PhD Oral Exam - Kayhan Alamatsaz, Building Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Energy sources that emit greenhouse gases are a primary contributor to global warming. As cities aim to be greener and more sustainable, shifting from conventional fossil fuel-based transportation to clean, renewable alternatives has become a key priority. Among the various solutions, the electrification of public transit systems has emerged as an effective approach to reduce urban air pollution and greenhouse gas emissions. Electric buses (EBs) are increasingly being adopted by transit agencies worldwide as an energy-efficient, emission-free alternative to conventional diesel buses. This shift has gained significant momentum in recent years, with many transit networks rapidly transitioning to fully or partially electric fleets. However, the introduction of EBs presents unique challenges for transit operation planning. Unlike conventional buses, EBs have limited travel ranges, require long charging times, and depend on the location of charging infrastructures. These operational differences necessitate a re-examination of traditional planning steps, including network design, timetabling, and bus scheduling. Existing mathematical optimization models developed for conventional buses cannot be directly applied to EBs due to their distinctive operational constraints. Consequently, new optimization models and solution methodologies are needed to ensure the efficient operation of EB fleets while maintaining a high level of service for passengers and minimizing operational costs for transit agencies.

In this thesis, we address the emerging optimization challenges of integrating electric buses into urban transit systems. First, we conduct a comprehensive literature review in this field to critically assess and classify the existing works and to identify potential areas for future research. The goal is to highlight the research gaps and propose potential directions for future studies to develop more realistic and applicable models and solution approaches for fully electric bus transit systems.

Second, we develop a mixed-integer linear programming (MILP) model that integrates bus timetabling and scheduling to minimize total operational costs, including travel, electricity consumption, and fleet utilization costs. The model also incorporates passenger service quality measures such as waiting time and seat availability to ensure user satisfaction. To evaluate the trade-offs between operational efficiency and service quality, a normalized weighted sum method is applied, enabling the selection of optimal headways that balance cost-effectiveness with high service levels. A case study of a university shuttle bus service in Montreal, Canada, demonstrates the effectiveness of the proposed model, showing significant reductions in passenger waiting times and improvements in seat availability compared to the current schedule.

In addition to scheduling optimization, this thesis considers the critical role of charging infrastructure in EB operations. We propose an integer linear programming (ILP) model for the joint optimization of bus scheduling and fast-charging station location planning in multi-depot networks. The objective is to minimize the combined costs of scheduling, travel, energy consumption, and charger installation. Due to the complexity of the problem, branch-and-price algorithms with different branching strategies are developed and tested across various problem instances to identify the most computationally efficient approach. Then, we apply the model on a real case study in Montreal to demonstrate the applicability of the model. We perform a sensitivity analysis to identify the most cost-effective type of electric bus, considering the specific characteristics of different EB types to assist the Montreal transit authority in selecting the most suitable EB type for fleet electrification.

The case studies and computational experiments show that the proposed models and solution methods significantly enhance service quality, lower operational and infrastructure costs, and support a smooth transition to a fully electric public transit system. By offering transit agencies practical and effective models, this research advances sustainable urban mobility and contributes to global efforts to address climate change.