Analysing the Relationship Between Traffic Flows, Road Infrastructure, and Car Crashes Data: An Approach Based on Spatiotemporal Point Patterns on Linear Networks

Road accidents represent a concern for modern societies, especially in poor and developing countries. In this paper, we develop a road safety model assuming that the car crashes recorded in Milan (Italy) during 2019 can be appropriately modelled as a realisation of a spatio-temporal point process on a linear network. We adopt a separable first-order intensity function with spatial and temporal components. The temporal dimension is estimated semi-parametrically using an additive Poisson regression model. The spatial dimension is estimated semi-parametrically considering a fixed effect related to the road class and a b-spline transformation of two potentially relevant space-varying covariates, namely the traffic flows and the distance to the closest road sign. This approach permits us to analyse traffic accidents at a very granular spatial scale, hence avoiding potential biases due to data aggregation.