Effect of Transit Systems’ Long-Term Disruptions on Travel Behaviour
Bibliographic record
Abstract
Long-term transit system disruptions can have substantial negative effects on travellers. These disruptions occur due to several reasons such as transit construction projects and labour strikes. The effects can alter how travellers make trips including changes in travel routes and transport mode choices. This could lead to loss of committed transit users, since some of them may shift to using other modes that are not sustainable or environmentally friendly such as private cars, thereby increasing congestion and vehicular emissions. This field of research is underexplored in the academic literature and so is the examination of the effectiveness of mitigation strategies for this type of disruptions. To help address these gaps, this research was undertaken with the aim of achieving two objectives. The first objective is a systematic review of the contemporary academic literature to investigate the current state of knowledge on the topic and provide an aggregated view of the findings, which should help inform future research efforts. Keyword searches were performed on three research databases, and the acquired publications were analyzed. Only 19 relevant peer-reviewed articles were found. One major finding was that the provision of alternative transit options with a high transit service level coupled with the efficient dissemination of real-time travel information could increase transit use during the disruption. The second objective is a case study of the effects of a long-term transit service disruption, due to a transit construction project in Montréal, on the travel behaviour of previously regular users of the suspended commuter train line. Two datasets containing responses to an online survey that was conducted in two waves, before and during the suspension, were analyzed using descriptive statistics. Among the findings was that 71% of the respondents shifted from using transit during the disruption, many of which relied on private cars. Additionally, respondents who relied on cars were the least likely to report that they would use the new transit service after its construction is complete. This study is one of the first efforts that explore the substantial impacts of long-term closures on transit users’ choices within the context of an active COVID-19 pandemic.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".