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Sustainability 2015, 7(3), 2662-2677; doi:10.3390/su7032662

A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China

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,
1,2
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1,2,* , 1,2
and
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1
School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079,China
2
Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editor: Marc A. Rosen
Received: 6 November 2014 / Revised: 16 February 2015 / Accepted: 17 February 2015 / Published: 4 March 2015
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Abstract

Research on spatial cluster detection of traffic crash (TC) at the city level plays an essential role in safety improvement and urban development. This study aimed to detect spatial cluster pattern and identify riskier road segments (RRSs) of TC constrained by network with a two-step integrated method, called NKDE-GLINCS combining density estimation and spatial autocorrelation. The first step is novel and involves in spreading TC count to a density surface using Network-constrained Kernel Density Estimation (NKDE). The second step is the process of calculating local indicators of spatial association (LISA) using Network-constrained Getis-Ord Gi* (GLINCS). GLINCS takes the smoothed TC density as input value to identify locations of road segments with high risk. This method was tested using the TC data in 2007 in Wuhan, China. The results demonstrated that the method was valid to delineate TC cluster and identify risk road segments. Besides, it was more effective compared with traditional GLINCS using TC counting as input. Moreover, the top 20 road segments with high-high TC density at the significance level of 0.1 were listed. These results can promote a better identification of RRS, which is valuable in the pursuit of improving transit safety and sustainability in urban road network. Further research should address spatial-temporal analysis and TC factors exploration. View Full-Text
Keywords: network-constrained; spatial cluster pattern; traffic crash; Kernel Density Estimation; Getis-Ord Gi*; riskier road segments network-constrained; spatial cluster pattern; traffic crash; Kernel Density Estimation; Getis-Ord Gi*; riskier road segments
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Nie, K.; Wang, Z.; Du, Q.; Ren, F.; Tian, Q. A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China. Sustainability 2015, 7, 2662-2677.

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