Bibliographic record
Abstract
Cycling has been considered as a healthy, environmentally friendly, and economical alternative mode of travel to motorized vehicles (especially private motorized vehicles). However, bicycles have often been neglected in the transportation planning and travel demand forecasting modeling processes. The current practice in modeling bicycle trips in a network is either nonexistent or too simplistic. Current practices are simply based on the all-or-nothing (AON) assignment method using single attributes such as distance, safety, or a composite measure of safety multiplied by distance. The purpose of this paper is to develop a two-stage traffic assignment model by considering key factors (or criteria) in cyclist route choice behavior. As an initial effort, the first stage considers two key criteria (distance-related attributes and safety-related attributes) to generate a set of nondominated (or efficient) paths. These two criteria are a composite function of subcriteria. Route distance consists of link distances and intersection turning penalties combined to give the distance-related attribute, while route safety makes use of the bicycle level of service (BLOS) measure developed by the Highway Capacity Manual (HCM) to determine the safety-related attribute. Efficient paths are generated based on the above two key criteria with a biobjective shortest path algorithm. The second stage determines the flow allocation to the set of efficient paths. Several traffic assignment methods are adopted to determine the flow allocations in a network. Numerical experiments are then conducted to demonstrate the two-stage approach for bicycle traffic assignment. Overall, the results of the Winnipeg network demonstrate the applicability of the two-stage bicycle traffic assignment procedure with the flexibility of using different criteria in the first stage to generate efficient paths and different traffic assignment methods in the second stage to allocate flows.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".