Optimizing Bus Stop Spacing Using the Simulated Annealing Algorithm with Spatial Interaction Coverage Model
Bibliographic record
Abstract
Public transportation systems, in particular, bus systems, play an essential role in the process of urbanization. Typically more bus stops enable more people to access the bus whereas lower the efficiency of bus system. This study uses a Spatial Interaction Coverage (SIC) model to identify and remove redundant bus stops while maintain the overall success of the whole bus system. The SIC model aims to model the relationship between demand points and bus stops. It takes factors such as the distance and the attractiveness of each bus stop into consideration. The simulated annealing algorithm is then applied with the SIC model to find the optimal combination of bus stops. By applying the SIC model to the iXpress 202 route in Kitchener-Waterloo region, it can effectively identify the number of stops to maintain and remove redundant stops. The bus operation efficiency can be increased by 7.28% after optimization. The SIC model provides a reliable method of modelling the interaction between facilities and demands. The ability of considering the attractiveness of stops and including distance decay into the model can help the transportation agency better plan bus routes. The relationships between bus ridership and the socioeconomic variables (population, income, and age) in the study area are also analyzed.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".