In the field of road safety, crashes involving physical injuries typically occur on roadways, which constrain the events to lie along a linear network. Substantial research efforts have been devoted to the development of methods for point patterns on linear networks. In one such model, we assume that crash coordinates are produced by a Poisson point process whose domain corresponds to edges in the road network. This talk focuses on the analysis of geo-localised accident data in the context of a smart city initiative launched by the City of Quebec (Canada) aiming to identify crash hotspots on the road network based on covariates derived from GPS data. Data originate from three sources: i) a geolocalised traffic accident database whose entries are based on police reports, ii) GPS trajectories obtained from a study on 4,000 drivers involving 55,000 trips and iii) the structure of the road network obtained from the OpenStreetMap (OSM) database. We highlight challenges, both methodological and computational, with the use of those three data sources in producing sensible inference for the covariate effects.