Community detection in large scale congested urban road networks.

Seyed Arman Haghbayan,Nikolas Geroliminis,Meisam Akbarzadeh,Nan Zheng

doi:10.1371/journal.pone.0260201

Seyed Arman Haghbayan, Nikolas Geroliminis + Show 2 more

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https://doi.org/10.1371/journal.pone.0260201

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Abstract

Traffic congestion in large urban networks may take different shapes and propagates non-uniformly variations from day to day. Given the fact that congestion on a road segment is spatially correlated to adjacent roads and propagates spatiotemporally with finite speed, it is essential to describe the main pockets of congestion in a city with a small number of clusters. For example, the perimeter control with macroscopic fundamental diagrams is one of the effective traffic management tools. Perimeter control adjusts the inflow to pre-specified regions of a city through signal timing on the border of a region in order to optimize the traffic condition within the region. The precision of macroscopic fundamental diagrams depends on the homogeneity of traffic condition on road segments of the region. Hence, previous studies have defined the boundaries of the region under perimeter control subjected to the regional homogeneity. In this study, a cost-effective method is proposed for the mentioned problem that simultaneously considers homogeneity, contiguity and compactness of clusters and has a shorter computational time. Since it is necessary to control the cost and complexity of perimeter control in terms of the number of traffic signals, sparse parts of the network could be potential candidates for boundaries. Therefore, a community detection method (Infomap) is initially adopted and then those clusters are improved by refining the communities in relation to roads with the highest heterogeneity. The proposed method is applied to Shenzhen, China and San Francisco, USA and the outcomes are compared to previous studies. The results of comparison reveal that the proposed method is as effective as the best previous methods in detecting homogenous communities, but it outperforms them in contiguity. It is worth noting that this is the first method that guarantees the connectedness of clusters, which is a prerequisite of perimeter control.

Highlights

Since over a decade ago, Network or Macroscopic Fundamental Diagram (MFD) is recognized as a promising tool for monitoring vehicular traffic conditions and implementing control strategies with the goal of analyzing and alleviating congestion problems at network scale
This paper primarily aims to find the sub-regions of urban road networks satisfying the following five criteria: (a) internal homogeneity in terms of traffic density, (b) external heterogeneity with other sub-regions, (c) sparse connection to their neighbor sub-regions, (d) connectedness, and (e) computational efficiency of the method, in which the shorter running time would offer an advantage in adaptation of the perimeter control boundary to the real-time traffic situation
The proposed method was applied to the network of San Francisco, USA and Shenzhen, China. These networks were used to test the previous methods of community detection for the application of perimeter control based on MFD

Summary

Introduction

Since over a decade ago, Network or Macroscopic Fundamental Diagram (MFD) is recognized as a promising tool for monitoring vehicular traffic conditions and implementing control strategies with the goal of analyzing and alleviating congestion problems at network scale. Setting traffic density at an optimum value (i.e. critical density) would set the flow at its maximum which implies highest utilization of the capacity provided by the network. This control process is called perimeter control. Number of vehicles entering the region is controlled via tolls drivers have to pay to be permitted to enter the region. These methods have been extensively explained in several studies [2,3,4,5]

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