Forecasting German crash numbers: The effect of meteorological variables

Kevin Diependaele, Heike Martensen, Markus Lerner, Andreas Schepers, Frits Bijleveld, Jacques J.F. Commandeur

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

At the end of each year, the German Federal Highway Research Institute (BASt) publishes the road safety balance of the closing year. They describe the development of accident and casualty numbers disaggregated by road user types, age groups, type of road and the consequences of the accidents. However, at the time of publishing, these series are only available for the first eight or nine months of the year. To make the balance for the whole year, the last three or four months are forecasted. The objective of this study was to improve the accuracy of these forecasts through structural time-series models that include effects of meteorological conditions. The results show that, compared to the earlier heuristic approach, root mean squared errors are reduced by up to 55% and only two out of the 27 different data series yield a modest rise of prediction errors. With the exception of four data series, prediction accuracies also clearly improve incorporating meteorological data in the analysis. We conclude that our approach provides a valid alternative to provide input to policy makers in Germany.

LanguageEnglish
JournalAccident Analysis and Prevention
DOIs
StateAccepted/In press - 2019

Fingerprint

Accidents
Plant shutdowns
accident
Administrative Personnel
road
Germany
Time series
Age Groups
road user
research facility
Safety
time series
age group
heuristics
Heuristics
time

Keywords

  • Meteorological effects
  • Road safety
  • Structural time-series model

Cite this

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title = "Forecasting German crash numbers: The effect of meteorological variables",
abstract = "At the end of each year, the German Federal Highway Research Institute (BASt) publishes the road safety balance of the closing year. They describe the development of accident and casualty numbers disaggregated by road user types, age groups, type of road and the consequences of the accidents. However, at the time of publishing, these series are only available for the first eight or nine months of the year. To make the balance for the whole year, the last three or four months are forecasted. The objective of this study was to improve the accuracy of these forecasts through structural time-series models that include effects of meteorological conditions. The results show that, compared to the earlier heuristic approach, root mean squared errors are reduced by up to 55{\%} and only two out of the 27 different data series yield a modest rise of prediction errors. With the exception of four data series, prediction accuracies also clearly improve incorporating meteorological data in the analysis. We conclude that our approach provides a valid alternative to provide input to policy makers in Germany.",
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Forecasting German crash numbers : The effect of meteorological variables. / Diependaele, Kevin; Martensen, Heike; Lerner, Markus; Schepers, Andreas; Bijleveld, Frits; Commandeur, Jacques J.F.

In: Accident Analysis and Prevention, 2019.

Research output: Contribution to JournalArticleAcademicpeer-review

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T2 - Accident Analysis and Prevention

AU - Diependaele,Kevin

AU - Martensen,Heike

AU - Lerner,Markus

AU - Schepers,Andreas

AU - Bijleveld,Frits

AU - Commandeur,Jacques J.F.

PY - 2019

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N2 - At the end of each year, the German Federal Highway Research Institute (BASt) publishes the road safety balance of the closing year. They describe the development of accident and casualty numbers disaggregated by road user types, age groups, type of road and the consequences of the accidents. However, at the time of publishing, these series are only available for the first eight or nine months of the year. To make the balance for the whole year, the last three or four months are forecasted. The objective of this study was to improve the accuracy of these forecasts through structural time-series models that include effects of meteorological conditions. The results show that, compared to the earlier heuristic approach, root mean squared errors are reduced by up to 55% and only two out of the 27 different data series yield a modest rise of prediction errors. With the exception of four data series, prediction accuracies also clearly improve incorporating meteorological data in the analysis. We conclude that our approach provides a valid alternative to provide input to policy makers in Germany.

AB - At the end of each year, the German Federal Highway Research Institute (BASt) publishes the road safety balance of the closing year. They describe the development of accident and casualty numbers disaggregated by road user types, age groups, type of road and the consequences of the accidents. However, at the time of publishing, these series are only available for the first eight or nine months of the year. To make the balance for the whole year, the last three or four months are forecasted. The objective of this study was to improve the accuracy of these forecasts through structural time-series models that include effects of meteorological conditions. The results show that, compared to the earlier heuristic approach, root mean squared errors are reduced by up to 55% and only two out of the 27 different data series yield a modest rise of prediction errors. With the exception of four data series, prediction accuracies also clearly improve incorporating meteorological data in the analysis. We conclude that our approach provides a valid alternative to provide input to policy makers in Germany.

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