Regional impact of Montreal's new highway toll bridge on road traffic and road safety
Bibliographic record
Abstract
In May 2011, a toll bridge was opened to public on Highway 25 to provide a new link connecting the islands of Montreal and Laval. The objective of this infrastructure was to relieve the road network of traffic by providing an additional alternative route for a subgroup of origins and destinations located on the Island of Montreal and its neighboring cities. This research examined the impact of the new infrastructure on traffic redistribution and safety conditions. In order to complete this work, multiple data sources were used, among others, such as traffic counts before and after the bridge opening, accident data, and the transportation model of the region of Montreal. The first part of the research examined the evolution of traffic counts using different indicators and a linear regression technique. The second part presents the development of collision prediction models used to assess the safety impact of the infrastructure. In addition, a tool was developed to automate the collision estimation process for different network scenarios. Considering the traffic impact, the average daily volume indicator showed that the two most impacted bridges by the new Highway 25 toll bridge were the Highway 40 Charles-de-Gaulle (CDG) Bridge (-14% of the average daily traffic) and the Pie-IX Bridge (-11% of the average daily traffic), which are the immediate adjacent bridges with respect to the new infrastructure.These bridges were also examined for each direction of traffic; it was found that the most important traffic level reductions were observed on the peak traffic directions. Examining the evolution in hourly volumes, it was noted that the most impacted periods of the day were the peak periods, where traffic conditions are worse than the other periods of the day. The average hourly traffic indicator also presented a shift of some off-peak trips to the peak periods on the Highway 40 CDG Bridge. The last method employed to assess the traffic impact of the new infrastructure was based on a linear regression and aimed to integrate the effects of different variables such as the temperature, precipitation, and gas price. It was found that temperature and precipitation had positive and negative correlations with traffic volumes, respectively.Regarding the safety impact of the Highway 25 toll bridge, the negative binomial regression model was used to estimate collision frequencies for vehicle-vehicle and vehicle pedestrian collisions on links and intersections. The statistical model's results predicted a change in the collisions' pattern matching the change in the traffic pattern following the opening of the new bridge. Considering a constant demand, the overall impact of the new infrastructure was found to have a positive effect on safety since the total number of link and intersection collisions was reduced. However, the 2016 traffic demand increased the collision frequencies for both types of collisions on links and intersections. The Collision Estimation Tool was also developed and is capable of estimating collision frequencies for a road network and comparing the collision estimates of different scenarios. The applicability of this tool was demonstrated through its use for the Highway 25 toll bridge impact assessment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".