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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest.

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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.

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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.