Bibliographic record
Abstract
Travel demand modeling is one of the key areas in transportation planning and engineering. Traditionally, it has been based on four inter-connected modules: trip generation, trip distribution mode choice, and traffic assignment. While the traditional approach remains popular among practitioners, it has been criticized widely due to its zonal aggregate nature. There has been a shift towards using micro-based models that use households and members of households as the units of analysis in lieu of the traffic analysis zones. This thesis contributes to advancing this micro-based paradigm by studying travel demand in the London Census Metropolitan Area (CMA), Ontario. It does so by developing an improved four-step travel demand model using a recent household travel survey that was collected in the year 2009. The focus will be as follows: first, compare various techniques that could be used to model trip generation (i.e., regression, cross-classification, discrete choice, and count models) at the micro-level. Also, compare the predictive ability of these micro-models against conventional zone-based models. Second, apply advanced geo-spatial methods and statistical techniques to model trip distribution using micro-data from the London Household Travel Survey (LHTS). To date, trip distribution in the four-stage model has relied on the gravity approach, which is too simplistic to capture the real complexities of spatial interactions between the traffic analysis zones forming an urban area. Also, its aggregate nature does not allow it to adequately capture the interaction of the traveler’s socio-economic characteristics with the attributes of alternative destinations. The results will allow us to devise an improved four-stage model that makes use of a conventional Household Travel Survey. Here, advanced techniques will be employed to improve the predictability of these models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".