Probabilistic and Optimization Models for Transportation Infrastructures with Uncertainties
Transportation infrastructures are pivotal for economic growth and societal development. However, congestion, escalating maintenance costs, and safety threats hinder their optimal performance. To address these challenges in transportation systems, this dissertation explores probabilistic and optimization models from the perspective of efficiency, risk analysis and reliability studies that can lead to enhanced overall performance and safety. Specifically, we identify three critical problems within three transportation infrastructures, namely, seaport efficiency, airport security, and bridge safety and reliability. Regarding port efficiency, the need to expand shipping capacity and address severe congestion raises serious concerns. To evaluate the relative efficiency of container ports, this study employs data envelopment analysis (DEA) models with stepwise selection and window analysis. Input and output variables are meticulously selected using a pioneering stepwise selection approach within the port industry. By conducting comparisons using a 4-year window analysis, the study provides decision-makers with insights to optimize operations, identify strategic investment areas, and enhance net income and container throughput. Furthermore, airport security has become of utmost priority amid the persistent and ever-changing threat of terrorism. We design a weighted alarm security screening system, integrating passenger prescreening with multi-level checked-baggage screening. Through the optimization of threshold values on screening devices, the study aims to enhance threat detection capabilities while minimizing the system's life cycle cost. A comprehensive numerical analysis demonstrates the system’s effectiveness in maximizing security levels under a budget constraint. Meanwhile, the prevention of bridge failures leading to catastrophic consequences is a critical endeavor. This research investigates a degradation-based reliability of bridge deterioration by leveraging Markov and semi-Markov processes. An optimization model is formulated to minimize the rehabilitation cost and pinpoint the optimal time for interventions including condition assessment and maintenance. The analysis is conducted using the comprehensive National Bridge Inventory dataset for the State of Texas. Overall, this dissertation explores probabilistic and optimization models to optimize the performance, reliability and security of transportation infrastructures facing specific challenges involving uncertainties.