Grid Mapping for Spatial Pattern Analyses of Recurrent Urban Traffic Congestion Based on Taxi GPS Sensing Data

Yang Liu, Xuedong Yan, Yun Wang, Zhuo Yang and Jiawei Wu
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Yang Liu: MOE Key Laboratory for Urban Transportation Complex System Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Xuedong Yan: MOE Key Laboratory for Urban Transportation Complex System Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Yun Wang: MOE Key Laboratory for Urban Transportation Complex System Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Zhuo Yang: Department of Civil, Environmental, and Infrastructure Engineering, Volgenau School of Engineering, George Mason University, Fairfax, VA 22030, USA
Jiawei Wu: Center for Advanced Transportation System Simulation, Department of Civil Environment Construction Engineering, University of Central Florida, Orlando, FL 32816, USA

Sustainability, 2017, vol. 9, issue 4, 1-15

Abstract: Traffic congestion is one of the most serious problems that impact urban transportation efficiency, especially in big cities. Identifying traffic congestion locations and occurring patterns is a prerequisite for urban transportation managers in order to take proper countermeasures for mitigating traffic congestion. In this study, the historical GPS sensing data of about 12,000 taxi floating cars in Beijing were used for pattern analyses of recurrent traffic congestion based on the grid mapping method. Through the use of ArcGIS software, 2D and 3D maps of the road network congestion were generated for traffic congestion pattern visualization. The study results showed that three types of traffic congestion patterns were identified, namely: point type, stemming from insufficient capacities at the nodes of the road network; line type, caused by high traffic demand or bottleneck issues in the road segments; and region type, resulting from multiple high-demand expressways merging and connecting to each other. The study illustrated that the proposed method would be effective for discovering traffic congestion locations and patterns and helpful for decision makers to take corresponding traffic engineering countermeasures in order to relieve the urban traffic congestion issues.

Keywords: recurrent traffic congestion; traffic grid modeling; density-based spatial clustering; GPS data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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