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Related Experiment Video

Updated: Jul 11, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Geographically weighted random forests for macro-level crash frequency prediction.

Dongyu Wu¹, Yingheng Zhang¹, Qiaojun Xiang¹

¹Jiangsu Key Laboratory of Urban ITS, Southeast University, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, China; School of Transportation, Southeast University, China.

Accident; Analysis and Prevention

|November 8, 2023

Summary

This summary is machine-generated.

Geographically Weighted Random Forest (GWRF) improves road safety predictions by accounting for spatial variations, outperforming traditional Random Forest (RF) models. This approach enables localized, effective road safety interventions.

Keywords:

Crash frequency prediction Random forest Road traffic safety Spatial analysis

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Area of Science:

Spatial analysis
Machine learning in transportation
Road safety engineering

Background:

Traditional Random Forest (RF) models offer robust predictions but overlook spatial variability in road safety.
Macro-level road safety analysis requires methods that capture geographically varying relationships between crash frequency and risk factors.

Purpose of the Study:

To introduce and evaluate a modified Random Forest algorithm, Geographically Weighted Random Forest (GWRF), for improved road safety analysis.
To compare the predictive performance of GWRF against traditional RF and Geographically Weighted Regression (GWR) models.
To investigate the impact of spatial relationships and multicollinearity on road safety prediction accuracy.

Main Methods:

Implementation of the Geographically Weighted Random Forest (GWRF) algorithm using London's Middle-super-output-area (MSOA) data.
Comparative analysis of RF, GWR, and GWRF models using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).
Inclusion of inter-zone discrepancy factors and assessment of multicollinearity effects on model performance.

Main Results:

GWRF demonstrated superior predictive performance compared to RF and GWR with optimal bandwidth selection.
GWRF model accuracy was not significantly affected by multicollinearity, although variable importance values could be reduced.
The influence of explanatory variables on crash frequency varied significantly across different zones, with downtown areas influenced by minor road density and peripheral areas by environmental discrepancies.

Conclusions:

GWRF offers a more accurate and spatially sensitive approach to road safety modeling than traditional RF.
Localized road safety interventions, informed by geographically specific risk factors, are more effective than city-wide guidelines.
Understanding spatial heterogeneity in risk factors is crucial for developing targeted and efficient road safety strategies.