Guidelines for Planning and Implementation of Transit Priority Measures (TPM) in Urban Areas
Bibliographic record
Abstract
In recent years there has been an increasing interest in sustainable transportation and surface transit systems. The reliability of transit systems have been a concern with the growing congestion on the roadways. To improve the performance of these systems, Transit Priority Measures (TPM) have been implemented in various jurisdictions across Canada. The overall objective of these TPM is to improve transit travel time, travel time reliability, and/or safety. However, guidelines that governed the selection and implementation of TPM were unavailable. As a result, these Guidelines were developed to create a process that practitioners may use that will aid in: Identifying the issues/concerns; Assessing the potential transit priority measures; and Implementing and monitoring the select TPM (s). There is a body of TPM applications that can meet these objectives. The TPM can be classified into three broad categories: Regulatory Measures; Transit Signal Priority; Physical Measures. The selected measure(s) should meet the majority of the principles: Safety; Delay; Disruption to Road Users; Consistency/Conspicuity; and Pragmatism. The Guidelines present a six step process for implementing TPM that includes a Decision Support Tool (DST) which guides the practitioner in the selection of the TPM. The results from the DST are preferred TPM that can be implemented on-street. (A) For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.034 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".