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Road safety forecasts in five European countries using structural time series models.

Constantinos Antoniou1, Eleonora Papadimitriou, George Yannis

  • 1a Laboratory of Transportation Engineering , National Technical University of Athens , Athens , Greece.

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Summary

This study uses structural time series models to forecast road traffic fatality risk in five European countries. The models provide reliable medium- to long-term predictions, proving effective across varying data conditions.

Keywords:
Europelatent risk time series (LRT) modelsroad safetystate-space modelsstructural time series models

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Area of Science:

  • Road safety
  • Transportation engineering
  • Statistical modeling

Background:

  • Road safety development modeling is complex, requiring consideration of quantifiable parameters and unobserved trends.
  • Accurate medium- to long-term road traffic fatality risk forecasting is crucial for effective safety interventions.

Purpose of the Study:

  • To apply structural time series models for reliable road traffic fatality risk forecasting.
  • To forecast road traffic fatality risk in five diverse European countries (Cyprus, Greece, Hungary, Norway, Switzerland).

Main Methods:

  • Utilized two structural time series models: local linear trend and latent risk.
  • Employed a decision tree for model selection, incorporating fatality and exposure data.
  • Introduced intervention variables and developed mobility scenarios.

Main Results:

  • Developed models generated realistic forecasts within acceptable confidence intervals.
  • The methodology demonstrated efficiency in handling diverse data availability and quality.
  • Structural time series models offer a flexible alternative for road safety forecasting.

Conclusions:

  • The study successfully applied structural time series models for road safety forecasting.
  • The methodology is adaptable to different data scenarios, offering a robust approach.
  • Future research directions are outlined, building upon these findings.