Waiting time and headway modelling for urban transit systems – a critical review and proposed approach
Bibliographic record
Abstract
The cost associated with the waiting time that passengers incur in a public transit network is one of the main components of total transit travel cost. The cost of a unit of waiting time per passenger is higher than the cost of a unit of riding time or access time. While the assumption of half the headway as the mean waiting time has been widely used in waiting time cost estimation, it is not always a realistic assumption considering heterogeneous passengers and different types of transit services. Moreover, many studies considered the waiting times of passengers only at the origin, while waiting times can also be incurred at transfer points and the destination, the latter especially for passengers with required arrival time. After describing definitions for type of passengers and type of transit service and reasoning about proper assumptions for mean waiting time, we conducted a comprehensive survey of articles in transit operation and planning published in highly-ranked journals from 2010 to 2019 which is presented in the paper. We found that most of the reviewed articles on transit suffer from lack of clear assumptions regarding the type of service and the type of passenger, which restricts the validity of the assumed waiting time. To address these issues, we develop a comprehensive approach to determine the mean waiting time of travellers. Mean waiting time for possible combinations of heterogeneous types of passengers (who plan and who do not plan their trips) and different service type (schedule-based, frequency-based, high-frequency and low-frequency) are developed. In addition, we critically review the waiting time considered in previous studies for a single route case (uniform headway with reliable service). The proposed comprehensive approach could be utilised in transit studies to better model the transit use which subsequently results in better designs and more efficient operations.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".