Highlights Collision and vehicle types are two factors that affect accident severity. A directed linear network is used for the analysis of differential risk for several accident typologies. A methodology is proposed for the accurate detection of differential risk hotspots. A Monte Carlo approach is taken to determine the statistical significance of each hotspot. The methodology is tested with several collision and vehicle types, providing remarkable differences across space.

    Abstract Traffic accidents can take place in very different ways and involve a substantially distinct number and types of vehicles. Thus, it is of interest to know which parts of a road structure present an overrepresentation of a specific type of traffic accident, specially for some typologies of collisions and vehicles that tend to trigger more severe consequences for the users being involved. In this study, a spatial approach is followed to estimate the risk that different types of collisions and vehicles present in the central area of Valencia (Spain), considering the accidents observed in this city during the period 2014–2017. A directed spatial linear network representing the non-pedestrian road structure of the area of interest was employed to guarantee an accurate analysis of the point pattern. A kernel density estimation technique was used to approximate the probability of risk along the network for each collision and vehicle type. A procedure based on these estimates and the sample size locally available within the network was designed and tested to determine a set of differential risk hotspots for each typology of accident considered. A Monte Carlo based simulation process was then defined to assess the statistical significance of each of the differential risk hotspots found, allowing the elaboration of rankings of importance and the possible rejection of the least significant ones.


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    Title :

    Identification of differential risk hotspots for collision and vehicle type in a directed linear network




    Publication date :

    2019-08-19




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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