Mutual Recognition Methodology Development
Report, 2015

Phase 1 of the Mutual Recognition Methodology Development (MRMD) project developed an approach to statistical modeling and analysis of field data to address the state of evidence relevant to mutual recognition of automotive safety regulations. Specifically, the report describes a methodology that can be used to measure evidence for the hypothesis that vehicles meeting EU safety standards would perform similarly to US-regulated vehicles in the US driving environment, and that vehicles meeting US safety standards would perform similarly to EU-regulated vehicles in the EU driving environment. As part of the project, we assessed the availability and contents of crash datasets from the US and the EU, as well as their collective ability to support the proposed statistical methodology. The report describes a set of three statistical approaches to “triangulate” evidence regarding similarity or differences in crash and injury risk associated with EU- and US-regulated vehicles. Approach 1, Seemingly Unrelated Regression, tests whether the models are identical and will also assess the capability of the data analysis to detect differences in the models, if differences exist. Approach 2, Consequences of Best Models, uses logistic regression to develop two separate models, one for EU risk and one for US risk, as a function of a set of predictors (i.e., crash, vehicle, and occupant conditions). The two models will then be exercised on a standard population for the EU and a standard population for the US. Approach 3, Evidence for Consequences, turns the question around to measures the overall evidence for each of a set of possible conclusions. Each conclusion is characterized by a range of relative risk on a single population. Evidence is measured using a weighted average of likelihoods for a large group of models that produce the same outcome. That evidence is then compared using Bayes Factors.

safety regulations

Injury risk

statistical methods

Author

Carol A.C. Flannagan

Paul E. Green

Kathleen D. Klinich

Miriam A. Manary

András Bálint

SAFER, The Vehicle and Traffic Safety Centre

Chalmers, Applied Mechanics, Vehicle Safety

Ulrich Sander

SAFER, The Vehicle and Traffic Safety Centre

Chalmers, Applied Mechanics, Vehicle Engineering and Autonomous Systems

Bo Sui

Peter Sandqvist

Selpi Selpi

SAFER, The Vehicle and Traffic Safety Centre

Chalmers, Applied Mechanics, Vehicle Safety

Christian Howard

Driving Forces

Sustainable development

Areas of Advance

Transport

Roots

Basic sciences

Subject Categories

Vehicle Engineering

Probability Theory and Statistics

More information

Created

10/8/2017