Investigating Relationship Between Driving Patterns and Traffic Safety Using Smartphones Based Mobile Sensor Data

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In spite of various advancements in vehicle safety technologies and improved roadway design practices, roadway crashes remain a major challenge. While certain hotspots may be unsafe primarily due to the geometric features of these locations, in many cases the safety risk seems to be an outcome of the unsafe driving patterns along the roadway stretching downstream and/or upstream of the actual crash locations. Even though there is plenty of research on correlating safety measures to roadway characteristics and some elements of traffic flow (e.g., exposure, speed), there is no significant literature on analyzing the correlation between high-resolution speed and acceleration data and crash risks along highway segments. Collecting such high-resolution data is now feasible with the mobile consumer devices such as smartphones. Smartphones are now equipped with sensors capable of recording vehicle performance data at a very fine temporal resolution in a cost-effective way. The current project used this mobile sensor data to identify unsafe driving patterns and quantified the relationship between these driving patterns and traffic crash incidences. The models with microscopic traffic measures were shown to be statistically better than traditional models that only control for roadway geometry and traffic exposure variables. Also, from a methodological standpoint, generalized count models that provide more flexibility through spatial dependency, heterogeneous dispersion, and random parameter heterogeneity were found to perform better than standard Poisson and Negative Binomial models.


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0000-0003-4235-036X (Sahin), 0000-0003-2003-9343 (Cetin)

Original Publication Citation

Sahin, O., Paleti, R., & Cetin, M. (2016). Investigating relationship between driving patterns and traffic safety using smartphones based mobile sensor data. Mid-Atlantic Transportation Sustainability University Transportation Center 2016.