Identifying the Key Nodes and Sections of Urban Roadway Network Based on GPS Trajectory Data

Jing Wang,Shichen Huang,Chunjiao Dong,Shuang Wang,Chunfu Shao,Bin Yu

doi:10.1155/2021/6682063

Jing Wang, Shichen Huang + Show 4 more

Open Access

https://doi.org/10.1155/2021/6682063

Copy DOI

Abstract

This paper proposes a novel approach to identify the key nodes and sections of the roadway network. The taxi-GPS trajectory data are regarded as mobile sensor to probe a large scale of urban traffic flows in real time. First, the urban primary roadway network model and dual roadway network model are developed, respectively, based on the weighted complex network. Second, an evaluation system of the key nodes and sections is developed from the aspects of dynamic traffic attributes and static topology. At the end, the taxi-GPS data collected in Xicheng District of Beijing, China, are analyzed. A comprehensive analysis of the spatial-temporal changes of the key nodes and sections is performed. Moreover, the repetition rate is used to evaluate the performance of the identification algorithm of key nodes and sections. The results show that the proposed method realizes the expression of topological structure and dynamic traffic attributes of the roadway network simultaneously, which is more practicable and effective in a large scale.

Highlights

An urban roadway network is composed of multiple intersections and roadway sections. e studies show that the importance of each intersection and roadway section in the urban roadway network is different, and large-scale congestion of the roadway network is often caused by the congestion of several key intersections and sections
We define two indexes, the level of traffic congestion and the grade of node, to build the node weight of the roadway network model, and for static topology, we construct three matrices based on node efficiency and shortest path
The weighted complex network is used to construct the roadway network model. en, the evaluation index system of key nodes and sections is developed from the perspectives of dynamic traffic attributes and roadway network topology

Summary

Introduction

An urban roadway network is composed of multiple intersections and roadway sections. e studies show that the importance of each intersection and roadway section in the urban roadway network is different, and large-scale congestion of the roadway network is often caused by the congestion of several key intersections and sections. Erefore, if the supporting and vulnerable nodes and sections can be identified, traffic planners and traffic managers can alleviate the traffic pressure by reasonably planning the topological structure of the urban roadway network. Based on the taxi-GPS data, Kong et al [2] constructed a method combining support vector machine and the fuzzy comprehensive evaluation model, which realized the identification and prediction of traffic congestion. Feng et al [3] proposed an identification method of critical roads based on the combination of GPS trajectory data and the directed weighted complex network. Taxi-GPS trajectory data are high-quality data for the study of urban traffic problems. The paper proposes a new method using taxi-GPS trajectory data to identify the key nodes and sections of the roadway.

Literature Review

Methodologies

Conclusion