A Bayesian Network Model for Contextual Versus Non-Contextual Driving Behavior Assessment
Driving behavior is generally considered to be one of the most important factors in crash occurrence. This paper aims to evaluate the benefits of utilizing context-relevant information in the driving behavior assessment process (i.e. contextual driving behavior assessment approach). We use a Bayesian Network (BN) model that investigates the relationships between GPS driving observations, individual driving behavior, individual driving risks, and individual crash frequency. In contrast to prior studies without context information (i.e. non-contextual approach), the data used in the BN approach is a combination of contextual features in the surrounding environment that may contribute to crash risk, such as road conditions surrounding the vehicle of interest and dynamic traffic flow information, as well as the non-contextual data such as instantaneous driving speed and the acceleration/deceleration of a vehicle. An information-aggregation mechanism is developed to aggregates massive amounts of vehicle GPS data points, kinematic events and context information into drivel-level data. With the proposed model, driving behavior risks for drivers is assessed and the relationship between contextual driving behavior and crash occurrence is established. The analysis results in the case study section show that the contextual model has significantly better performance than the non-contextual model, and that drivers who drive at a speed faster than others or much slower than the speed limit at the ramp, and with more rapid acceleration or deceleration on freeways are more likely to be involved in crash events. In addition, younger drivers, and female drivers with higher VMT are found to have higher crash risk.
X. Zhu et al., "A Bayesian Network Model for Contextual Versus Non-Contextual Driving Behavior Assessment," Transportation Research Part C: Emerging Technologies, vol. 81, pp. 172-187, Elsevier, Aug 2017.
The definitive version is available at https://doi.org/10.1016/j.trc.2017.05.015
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Accidents; Bayesian networks; Crashworthiness; Digital storage; Highway accidents; Information management; Regression analysis; Risk analysis; Semantics; Traffic control; Vehicles; Acceleration/deceleration; Assessment approaches; Bayesian network models; Dynamic traffic flow; Information aggregation; Information aggregation mechanisms; Regression model; Surrounding environment; Risk assessment; Aggregation behavior; Assessment method; Bayesian analysis; GPS; Network analysis; Numerical model; Regression analysis; Social behavior; Bayesian Network model; Contextual driving risk analysis; Information-aggregation; Regression models
International Standard Serial Number (ISSN)
Article - Journal
© 2017 Elsevier, All rights reserved.
01 Aug 2017