A detailed spatiotemporal analysis of traffic crash hotspots. (June 2019) - Bíl, Michal; Andrášik, Richard; Sedoník, Jiří
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. Highlights: We studied spatiotemporal behavior of hotspots identified using the KDE+. Crash data were analyzed using a 3-year time window with a one-day step. All the hotspots covered between 6.8% and 8.2% of the road network. Hotspots evolved over time, emerged or disappeared. Only 100 hotspots were stable over nine-year period (2010–2018).