Development of an accident prediction model for Italian freeways
| Autor: | Francesca La Torre, Valentina Branzi, Monica Meocci, Niccolo’ Tanzi, Andrea Paliotto, Lorenzo Domenichini |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
050210 logistics & transportation
Models Statistical Computer science 05 social sciences Accidents Traffic Public Health Environmental and Occupational Health Human Factors and Ergonomics Crash Residual Transport engineering Data set Italy Goodness of fit Calibration 0502 economics and business Data analysis Humans 0501 psychology and cognitive sciences Built Environment Safety Risk Reliability and Quality Highway Safety Manual Annual average daily traffic 050107 human factors Predictive modelling |
| Zdroj: | Accident Analysis & Prevention. 124:1-11 |
| ISSN: | 0001-4575 |
| Popis: | The roadway safety management process plays an important role in the national efforts for improving road safety along the Italian freeway network. In 2016, 8.3% of the overall Italian road deaths and 6.3% of the overall road injuries occurred along the 6700 km-long freeway network. Accident Prediction Models (APMs) represent one of the best tools to perform a road safety quantitative assessment. With the aim of providing the Italian freeway agencies with a tool that allows to deal with potential safety issues, this paper defines two APMs for single- and multiple-vehicle fatal-and-injury crashes to be applied on Italian rural freeway segments, based on jurisdictional specific Safety Performance Functions (SPFs) developed in the PRACT project. The proposed procedure is based on the Highway Safety Manual (HSM) approach, and it introduces a new methodology to transfer the HSM to European motorways. In order to improve the prediction accuracy, the proposed APMs consist in a jurisdictional specific base SPF, developed for the base data set as a function of Annual Average Daily Traffic (AADT) and segment length, combined with Crash Modification Factors (CMFs), in order to account for differences between each site and the base conditions. The full models are then calibrated based on the total number of accidents observed in the wide data set. For both full models (one for single-vehicle and one for multiple-vehicle crashes), the goodness of fit is evaluated in terms of chi square test, root mean square error, observed Vs predicted diagram and predicted Vs residual diagram. The results show a good aptitude of both models to describe the analysis data set. The proposed models represent a solid and reliable tool for practitioners to perform accident predictions along the Italian freeway network. |
| Databáze: | OpenAIRE |
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