Exploring the attributes relevant to accidents between vehicles and unprotected road users, taking Stockholm as an example

Abstract: Traffic accidents is one of the major causes of fatalities and economic loss around the world. Thus, there is an urgent need for a better understanding about the factors that contribute to accidents so that the accidents can be prevented in the future. The research objective of this thesis is to analyze the traffic accidents between vehicles and unprotected road users (pedestrians and bicycles) in Stockholm, finding spatial distribution patterns, related attributes and examining relationships between accidents and a number of vehicle flows. The data is first analyzed with general statistical analysis to examine the basic characteristics. There is no apparent trend of change among the number of accidents per year, while the numbers of accidents happening from May to October is higher than the rest of the year except for July due to less traffic during holiday period. Most traffic accidents occur in overcast weather, on a dry road surface, or during the day. In the spatial analysis part of the thesis, Global Moran’s I is used to detect whether there is an attribute-related spatial distribution pattern. Hot spot analysis is then applied on the clustered attributes to find significant hot and cold spots over the study area. The conclusions are that road surface conditions and occurrence time during day/night are two related factors that influence traffic accidents while weather is not considered a related attribute since the accidents distribute randomly in terms of weather, of which it is difficult to obtain temporally-aligned, detailed local information for further analysis. Different parameters are selected and discussed during the process. When calculating the distance between two accidents in traffic accident analysis, Manhattan distance is more appropriate than Euclidean distance since traffic accidents are restricted to the road network. The distance band determines scales of analysis tools, with 50 meters on an intersection and 500 meters for a larger region in Stockholm. Most hot spots arise at intersections and roundabouts where different types of traffic flows meet each other. The result of the relationships between traffic accidents and different types of vehicle flows shows that the correlation coefficients between number of traffic accidents and traffic flows are low, meaning that there is no obvious correlation between them, which is also proved by the scatter plots. Poisson regression model is applied on the traffic accident data. As a result, high-risk and low-risk areas in Stockholm are pointed out. Some are consistent with the hot-spot analysis result.