Estimating an Injury Crash Rate Prediction Model based on severity levels evaluation: the case study of single-vehicle run-off-road crashes on rural context

摘要

In general in case of crash situations the quality of collected data is very limited and several information are\udusually unreliable. Thus it is recognised that a significant effort is required in order to improve the quality of the\udcrash prediction models moreover a crucial role is played by the identification of the factors influencing the crashes\udoccurrence and the levels of severity estimation. In this paper two injury crash rate prediction models related to\udsingle-vehicle run-off-road crashes type are calibrated and in particular significant attributes estimated are identified\udnot only with roadway geometric characteristics and surface conditions, but also with gender/number-of-drivers. To\udthis aim a survey of injury crashes on two-lane rural roads collected in the Southern Italy was considered and\udanalysed. Finally before the calibration step, a preliminary analysis of the data was provided through the estimation\udof the levels of severity by multinomial logit; in fact by this model only segments with highest values of severity are\udidentified and involved in the calibration procedure.