Traffic Safety and Operations on Shared-Access Facilities: An Urban Arterial Case Study
Bibliographic record
Abstract
In designing intersection signal timing plans transportation professionals have to account for two main objectives that often times are antagonistic: to ensure good flow of traffic and to maintain a high level of safety for all road users. Although the two objectives do not necessarily exclude one another, identifying the compromise that provides the best traffic and safety conditions for all road users is not a straightforward exercise. In this study, the authors propose a methodology that can be used to determine the best signal timing of urban intersections by reaching a desired equilibrium between the two objectives. The methodology is using a combined delay-safety (DS) performance measure in an artificial intelligence decision-making framework. A case study of an urban arterial with a newly built bicycle path in downtown Montreal, Quebec, was investigated using a microscopic traffic simulator, VISSIM. A multilayer perceptron (MLP) neural-network uses several traffic flow parameters as input information to identify, out of three possible configurations (i.e., independent signals, coordinated for automobile progression, and coordinated for bicycle progression) what type of signal timing plan yields the best tradeoff between automobile delay and safety of nonmotorized users. Based on several levels of input flows from real-world and simulated traffic data a large pool of possible input/output combinations was used to train and test the MLP neural network with two hidden layers. It was found that for 99.8% of the tested cases the neural network identifies correctly the configuration of signal timing plan that yields the lowest DS value.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".