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Multilevel modelling and time series analysis in traffic safety research – Methodology

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Deliverable D7.4 of the EU FP6 project SafetyNet, (2007)

Abstract

The SafetyNet project is set up to build a European Road Safety Observatory. The data assembled or gathered for the observatory consist of the Community database on Accidents on the Roads in Europe (CARE); data on road safety risk indicators; data on road safety performance indicators and in-depth accident data. Potential users will link data from different data-sets, consider different levels of aggregation jointly, and analyse the development over time. Work package 7 (WP7) is set up to deal with statistical and conceptual issues that come into play when analysing such complex data structures. One of WP7’s main objectives is to develop a best practice advice for the analysis of data structures that require more than the standard statistical tools. This best practice consists of D7.4 “Multilevel modelling and time series analysis in traffic research – A methodology” and D7.5 “Multilevel modelling and time series analysis in traffic research – The manual”. The main goal is to enable the reader to deal with complex data-structures that show dependencies in space (nested data) or in time (time series data). At first it is demonstrated how such dependencies can compromise the applicability of standard methods of statistical inferences, because they can lead to an underestimation of the standard error and consequently of the error in statistical tests. As a solution to this problem, two families of statistical techniques are presented to deal with these dependencies. Multilevel Modelling is dedicated to the analysis of data that are structured hierarchically. It offers the possibility to include hierarchical structures into the model of analysis. In road-safety research, multilevel analyses allow for the introduction of exposure data and of safety performance indicators, even if those are not specified at the same level of disaggregation as the accident data themselves. In this way, multilevel analyses allow a global and detailed approach simultaneously. Time series analyses are employed to overcome dependency issues in time-related data. They allow describing the development over time, relating the accidentoccurrences to explanatory factors such as exposure measures or safetyperformance indicators (e.g., speeding, seatbelt-use, alcohol, etc), and forecasting the development into the near future. Deliverable D7.4 gives the theoretical background for these two families of analyses. For each technique the objectives, detailed model formulation, and assumptions are described and subsequently the technique is illustrated with an empirical example relevant to traffic safety research.

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