Citation

Shen S, Zhang F, Linwood SL, Lu B, Zhu M. Transp. Res. Rec. 2024; 2678(7): 835-844.

Copyright

(Copyright © 2024, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981231209321

PMID

unavailable

Abstract

Being able to predict motor vehicle crashes which are a major public health concern would greatly improve traffic safety. The prevalence of mobile sensing platforms now allows spatially and temporally rich driving data to be collected relatively easily. Research efforts have been devoted to predicting crashes from such data. This paper seeks in particular to assess the feasibility and performance of using aggregated fine geo-resolution vehicle telemetric data for crash risk prediction. We acquired vehicle telemetric data from Geotab Inc., which recorded the frequency of hard acceleration, hard braking, harsh cornering, and the average magnitude of those harsh events among its registered commercial vehicles for every 150 × 150 m2 roadway segment within Columbus, Ohio between January and April 2018. We aggregated the data, obtained the crash history from the Ohio Police Accident Report, and leveraged three machine learning-based algorithms to predict the crash risk. The results suggest that aggregated vehicle telemetric data could provide acceptable predictions for crash risk at a roadway segment level. Our models' predictive performances were further improved and maximized by including in the models both vehicle telemetric data and roadway geometric characteristics. Several factors, such as the aggregated count of hard accelerations and the presence of an intersection, were shown to be the factors that potentially made the greatest contribution to crash occurrence. We concluded that vehicle telemetric data could provide complementary and valuable information about crash likelihood monitoring, which may enable the police and city planners to implement proactive safety interventions. Yet the nature of traffic crashes is still complex and multi-dimensional.

Language: en