Spatio-temporal modeling of traffic accidents incidence on urban road networks based on an explicit network triangulation

ABSTRACT

Traffic deaths and injuries are one of the major global public health concerns. The present study considers accident records in an urban environment to explore and analyze spatial and temporal in the incidence of road traffic accidents. We propose a spatio-temporal model to provide predictions of the number of traffic collisions on any given road segment, to further generate a risk map of the entire road network. A Bayesian methodology using Integrated nested Laplace approximations with stochastic partial differential equations (SPDE) has been applied in the modeling process. As a novelty, we have introduced SPDE network triangulation to estimate the spatial autocorrelation restricted to the linear network. The resulting risk maps provide information to identify safe routes between source and destination points, and can be useful for accident prevention and multi-disciplinary road safety measures.

KEYWORDS:

• INLA
• network triangulation
• Poisson hurdle model
• SPDE
• traffic risk mapping

Author contributions statement

Conceptualization, PJ and SC; Data curation, SC; Formal analysis, SC, PJ and JM; Methodology, SC, PJ and JM; Project administration, JM; Resources, SC; Software, PJ and SC; Validation, PJ and JM; Writing original draft, SC; Writing review & editing, PJ and JM. The authors declare that they have no conflict of interest.

Ethical statement

Author and co-authors testify that, this manuscript is original, has not been published before and is not currently being considered for publication elsewhere. We know of no conflicts of interest associated with this publication and there has been no financial support for this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).