Applying Extreme Value Models to Surrogate Measures for Traffic Safety Analysis

Applying Extreme Value Models to Surrogate Measures for Traffic Safety Analysis

Borsos, Attila (TU Delft Civil Engineering and Geosciences)

Hagenzieker, Marjan (mentor)
Farah, Haneen (graduation committee)
Cai, Juanjuan (graduation committee)
Laureshyn, Aliaksei (graduation committee)

Delft University of Technology

2019-11-13

The most common way to evaluate traffic safety is investigating the occurrence and severity of crashes using historical data. This approach however has a number of limitations, the most important of which is probably its reactive nature. An alternative method using non-crash events has gained a lot of attention recently, especially thanks to the rapid improvement of sensing technologies. By gathering trajectory data and calculating various Surrogate Measures of Safety it has become possible to analyse safety without waiting for accidents to happen. Using these indicators combined with Extreme Value Theory (EVT) one can estimate the probability of crashes as extreme (unobserved) events. The primary goal of this thesis is to contribute to the research that has been done so far on the application of Extreme Value Theory to Surrogate Measures for traffic safety analysis. Research questions seek for answers to what we can learn from applying univariate EVT using indicators describing collision course and crossing course interactions, and how we can predict nearness to collision and severity using bivariate EVT models.

traffic safety
extreme value theory
surrogate measures of safety

http://resolver.tudelft.nl/uuid:9c23dd43-66e5-424d-9763-12a00fa8f28b

Student theses

master thesis

© 2019 Attila Borsos