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Access Type
WSU Access
Date of Award
January 2024
Degree Type
Dissertation
Degree Name
Ph.D.
Department
Civil and Environmental Engineering
First Advisor
Steven Lavrenz
Abstract
Road traffic crashes are a major global issue, causing over 1.19 million fatalities worldwide, with vulnerable road users (VRU) such as pedestrians, bicyclists, and motorcyclists accounting for more than half of the fatalities. The recent trends in VRU crashes in the United States reveal a significant increase in pedestrian and bicyclist fatalities. These increasing traffic crashes affect the individuals involved, their families, communities, and the entire country physically, psychologically, and financially. Examining the contributing factors to crashes and establishing suitable countermeasures through shared responsibility among policymakers, stakeholders, and road users is essential to maintaining safer roadways. Maintaining and evaluating transportation data using appropriate statistical approaches is vital for such comprehensive analysis.Traditional statistical methods based on historical crash records provide insight into crash causality. Novel transportation data analysis techniques integrated with intelligent transportation systems can significantly enhance safety and performance analysis. This research encourages stakeholder cooperation by leveraging novel transportation data analysis techniques to examine various traffic operation and safety performance measures, including crashes and crash surrogates. This dissertation has three primary objectives. Initially, it creates a framework for classifying suburban-type roadways (STR), a context-specific roadway classification system emphasizing VRU safety. Second, by addressing problems associated with unobserved heterogeneity in crash data, it investigates an advanced approach towards crash data collection and analysis. Third, it investigates the application of surrogate safety measures (SSM) alongside video analytics techniques to evaluate risky behavior, such as red-light violations (RLV) and near-miss events. These objectives are achieved by conducting research projects utilizing multiple data sources on highway safety, including crash data, crash surrogates, and intelligent transportation systems. The findings from this dissertation contribute to three critical areas of transportation safety and operations: - Novel context-specific transportation evaluation - Novel analytics in transportation evaluation - Novel performance measures for transportation evaluation Overall, this study contributes to a larger initiative to upgrade transportation data analysis while employing recent technological developments to improve safety by reducing fatalities and mobility by reducing congestion.
Recommended Citation
Khanal, Bedan, "Novel Safety Analysis For Heterogenous Roadways: Integrating Crash Data, Crash Surrogates, And Intelligent Transportation Systems" (2024). Wayne State University Dissertations. 4137.
https://digitalcommons.wayne.edu/oa_dissertations/4137
