Surrogate safety and network screening: Modelling crash frequency using GPS travel data and latent Gaussian Spatial Models

• Published in: Accident analysis and prevention Vol. 120; pp. 174 - 187
• Main Authors: Stipancic, Joshua, Miranda-Moreno, Luis, Saunier, Nicolas, Labbe, Aurelie
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.11.2018
• Subjects: Crash frequency; Full Bayes; GPS; INLA; Latent Gaussian Model; Spatial; INLA; Full Bayes; Latent Gaussian Model; GPS; Spatial; Crash frequency
• ISSN: 0001-4575; 1879-2057; 1879-2057
• DOI: 10.1016/j.aap.2018.07.013

•A methodology for screening the network using surrogate safety measures is proposed.•The Full Bayes crash frequency model uses surrogate measures as covariates.•The model is estimated using the Integrated Nested Laplace Approximation approach.•In general, the effect of the covariates supported results in previous studies.•Similar models could be used to identify hotspots at links and intersections. Improving road safety requires accurate network screening methods to identify and prioritize sites in order to maximize the effectiveness of implemented countermeasures. In screening, hotspots are commonly identified using statistical models and ranking criteria derived from observed crash data. However, collision databases are subject to errors, omissions, and underreporting. More importantly, crash-based methods are reactive and require years of crash data. With the arrival of new technologies including Global Positioning System (GPS) trajectory data, proactive surrogate safety methods have gained popularity as an alternative approach for screening. GPS-enabled smartphones can collect reliable and spatio-temporally rich driving data from regular drivers using an inexpensive, simple, and user-friendly tool. However, few studies to date have analyzed large volumes of smartphone GPS data and considered surrogate-safety modelling techniques for network screening. The purpose of this paper is to propose a surrogate safety screening approach based on smartphone GPS data and a Full Bayesian modelling framework. After processing crash data and GPS data collected in Quebec City, Canada, several surrogate safety measures (SSMs), including vehicle manoeuvres (hard braking) and measures of traffic flow (congestion, average speed, and speed variation), were extracted. Then, spatial crash frequency models incorporating the extracted SSMs were proposed and validated. A Latent Gaussian Spatial Model was estimated using the Integrated Nested Laplace Approximation (INLA) technique. While the INLA Negative Binomial models outperformed alternative models, incorporating spatial correlations provided the greatest improvement in model fit. Relationships between SSMs and crash frequency established in previous studies were generally supported by the modelling results. For example, hard braking, congestion, and speed variation were all positively linked to crash counts at the intersection level. Network screening based on SSMs presents a substantial contribution to the field of road safety and works towards the elimination of crash data in evaluation and monitoring.