Multifaceted Investigations on Transit Demand Resilience: A Roadmap for Developing Sustainable Urban Transit Policies
Bibliographic record
Abstract
The thesis inspects transit's resiliency during an unprecedented disruptive event and conducts longitudinal analysis to identify strategies for transit to resist, recover and adapt to such disturbances. The recent COVID-19 pandemic has disrupted the transit system and its overall functions. Besides, users' vigilant safety concerns and the consequent transit avoidance behavior left the concerned authorities searching for recovery strategies. Therefore, the research aimed to conduct empirical investigations on the COVID impact on users' multidimensional transit preferences at different pandemic phases. The study also intended to fabricate a comprehensive travel demand model to provide data-driven insight into the plausible policies for building a health crisis-resistant transit system.The dissertation presents six empirical investigations, each highlighting different research avenues. For behavioral studies, the emphasis lies in administering and designing multiple surveys, conceptualizing choice experiments, preprocessing collected data, and devising advanced transportation demand modeling techniques. The first study examines the passenger's transit mode choice behavior capturing the user's sensitivity to specific pandemic characteristics and infection-resistant transit safety policies. The second work uses an integrated modeling approach to investigate the interaction between users' in-vehicle safety perception and transit usage during the second pandemic year. It also portrays the efficacy of rapid vaccine rollout in restoring transit demand. Later, the research examines the long-lasting impact of pandemic fear, individuals' vaccination status, and level of service attributes on users' transit route choice behavior. Then, the thesis presents a hypothesized psychometric model utilizing the protection motivation theory. It captures the interplay amongst trip makers' pandemic-induced psychometric factors, altered travel behavior, and future transit usage. Conversely, the fifth work provides insight into the potential of post-pandemic interventions in reinstating the demand. However, the final work developed a multimodal network microsimulation model in an agent-based framework for the Greater Toronto Area using a simulation tool, MATSim, and a wide range of transportation data. The model will be a testbed for evaluating the policies endorsed in former analyses. The empirical findings provide first-hand evidence for transit planners with timely and effective transit policies and service planning to withstand the unexpected pandemic shock and reinstate its regular functionality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".