A tool for long-term predictions of road safety based on travel demand modeling and network characteristics

Road safety is one of the major issues regarding transportation and public health. The knowledge of the number of crashes and their spatial and temporal distribution is relevant to develop countermeasures and assess their costs and benefits. However, the prediction of crashes on entire networks requires of the availability of travel demand data on every road. Generally, it is needed to extrapolate traffic counts or travel surveys to the entire network. In this paper, crash prediction models are estimated, based on the outputs of an agent-based travel demand model. Only aggregated crash counts are used to estimate the crash prediction model. The results characterize the spatial distribution of crashes and fatalities by road type and analysis zone. The proposed approach is applied to the metropolitan area of Munich, which includes 28 counties, 444 municipalities and a population of 4.4 Million. The total amount of crashes with casualties or fatalities was 19,714 in 2011.     «

Road safety is one of the major issues regarding transportation and public health. The knowledge of the number of crashes and their spatial and temporal distribution is relevant to develop countermeasures and assess their costs and benefits. However, the prediction of crashes on entire networks requires of the availability of travel demand data on every road. Generally, it is needed to extrapolate traffic counts or travel surveys to the entire network. In this paper, crash prediction models are...     »