Samband mellan halkolyckor och vädersituationer för fotgängare: En förstudie för att studera möjligheten till ett halkvarningssystem för fotgängare

Pedestrian fall injuries make up a substantial share of traffic-related injuries, with slipping on snow or ice being a common cause. Developing an effective slip-warning system for pedestrians requires a better understanding of how weather conditions relate to fall incidents. This study examines which weather situations most often coincide with high numbers of slip-related pedestrian injuries, and whether these situations also occurred on days without injuries. The four municipalities included are Umeå, Linköping, Gothenburg and Malmö. Fall injury data were collected from the national Strada registry for the winter seasons 2022/23 and 2023/24. Weather observations from SMHI included air temperature, precipitation and ground surface conditions, complemented by local sensor data from the BRIGHT network in Linköping. Sequence and cluster analysis were used to study weather conditions on the day of the accident and the four preceding days. No single weather situation clearly explains days with many slip accidents. However, certain patterns recur. Temperature fluctuations around freezing, as well as persistent sub-zero temperatures combined with precipitation or wet or snow-covered surfaces, frequently coincide with slip accidents. The patterns vary between municipalities. In Umeå, many accidents occur even during stable cold periods. Linköping shows more accidents after prolonged sub-zero conditions followed by precipitation. Gothenburg and Malmö, with milder climates, experience the most accidents during temperature shifts around freezing, especially when precipitation occurs near or below 0°C. The study also shows that SMHI’s official stations do not always capture local temperature variations. BRIGHT network in Linköping reveal differences of up to one degree, which may influence slip risk when temperatures hover around freezing. Overall, daily weather data is insufficient for accurate pedestrian slip warnings. More detailed hourly data, improved representation of local conditions and modelling covering the full study area are needed for an effective warning system.