points. It is based on a state-of-the-art feature selection method, BSSReduce (Bijective soft set based feature selection). Data from Baidu Map for Chongqing and Beijing are collected as a case to conduct this study. The results indicate that our proposed method helps the road managers recognize 75 and 300 key congestion points from over 10,000 and 50,000 points of the urban roads each month. The visualized results, as well as the significance measurements, provide road managers an expert system to quickly rule out congestion and work out new solutions to future traffic management.