Route Choice Modelling for Urban Commuters: Considering Bridge choice as a key determinant of selected routes
Bibliographic record
Abstract
Trip assignment is still a modelling and prediction challenge. For aggregate analyses, traditional trip assignment approaches may suffice. However, investigations of drivers’ choices with respect to network infrastructure changes require more disaggregate and behavioural approach. Effects of critical infrastructure elements in the network on route choice behaviour of the drivers are often crucial to investigate. The case of Montreal is of particular interest since the city, an Island, is completely separated from the rest of the region by two important rivers. Consequently, drivers have to select one of the available bridges to reach their destination. The research relies on a set of observed trips with bridge declaration from a large-scale travel survey conducted in 2008. It is a one-day trip diary reaching some 4% of the residing population and including the bridge chosen in the itinerary for car driver trips. The paper provides a descriptive analysis of the bridges and their usage. An advanced discrete choice model that jointly models choice set formation and final choice is then formulated and estimated using the observed trips. Empirical model correctly identifies effects of travel time interacting with time of day and destination trip purpose. Travellers are more sensitive to travel time during off-peak period. Empirical results show that age, gender and household auto ownership explain the variation of scale parameters of route/bridge choice; for instance, older people show more stable route/bridge choice behaviour than younger ones. Discussion on the performance of the model is provided along with further result analysis and perspectives for further work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".