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Sustainability 2017, 9(4), 533; doi:10.3390/su9040533

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

1
MOE Key Laboratory for Urban Transportation Complex System Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
2
Department of Civil, Environmental, and Infrastructure Engineering, Volgenau School of Engineering, George Mason University, Fairfax, VA 22030, USA
3
Center for Advanced Transportation System Simulation, Department of Civil Environment Construction Engineering, University of Central Florida, Orlando, FL 32816, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Bhavik Bakshi
Received: 6 March 2017 / Revised: 24 March 2017 / Accepted: 28 March 2017 / Published: 31 March 2017
(This article belongs to the Section Sustainable Engineering and Science)
View Full-Text   |   Download PDF [10331 KB, uploaded 4 April 2017]   |  

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. View Full-Text
Keywords: recurrent traffic congestion; traffic grid modeling; density-based spatial clustering; GPS data recurrent traffic congestion; traffic grid modeling; density-based spatial clustering; GPS data
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Liu, Y.; Yan, X.; Wang, Y.; Yang, Z.; Wu, J. Grid Mapping for Spatial Pattern Analyses of Recurrent Urban Traffic Congestion Based on Taxi GPS Sensing Data. Sustainability 2017, 9, 533.

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