Novel Methodology for Safety and Economic Evaluation of Red-light Cameras at Signalized Intersections
Bibliographic record
Abstract
Red-light running is a phenomenon that has led to frequent collisions causing fatalities, injuries, and property damage.Red-light cameras (RLCs) have been used in an attempt to reduce the frequency of these collisions.The main objectives achieved in this thesis are: (1) The site-selection bias of traditional methodologies were corrected by using a novel methodology (propensity score matching); ( 2) The fixed and random-effect panel regressions were utilized to account for the spatial and temporal correlations in the data; (3) The identification of how RLC effectiveness varies by site characteristics through the use of interaction terms; (4) The determination of spillover distances and times resulting from the presence of RLCs; (5) The benefit-cost methodology supported by economic analysis and sensitivity analysis using the novel methodology; and (6) The establishment of guidelines for effective implementation of the RLC treatment.The research used field data from the City of Ottawa (Canada) involving 34 RLC intersections and 14 control intersections observed for the period 1999-2012.The results from this thesis indicate a consistent significant reduction in angle and turning movement collisions of 19% and 21%, respectively, and an increase in rear-end and sideswipe collisions of 12% and 6.7%, respectively.The magnitude and the direction of these effects are comparable to results of previous studies in the literature.The benefit-cost analysis, based on social costs of collisions avoided, the spillover effects and fine revenue, yielded an overall annual cost savings to the community of over $4.4 million CAD across the 34 RLC intersections; with a benefit-cost ratio of 4.50.An extended cost sensitivity analysis was incorporated to quantify the robustness of the base-case conclusions.Figure 1.1 -Evaluation Framework Literature review of RLC enforcement Data collection and preparationTraffic safety data Traffic safety modeling Comparison of results and model selection Guidelines for practical implementation Economic data Economic modeling Intersection safety Impact of RLC programs Statistical techniques for RLC safety evaluation Economic effect of RLC Identify limitations in the literature RLC data Collisions data Traffic data Geometric data Contact with City of Ottawa RLC costs Collision costs Total net costs Total net benefit Benefit-cost ratio Variables: Collisions Traffic Geometric Methods: Cross-sectional Observational before-after Case-control Interactions Spillover effects Sensitivity analysis
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".