Master Thesis – Crash Data Analysis & Weighting Methods
Autoliv GroupOm tjänsten
BACKGROUND
Would you like to use your skills in statistics and data analysis to contribute to a better understanding of real-world traffic
crashes and injury prevention?
In-depth crash databases provide valuable information about how and why crashes occur and how injuries can be prevented. However,
these databases may contain sampling biases, which need to be accounted for to ensure that results accurately represent the
relevant country or region.
The Road Accident Sampling System India (RASSI) is an in-depth crash database containing data from more than 8,000 crashes
collected over a 12-year period. RASSI currently provides weighting factors to adjust for sampling bias at crash level, but there
is a need to extend this methodology to person and injury levels.
Another international database, the Initiative for the Global Harmonisation of Accident Data (IGLAD), currently lacks weighting
factors, which limits how the data can be used in representative analyses.
ABOUT THE THESIS
In this Master Thesis project, you will explore and compare statistical methods for correcting sampling bias in in-depth crash
databases.
The main focus will be to develop multilevel weighting factors that can be applied from crash level down to person and injury
level. You will evaluate different weighting approaches and identify a suitable method for the application.
The selected method will then be applied to IGLAD data to explore whether the methodology can be transferred to another
international crash database and to assess its strengths and limitations.
YOUR THESIS WILL INCLUDE
WHO ARE WE LOOKING FOR?
We are looking for 1–2 Master’s students with an interest in statistics, data analysis and real-world applications.
You should have:
Knowledge of traffic safety epidemiology and sample design is beneficial but not required.
INTERESTED?
For more information about the thesis, please contact:
Hanna Jeppsson
Junaid Shaikh
Jordanka Kovaceva, Chalmers University of Technology
More lives saved – more life lived!
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