Machine Learning Based Demand Modelling for On-Demand Transit Services: A Case Study of Belleville, Ontario.
Bibliographic record
Abstract
The use of mobile applications apps and GPS service on smartphones for transportation management applications has enabled the new mobility service, where the transportation supply is following the users' schedule and routes. In September 2018, the City of Belleville in Canada and Pantonium operationalized the same idea, but for the public transit service in the city to develop an on-demand transit (ODT) service. An existing fixed route (RT 11) public transit service was converted into an on-demand service during the night as a pilot project to maintain a higher demand sensitivity and highest operation cost efficiency per trip. In this study, Random Forest (RF), Bagging, Artificial Neural Network (ANN), and Deep Neural Network (DNN) machine learning algorithms were adopted to develop a pickup demand model (trip generation) and a trip demand model (trip distribution model) for Belleville ODT service based on the dissemination areas' demographic characteristics and the existing trip characteristics. The developed models aim to explain the demand behavior, investigate the main factors affecting the trip pattern and their relative importance, and to predict the number of generated trips from any dissemination area as well as between any two dissemination areas. The results indicate that the developed models can predict 63% and 70% of the pickup and trip demand levels, respectively. Both models are most affected by the month of the year and the day of the week variables. In addition, the population density has a higher impact on the ODT service pickup demand levels than the other demographic characteristics followed by the working age percentages and median income characteristics. Whereas, the distribution of the trips depends on the demographic characteristics of the destination area more than the origin area.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".