Optimization of a Semiflexible Demand-Responsive Feeder System in Suburban Areas Using a Memetic Algorithm
Bibliographic record
Abstract
Traditional bus operations in suburban areas are inefficient due to their fixed routes and timetables. Since suburban operations deal with low demand spread in a large area, the service stays underused for most off-peak hours. In order to render the operation profitable and increase the number of passengers on each bus, operators reduce the frequency of the service, which results in an increase of passenger waiting time for the service. As a solution to this problem, this paper introduces a demand-responsive public bus system that aims to adjust routes and timetables of a semiflexible system to the demand for transportation. The operation still offers a reliable service like the traditional system but aims to reduce the passenger travel time. A memetic algorithm is developed to optimize this demand-responsive system. For a network with 25 bus stops served hourly by three lines and with an average demand of 20 requests per hour, the memetic algorithm is demonstrated to reduce the passenger waiting time with almost 50% in comparison with a traditional system operating in the same network with fixed routes and timetable.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".