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

Ke Nie, Zhensheng Wang, Qingyun Du, Fu Ren and Qin Tian
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Ke Nie: School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079,China
Zhensheng Wang: School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079,China
Qingyun Du: School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079,China
Fu Ren: School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079,China
Qin Tian: School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079,China

Sustainability, 2015, vol. 7, issue 3, 1-16

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.

Keywords: network-constrained; spatial cluster pattern; traffic crash; Kernel Density Estimation; Getis-Ord Gi*; riskier road segments (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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