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Journal Article

Citation

Bíl M, Andrášik R, Sedoník J. Appl. Geogr. 2019; 107: 82-90.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.apgeog.2019.04.008

PMID

unavailable

Abstract

A number of traffic crash databases at present contain the precise positions and dates of these events. This feature allows for detailed spatiotemporal analysis of traffic crash patterns. We applied a clustering method for identification of traffic crash hotspots to the rural parts of primary roads in the Czech road network (3,933 km) where 55,296 traffic crashes occurred over 2010 - 2018. The data were analyzed using a 3-year time window which moved forward with a one-day step as an elementary temporal resolution. The spatiotemporal behavior of hotspots could therefore be analyzed in great detail. All the identified hotspots, during the monitored nine-year period, covered between 6.8% and 8.2% of the entire road network length in question. The percentage of traffic crashes within the hotspots remained stable over time at approximately 50%. Three elementary types of hotspots were identified when analyzing spatiotemporal crash patterns: hotspot emergence, stability and disappearance. Only 100 hotspots were stable (remained in approximately the same position) over the entire nine-year period. This approach can be applied to any traffic-crash time series when the precise positions and date of crashes are available.


Language: en

Keywords

Hotspot; KDE+; Kernel density estimation; Road network; Spatiotemporal analysis; Traffic crash

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