Analysing the development of road safety using demographic data

Area of Science:

• Traffic Safety and Risk Analysis
• Demographic Data Integration
• Time Series Forecasting

Background:

• Traditional road safety analyses often rely solely on distance traveled data.
• Demographic shifts can influence travel patterns and road safety metrics.
• Understanding population dynamics is crucial for accurate risk assessment.

Purpose of the Study:

• To demonstrate the improvement of road safety time series analyses using demographic data.
• To show how per capita distance traveled data can stabilize analyses.
• To explore alternative risk assessment methods when distance traveled data is unavailable.

Main Methods:

• Time series analysis of road safety and risk data stratified by age, gender, or both.
• Comparison of total distance traveled versus per capita distance traveled.
• Utilizing Dutch demographic and road usage data for model illustration.

Main Results:

• Per capita distance traveled exhibits smoother trends than total distance traveled, reducing stochastic fluctuations.
• Demographic data integration substantially improves the accuracy of distance traveled analyses and forecasts.
• Stratified analysis of mortality (casualties per inhabitant) serves as a viable alternative for risk assessment without distance traveled data.

Conclusions:

• Demographic data is essential for robust road safety and risk analysis and forecasting.
• Per capita distance traveled provides a more stable metric for evaluating traffic safety trends.
• Integrating population data offers a significant advantage over analyses relying solely on travel data.