Predicting cycling accident risk in Brussels: a spatial case-control approach

Area of Science:

• Transportation Safety
• Urban Planning
• Spatial Analysis

Background:

• Cycling safety is a critical concern in urban environments.
• Understanding the influence of road infrastructure on cycling accidents is essential for prevention.
• The Brussels-Capital Region presents a unique case for studying cycling risk factors.

Purpose of the Study:

• To predict cycling accident risk across the entire road network in Brussels.
• To identify specific road infrastructure elements that influence cycling safety.
• To provide a tool for urban planners and policymakers to mitigate risks.

Main Methods:

• A spatial Bayesian modeling approach was employed.
• A case-control strategy was used with a binary dependent variable (accident/no accident).
• Control sites were sampled based on potential bicycle traffic along the road network.

Main Results:

• High accident risk is associated with on-road tram tracks, bridges lacking cycling facilities, and complex intersections.
• Proximity to shopping centers, garages, and heavy van/truck traffic increases risk.
• Cycle facilities at intersections and parked vehicles near separated facilities also elevate risk, while contraflow cycling reduces it.

Conclusions:

• Predicted accident risk mapping can identify high-risk locations before accidents occur.
• This tool aids planners in targeted infrastructure improvements and risk mitigation.
• The findings offer valuable information for cyclists to choose safer routes.