To identify high-risk locations (hotspots) of road traffic crashes (RTC) in a redeveloping area of Shanghai, for formulating relevant countermeasures in similar areas. After geocoding the crash locations on electronic map, assessment of spatial clustering of accidents and hotspots spatial densities was conducted following Moran's I method, the kernel density estimation, the Ripley's K-function and the network-based kernel density estimation (NKDE). A total of 21,679 RTC incidents resulting in 24,147 victims were recorded from 2010 to 2012. RTCs tended to occur among male (81.8%) aged 20-49 years old (84.9%) riding buses (60.1%) or electric bikes (16.1%) in working time (15%). The network spatial analysis pinpointed the hotspots of RTC at the street level in the Songjiang new urban area, and more RTCs occurred at road intersections than on road segments. Findings from this research may help the authorities develop efficient programmes to target high-risk locations and specific vulnerable populations.
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| http://dx.doi.org/10.1080/17457300.2018.1431938 | DOI Listing |
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