Long-term Urban Road Restrictions During Special Events
Bibliographic record
Abstract
Numerous special events and festivals (i.e. Celebrate Yonge, BuskerFest, Summer in the Village, etc.) occur annually throughout the City of Toronto, notably within the downtown core, to provide enhanced and extended pedestrian realms (which is a desirable urban condition since pedestrian needs and movements are significantly higher than vehicular movements in the downtown urban core area). These events create a desirable attraction for additional walking and cycling traffic (active modes of transportation); however, there is the need for partial or full road closures along arterial road networks to accommodate these events. This results in displaced vehicular traffic as ambient traffic patterns are required to detour along alternate adjacent routes. In order to facilitate the implementation of these public realm enhancement projects, a transportation program was developed to plan, implement, and monitor the impacts of these projects. This was especially important given the emphasis on multi-modal and sustainable transportation planning wherein transportation planning must address all vehicular, cycling and pedestrian travel modes. To quantify the magnitude of the impacts in traffic patterns during these special events, turning movement traffic counts (TMCs) were conducted during Friday AM and PM peak periods for pre-event and during-event conditions along the road network within the study area. TMCs included separate counts for vehicles, cyclists, and crossing pedestrians. Screenline and link analysis was undertaken to review the vehicular, bicycle, and pedestrian volumes to determine if there were notable changes in the traffic patterns along the closed road and in the study area as a result of the event and the resultant lane reductions or full road closure. Additionally, a Traffic Management Plan (TMP) was prepared detailing the temporary detour route, and pavement marking and signage plan. Development of the TMP took into consideration the impacts of how loading/delivery service, waste management, and emergency vehicles would be accommodated, along with providing for thematic delineation and protective barriers (such as planters, and Muskoka granite armour stone, respectively) between the enhanced pedestrian zones and the vehicular travel lanes. Post-event documentation was prepared to summarize the overall transportation program, identify impacts and lessons learned, and to be used as a tool for planning future long-term seasonal and permanent (full or partial) road closure events. A key element of the documentation was the framework to re-allocate future right-of-way space from an auto-oriented configuration to better reflect the pedestrian usage of the area in a new balanced mix of travel mode space.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".