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Author ORCID Identifier
N/A
AccessType
Open Access Dissertation
Document Type
dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Civil and Environmental Engineering
Year Degree Awarded
2019
Month Degree Awarded
February
First Advisor
Michael Knodler, Jr.
Subject Categories
Transportation Engineering
Abstract
Transportation Safety Planning (TSP) is a statewide-scale tool and combines transportation planning processes with safety aims to increase safety and reduce transportation fatalities and injuries. Traffic safety, which continues to remain a critical issue worldwide, has led to a myriad of modeling techniques to improve analytical capabilities with respect to crash modeling and prediction. State and metropolitan transportation planning processes must be consistent with Strategic Highway Safety Plans. This research aims to identify models and methods to improve the ability to capture variables that have the most significant impact on traffic safety through crash prediction modeling. In order to achieve this research goal, the research objectives are as follows:
- Identify important variables in TSP.
- Investigate different areal unit such as traffic analysis zones (TAZs) and traffic analysis districts (TADs).
- Explore the modifiable areal unit problem (MAUP), which addresses crashes on the boundaries and autocorrelation in macro-level crash modeling.
- Analysis of before and after crashes and testing Poisson distribution
DOI
https://doi.org/10.7275/13489398
Recommended Citation
Garmroudi Dovirani, Somaye, "Applications of Machine Learning Methods in Macroscopic Crash Analysis for Transportation Safety Management" (2019). Doctoral Dissertations. 1482.
https://doi.org/10.7275/13489398
https://scholarworks.umass.edu/dissertations_2/1482