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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