Optimizing route choice in multimodal transportation networks
Bibliographic record
Abstract
Advanced traveler information systems provide travelers with pre-trip and en route travel information necessary to improve the trip decision making process based on various criteria (e.g., avoiding the negative impacts of traffic congestion, selecting specific travel modes, etc.). This study investigates an adaptive routing methodology for multimodal transportation networks. To integrate transit networks, the model takes into account both the predefined timetables of public transportation services and the variability of travel times. A graph theory based methodology is proposed to capture travel behavior within a multimodal network. The study advances a routing algorithm based on Markov decision processes. Special network modeling elements were defined to allow the developed algorithm to select the most efficient transportation mode at each junction along a given route. The proposed methodology is applied to a small real-world network located in the central business district area of Montreal, Quebec. The network includes bus, subway, and bicycle transportation facilities. The simulations were run under the assumption that users do not use private vehicles to travel between arbitrary selected origin and destination points. The developed routing algorithm was applied to several simulation scenarios. The results identified what is the most efficient combination of transportation modes that the travelers have to use given certain traffic and transit service conditions. Larger and more complex networks of motorized and non-motorized modes with stochastic properties will be investigated in subsequent work.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".