Grid Mapping For Spatial Pattern Analyses Of Recurrent Urban Traffic Congestion Based On Taxi Gps Sensing Data

Keywords

Density-based spatial clustering; GPS data; Recurrent traffic congestion; Traffic grid modeling

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.

Publication Date

3-31-2017

Publication Title

Sustainability (Switzerland)

Volume

9

Issue

4

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.3390/su9040533

Socpus ID

85017334409 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85017334409

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