Built environment, driving errors and violations, and crashes in naturalistic driving environment

• Published in: Accident analysis and prevention 2021-07, Vol.157 (C), p.106158-106158, Article 106158
• Main Authors: Ahmad, Numan, Wali, Behram, Khattak, Asad J., Dumbaugh, Eric
• Format: Article
• Language: English
• Subjects: Built environment; Driver errors and violations; Multinomial logit; Naturalistic driving study; Ordered probit; Path analysis; SHRP2; Taxonomy
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2021.106158

•Links between built environment, driving errors, & safety are systematically examined.•Systematic taxonomy is applied conceptualizing wide range of driving errors/violations.•Baselines, near-crashes, and crashes in naturalistic driving environment are analyzed.•Discrete outcome path analysis framework is used.•Relevance of ITS countermeasures in addressing key driving errors is discussed. Driving errors and violations are highly relevant to the safe systems approach as human errors tend to be a predominant cause of crash occurrence. In this study, we harness highly detailed pre-crash Naturalistic Driving Study (NDS) data 1) to understand errors and violations in crash, near-crash, and baseline (no event) driving situations, and 2) to explore pathways that lead to crashes in diverse built environments by applying rigorous modeling techniques. The “locality” factor in the NDS data provides information on various types of roadway and environmental surroundings that could influence traffic flow when a precipitating event is observed. Coded by the data reductionists, this variable is used to quantify the associations of diverse environments with crash outcomes both directly and indirectly through mediating driving errors and violations. While the most prevalent errors in crashes were recognition errors such as failing to recognize a situation (39 %) and decision errors such as not braking to avoid a hazard (34 %), performance errors such as poor lateral or longitudinal control or weak judgement (8 %) were most strongly correlated with crash occurrence. Path analysis uncovered direct and indirect relationships between key built-environment factors, errors and violations, and crash propensity. Possibly due to their complexity for drivers, urban environments are associated with higher chances of crashes (by 6.44 %). They can also induce more recognition errors which correlate with an even higher chances of crashes (by 2.16 % with the “total effect” amounting to 8.60 %). Similar statistically significant mediating contributions of recognition errors and decision errors near school zones, business or industrial areas, and moderate residential areas were also observed. From practical applications standpoint, multiple vehicle technologies (e.g., collision warning systems, cruise control, and lane tracking system) and built-environment (roadway) changes have the potential to reduce driving errors and violations which are discussed in the paper.