TY - JOUR PY - 2023// TI - Smartphone-based hard-braking event detection at scale for road safety services JO - Transportation research part C: emerging technologies A1 - Liu, Luyang A1 - Racz, David A1 - Vaillancourt, Kara A1 - Michelman, Julie A1 - Barnes, Matt A1 - Mellem, Stefan A1 - Eastham, Paul A1 - Green, Bradley A1 - Armstrong, Charles A1 - Bal, Rishi A1 - O'Banion, Shawn A1 - Guo, Feng SP - e103949 EP - e103949 VL - 146 IS - N2 - Road crashes are the sixth leading cause of lost disability-adjusted life-years (Dalys) worldwide. One major challenge in traffic safety research is the sparsity of crashes, which makes it difficult to achieve a fine-grain understanding of crash causations and predict future crash risk in a timely manner. Hard-braking events have been widely used as a safety surrogate due to their relatively high prevalence and ease of detection with embedded vehicle sensors. As an alternative to using sensors fixed in vehicles, this paper presents a scalable approach for detecting hard-braking events using the kinematics data collected from smartphone sensors. We train a Transformer-based machine learning model for hard-braking event detection using concurrent sensor readings from smartphones and vehicle sensors from drivers who connect their phone to the vehicle while navigating in Google Maps. The detection model shows superior performance with a 0.83 Area under the Precision-Recall Curve (Pr-auc), which is 3.8×better than a Gps speed-based heuristic model, and 166.6×better than an accelerometer-based heuristic model. The detected hard-braking events are strongly correlated with crashes from publicly available datasets, supporting their use as a safety surrogate. In addition, we conduct model fairness and selection bias evaluation to ensure that the safety benefits are equally shared. The developed methodology can benefit many safety applications such as identifying safety hot spots at road network level, evaluating the safety of new user interfaces, as well as using routing to improve traffic safety.

Language: en

LA - en SN - 0968-090X UR - http://dx.doi.org/10.1016/j.trc.2022.103949 ID - ref1 ER -