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Advancing Data-driven Decision Making for Transportation Safety: Emerging Trends, Statistical Modeling, and Causal Inference

Year: 2024
Type: dissertation
Abstract: Despite recent advances in data-driven decision-making for transportation safety, there remain potential biases in the decision-making for emerging trends (i.e., shared mobility, safe systems, vehicle electrification, and equity) in transportation. In that case, this dissertation aims to facilitate robust and equitable data-driven decision-making in transportation safety, addressing the potential biases: 1) spatial and inherent correlations across different types of crashes, 2) measurement error biases of exposures, 3) confounding biases, and 4) disparity biases in transportation safety. To be more specific, four research objectives are demonstrated below. Chapter 3 assessed the safety performance of taxi and ride-hailing services by a multivariate conditional autoregressive model considering measurement errors in mode-specific exposures. Ride-hailing services are found to be prone to a higher risk of minor injury crashes than taxis, despite no significant difference in the risks of severe injury crashes. Chapter 4 investigated the safety effectiveness of citywide speed limit reduction by a causal inference approach integrating propensity score matching and spatial difference-in-differences. The speed limit reduction was found to result in a 62.09% decrease in fatal crashes but insignificant changes in injury and property-damage-only crashes. Chapter 5 compared the injury severity between electric vehicle crashes and conventional vehicle crashes by a doubly robust estimator, including matching-based and regression-based ones. Electric vehicle crashes are found to be prone to a lower risk of being severely injured compared to conventional ones. Chapter 6 proposes a novel concept of equity-aware safety performance functions for pedestrian crashes, enabling a distinct treatment of equity-related variables and yielding an equity-aware identification of crash hotspots. The proposed approach is found to diminish the effects of equity-related variables, generating higher potentials for safety improvement for disadvantaged areas with significant differences in the rankings of crash hotspots. In a nutshell, these studies above add more robust and equitable data-driven decision-making approaches to the literature methodologically; the conclusions practically provide researchers, practitioners, and policymakers insights into transportation safety management of emerging trends.
DOI: 10.25777/mdjw-cd79
Language: English
Open Access status: green
OpenAlex ID: W6927117652
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