Disruption, Congestion and Mitigation: Charaterisation of Strategic Road Infrastructure Using Partial Itinerary Data
Bibliographic record
Abstract
In large urban areas, high-capacity transit and road infrastructure play a crucial role in the spatiotemporal distribution of economic and social activities. In many cities, the subway is the critical component of the public transit system just as freeways form the effective backbone of the road network. In the case of island cities like Montreal, bridges are also essential to the proper functioning of the transportation system as a whole. As such, subways, bridges and freeways can be considered “strategic” transportation infrastructure since the disruption of just one of them has wide-reaching consequences. It is therefore important, from both long-term planning and operational perspectives, for transportation authorities to identify strategic infrastructure and to have good knowledge of its users’ travel patterns. In Montreal, methods of analysing public transit usage patterns based on travel survey data have long been used for planning and operational financing purposes. However, a similar methodology has yet to be adopted for roads. This paper presents a methodology for thoroughly characterising the users of strategic road infrastructure (bridges and freeways) based on data contained in a large-sample household travel survey. The Montreal travel survey asks all respondents who completed their trip by driving a car which major bridge or freeway was used. The 2008 survey contained roughly 70,000 trips with at least one bridge or freeway declared. Around 60,000 of these declarations could be validated using a constrained trip assignment algorithm applied to a large and detailed network (117,000 links). Adopting a totally disaggregate approach, the algorithm transforms the bridge and freeway declarations into complete itineraries while preserving the socio-demographic attributes of each traveller. These results can be used to analyse strategic road infrastructure from multiple perspectives: the detailed characterisation of the “clientele”, an estimation of their travel consumption, analysis of congestion and road pricing, and the design of mitigation measures – including alternative public transit options – in the event of closure or failure. An interactive visualisation tool forms the basis of these investigations. The method is based on a travel survey but could be adapted for emerging passive data sources that provide partial itinerary information such as GPS traces, automatic toll collection systems, mobile device applications and so on.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".