Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach

https://doi.org/10.1016/j.physa.2018.02.062Get rights and content

Highlights

  • We propose a data-driven approach to measure the heterogeneous traffic correlations.

  • We construct a traffic-enhanced dual-graph to represent the road system.

  • We detect the traffic-correlated road segment clusters with the Infomap algorithm.

  • Our proposed approach can reveal the road traffic correlation better than the others.

Abstract

A better characterization of the traffic influence among urban roads is crucial for traffic control and traffic forecasting. The existence of spatial heterogeneity imposes great influence on modeling the extent and degree of road traffic correlation, which is usually neglected by the traditional distance based method. In this paper, we propose a traffic-enhanced community detection approach to spatially reveal the traffic correlation in city road networks. First, the road network is modeled as a traffic-enhanced dual graph with the closeness between two road segments determined not only by their topological connection, but also by the traffic correlation between them. Then a flow-based community detection algorithm called Infomap is utilized to identify the road segment clusters. Evaluated by Moran’s I, Calinski–Harabaz Index and the traffic interpolation application, we find that compared to the distance based method and the community based method, our proposed traffic-enhanced community based method behaves better in capturing the extent of traffic relevance as both the topological structure of the road network and the traffic correlations among urban roads are considered. It can be used in more traffic-related applications, such as traffic forecasting, traffic control and guidance.

Introduction

Modeling the spatial interaction between components of urban road systems is essential for understanding traffic patterns. Urban road traffic has inherent spatial co-variations. Traffic conditions detected at different locations within a certain distance are often correlated [[1], [2], [3], [4], [5]]. A good characterization of this spatial co-variation can be used for better understanding urban traffic rhythm from a spatial perspective [6], or for improving traffic-related applications, such as traffic interpolation and forecasting [[7], [8], [9], [10], [11], [12]]. The fundamental issue of spatial interaction analysis is the determination of spatial neighbors: those elements surrounding a given element that could be considered to influence the element [13].

In previous studies, spatial neighborhoods are usually defined by distance. For instance, Kamarianakis and Prastacos [7] used Euclidean distance in geographical space to model the spatial correlation of traffic flow. Min et al. [9] used spatial neighbors within a defined topological distance in a city road network to improve traffic forecasting. Zou et al. [11] also used topological distance to determine neighboring roads, hence to improve the interpolation of traffic data, while Ding et al. [10], Min and Wynter [3] determined spatial neighbors by time-dependent route distance. However, distance based models usually cannot depict the urban traffic well due to the existence of spatial heterogeneity, which is resulted from the uneven urban structures and travel demands.

In urban road systems, some roads carry more traffic than others, and thus have higher impacts on their neighbors [14]. This leads to two types of spatial heterogeneity in city traffic. First, from a global perspective, the influence of road traffic varies at different locations. This may lead to difficult definition of traffic-relevant neighbors. Second, from a local perspective, the influence of road traffic is anisotropic where traffic flow on a road segment will not spread evenly to all downstream road segments, but instead concentrate in certain directions [15]. Cheng et al. [4] explored dynamic space–time autocorrelation on journey time data in London and revealed that the spatial heterogeneity are due to the variation of the level of correlation between individual road links. Furthermore, the size of the spatial neighborhood is time dependent [12].

Community detection is an algorithm to partition all the nodes in a network into different clusters. Nodes are densely connected within the same clusters and sparsely connected between different clusters [[16], [17], [18], [19], [20]]. In the case of road network with road segment as node and the connection between road segments as edge, nodes within the same clusters are densely connected, which may also be interacted strongly in traffic.

Therefore, in this paper, we propose a new approach called “traffic-enhanced community detection” to extract the traffic-correlated road segment clusters (i.e., spatial neighborhood) by considering both the topological connections/distances and the traffic correlations among road segments. First, the urban road network is modeled as a dual graph where the road segments represent nodes, the connections among road segments represent edges, and the weights on the edges represent the traffic correlation of the road segments. Second, the traffic-correlated road segments are gathered together through the Infomap community detection algorithm. The Mutual information index is used to evaluate the robustness of the community detection results, while the Moran’s I and Calinski–Harabaz Index are used to verify the approach’s capacity to uncover the heterogeneous traffic relevance on the road system.

The remainder of the paper is organized as follows: Section 2 introduces traffic-enhanced community detection method in detail. Section 3 conducts a series of experiments using mass traffic data to evaluate the robustness and effectiveness of the identified traffic-related road segment clusters. Section 4 is devoted to discussions. The conclusion is drawn in Section 5.

Section snippets

Modeling urban road system

Traditionally, an urban road system is represented as a network where the interactions are mapped as nodes and road segments are mapped as edges [21]. Such a plain representation is called “primal graph”, which is often used to describe the spatial configuration of the road segments and interactions. However, a “dual-graph” with road segments represented as nodes and the connections among road segments represented as edges is more suitable to model the relationships among road segments, which

Data

The road system of downtown Beijing, China (as shown in Fig. 3) is used in this case study. It contains 4616 road segments, including expressways, arterial streets, and collector streets, which are rendered by red, orange and green respectively.

Vehicular speeds on road segments are obtained from more than 50,000 GPS-equipped taxis at 5-min time intervals, from April 1, 2012 to June 30, 2012. Since the traffic conditions are markedly different on workdays and weekends, two datasets are built

Discussion

In this paper, we propose a novel approach to extract the traffic-related road segment clusters by conducting the Infomap community detection algorithm on the traffic-enhanced dual graph. The highlights of this paper can be summarized as follows.

(1) A traffic-enhanced topological dual-graph is constructed to represent the road system, which can well describe the topological connections and traffic correlations among road segments at the same time.

(2) The Infomap community detection algorithm is

Conclusions

Identifying and quantifying the traffic correlations within road systems is essential for both traffic management and effective vehicle navigation. In this paper, we have proposed a novel approach to measure the traffic correlations within city road systems. It uses a traffic-enhanced topological dual-graph to represent the road network, and then divides a road network into closely connected and strongly traffic correlated road segment clusters. It is argued the data-driven method can reveal

Acknowledgments

This research is supported by the National Natural Science Foundation of China (Grant No. 41631177), Key Project of the Chinese Academy of Sciences, China (Grant No. ZDRW-ZS-2016-6-3), and the National Key Research and Development Program, China (Grant No. 2016YFB0502104). We also thank the anonymous referees for their helpful comments and suggestions.

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