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

Abstract

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. OPEN ACCESSSustainability 2015, 7 2663

Analysis of the Spatial Variation of Network-Constrained Phenomena Represented by a Link Attribute Using a Hierarchical Bayesian Model

“…Therefore, many researchers have made significant progress by extending the planar methods to network-based methods. For instance, two popular event-based methods, planar kernel density estimation (KDE) and planar K-function methods have been extended to the network KDE [4,6,14,15] and the network K-function [4,5,11,24] methods, respectively. Using the link-attribute approach, an exploratory methodology named local indicators of network-constrained clusters (LINCS) was introduced to detect the local scale clustering of network events [16].…”

“…Like the local I statistic, the local G statistic can be applied to analyse network-constrained phenomena by modifying the weight matrix [13,15]. The local I statistic aims to determine the autocorrelation between a region and its neighbours; however, the local G statistic measures the concentration of attributes of a variable around a region [36].…”

Analyzing Risk Factors for Fatality in Urban Traffic Crashes: A Case Study of Wuhan, China

“…According to the National Bureau of Statistics of China, the death rate per ten thousand vehicles declined to 2.22 in 2014, a decrease of 5.1% over 2013 [1]. However, road traffic injuries exceeded any other causes of injury death and took first place in China [2,3]. Wuhan is the capital of Hubei province, which is the most populous city in Central China.…”

An Integrated Spatial Clustering Analysis Method for Identifying Urban Fire Risk Locations in a Network-Constrained Environment: A Case Study in Nanjing, China

“…Results in Experiments 3 and 4 denoted that the ILINCS method failed to identify the H-H road segments, while the NKDE-ILINCS method using kernel density as input in Experiments 1 and 2 could disclose and identify H-H road segments successfully. Moreover, Experiment 1 can identify the H-H road segments without considering more details to keep segments in a coherent and valid length [41]. In this paper, we recommend using the combination of H-H road segments and z-score to detect fire higher-risk locations.…”