Adaptive route choice model for public transit systems: an application of Markov decision processes
Bibliographic record
Abstract
Over the past couple of decades the advancements in the areas of information and computational technology allowed for a variety of intelligent transportation systems developments and deployments. This study investigates an advanced traveler information system (ATIS) and (or) an advanced public transit system (APTS) adaptive and real-time transit routing component. The proposed methodology is applied to bus routes with fixed, predefined bus line alignments. It is shown that routing buses on such systems can be modeled in real-time by employing an associated Markov chain with reward model to minimize the impact of congested traffic conditions on the travelers and the overall operation cost of the transit system. A case study using a traffic and transit data from a real-world bus line was used to apply the proposed bus routing approach. It was found that under certain traffic congestion conditions buses should be re-routed to minimize their travel time and the associated system costs. The hypothetical congestion scenarios investigated show that individual bus travel time delays range between 50 and 740 s when the proposed adaptive routing is employed. The proposed methodology is also suitable for application to transit systems that run on a demand-adaptive basis (the bus line alignment changes with the travelers demand). Additional calibration and future integration of the system into specific ATIS and (or) APTS user services will be investigated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".