Collisions on local roads: model development and policy level scenario analysis
Bibliographic record
Abstract
The problem of collisions on local roads has received little specific attention despite the considerable number of such collisions that occur each year. First part of this study identifies the factors that influence local road collision frequency at traffic analysis zone (TAZ) level with a particular focus on the planning and policy related variables. The City of Calgary is used as a case study, where we focus on the impacts of land use, demographic characteristics, and travel characteristics. We also investigate the effects of some key transportation planning parameters for which there have been very limited studies, including the number of personal and commercial trips and the employment numbers in various categories. This study examines the impact of the number of trips made by automobile versus more sustainable transport modes like transit, walking, and biking for personal travel. It also examines the impact of commercial truck movement on the number of collisions on local roads in a TAZ. The impact of transit-oriented development zone initiatives is explored, as is the relationship between the predominant land use type (e.g., residential, commercial, industrial) and the number of collisions on local roads. In the second part, collision prediction models were linked with regional transportation model (RTM), which is calibrated and modeled in EMME. Since the choice of transportation mode is explicitly modeled through utility functions in the RTM, the proposed approach will allow us to do scenario analysis for planning and policy level issues proactively such as impact on local collisions due to change in fuel price, parking cost, transit headway, and transit fare. Results showed that property damage only (PDO) and fatal and injury (FI) collisions decreased by 13% and 6%, respectively, when fuel price was doubled. It was also observed that PDO and FI collisions decreased by 8% and 5%, respectively, when parking cost was doubled. PDO and FI collisions decreased by 7% and 4%, respectively, when transit headway was reduced to half. When transit fare was reduced to half, PDO and FI collisions decreased by 5% and 2%, respectively. PDO and FI collisions decreased by 10% and 5%, respectively, when transit fare was set to zero. These scenario analyses demonstrate how the impact of transportation planning or policy level issues on the collision count on local roads can be incorporated in our proposed model.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".