Efficient mapping of crash risk at intersections with connected vehicle data and deep learning models

Accident; Analysis and Prevention
Jiajie HuXiong Yu

Abstract

Traditional methods for identifying crash-prone roadways are mainly based on historical crash data. It usually requires more than three years to collect a sufficient amount of dataset for road safety assessment. However, the emerging connected vehicles (CVs) technology generates rich instantaneous information, which can be used to identify dangerous road sections proactively. Information about the identified crash-prone intersections can be shared with the surrounding vehicles via CVs communication technology to promote cautious driving behaviors; in the longer term, such information will guide the implementation of countermeasures to prevent potential crashes. This study proposed a deep-learning based method to predict the risk level at intersections based on CVs data from the Michigan Safety Pilot program and historical traffic and intersection crash data in areas around Ann Arbor, Michigan, USA. One month of data by CVs at intersections were used for analyses, which accounts for about 3%-12% of overall trips. The risk levels of 774 intersections (i.e., low, medium and high risk) are determined by the annual crash rates. Feature extraction process is applied to both CV's data and traffic data at each intersection and 24 featu...Continue Reading

References

Feb 24, 2001·Accident; Analysis and Prevention·M M Minderhoud, P H Bovy
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Citations

Apr 27, 2021·Accident; Analysis and Prevention·Qiangqiang ShangguanShou'en Fang

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