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Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest
Yangsong Gu1, Diyi Liu1, Ramin Arvin1
1Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, USA.
Accident; Analysis and Prevention
|November 8, 2022
Summary
Predicting traffic crashes is improved with connected vehicle data and a new Geographical Random Forest (GRF) AI model. This method accurately identifies risky intersections by analyzing driving behaviors and spatial factors.
Area of Science:
- Traffic Safety
- Artificial Intelligence
- Transportation Engineering
Background:
- Accurate crash frequency prediction is vital for proactive traffic safety management.
- Connected vehicles generate extensive data linking driving behaviors to crash occurrences.
- Addressing spatial dependencies and numerous driving features in crash prediction remains challenging.
Purpose of the Study:
- To investigate a novel Artificial Intelligence technique, Geographical Random Forest (GRF), for crash frequency prediction.
- To address spatial heterogeneity and incorporate multiple driving behavior predictors.
- To predict rear-end crash frequency at intersections using connected vehicle data.
Main Methods:
- Utilized over 2.2 billion connected vehicle Basic Safety Message (BSM) observations.
- Extracted 30 indicators of driving volatility, including speed, acceleration, and yaw rate.
- Developed and implemented a Geographical Random Forest (GRF) model to predict rear-end crashes at intersections.
Main Results:
- Rear-end crashes are more frequent at intersections with minor roads.
- Frequent hard acceleration/deceleration events are key predictors of rear-end crashes.
- The GRF model demonstrated a 9% lower test error than Global Random Forest, indicating superior performance and fit.
- Geographical visualization revealed spatial non-stationarity in variable importance.
Conclusions:
- The GRF framework effectively predicts rear-end crash frequency at intersections.
- The model can proactively identify high-risk intersections based on driving volatility indicators.
- Findings support alerting drivers to worsening driving volatility patterns to prevent crashes.
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