Toward a crowdsourcing solution to identify high-risk highway segments through mining driving jerks

Abstract

Traffic crashes have become a leading cause of preventable deaths globally. Identifying high-risk segments not only benefits safety specialists to better understand crash patterns but also reminds road users to be aware of driving risks. This study reports on a new crowdsourcing solution to identify high-risk highway segments by analyzing driving jerks. Driving jerks represent the abrupt changes of acceleration, which have been shown to be closely related to traffic risks. In this study, we first calculate driving jerks from each participant's naturalistic driving data and identify "unsafe" drivers based on their jerk-ratio. Then, we innovatively propose an improved line-constrained clustering method to identify each participant's jerk clusters on each road. These individual-specific jerk clusters are overlapped with road networks to identify potential risky segments. By synthesizing these potential risky segments reported by different participants, we obtain the final detection results for high-risk highway segments. In this study, we compare the jerk-cluster-determined risky segments with crash-rate-determined risky segments to evaluate the proposed solution's effectiveness. The study results demonstrate that our crowdsourcing solution can effectively identify high-risk road segments with an estimated 75 % accuracy. More importantly, by analyzing this valued surrogate measure, safety specialists can identify hazardous road segments before crashes occur.

Keywords: Crowdsourcing; Driving jerks; Highway safety; Naturalistic driving data; Spatial clustering; Surrogate safety measure.