Data Fusion Methods to Support Travel Demand Modelling in Emerging Contexts
Bibliographic record
Abstract
In the face of growing incomprehensiveness of traditional data sources, lack of behavioural information of emerging sources, and changing data requirements of advanced models, the dissertation investigates the feasibility of fusing data from multiple sources to generate richer, more comprehensive, and more representative information to support disaggregate travel demand modelling. It presents an econometric data fusion framework that is based on four dimensions: purpose of fusion, characteristics of input data, feature of fusion methods, and nature of outputs. It proposes a conceptual framework to integrate econometric data fusion with activity-travel model to create a consistent simulation system. The thesis presents four empirical investigations that implement parts of the proposed conceptual framework. The first two studies demonstrate the feasibility of fusing traditional data with emerging data to infer the missing semantic information of the latter. The first exercise proposes an econometric fusion method to infer trip purposes from GPS trajectories of ride-hailing services in Toronto by combining with travel survey and land use data. The inferred trip purpose pattern extends our understanding about ride-hailing demand and helps account for trip underreporting in travel surveys. The second investigation infers origin and destination zones of transit trips from smart card data by fusing with survey data, land use information, and network characteristics. This helps generate up-to-date demand data necessary for public transport planning and operations. The dissertation also demonstrates the feasibility of fusing multiple survey data to capture travel attitudes and preferences. The third analysis focuses on enriching a “core” travel diary survey with attitudinal information from “satellite” surveys via implicit fusion to evaluate the impacts of COVID-19 on passenger travel demand. The results highlight the ability of the imputed variables to support the estimation of an advanced econometric model. The fourth investigation proposes a model-based method to fuse repeated cross-sectional travel survey data based on the theory of rational inattention to capture travel preference evolution and enhance the forecasting robustness of discrete choice models. Taken together, the components of this dissertation lay the framework for producing rich and accurate input data for evidence-based transportation planning in the emerging context.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".