Simulation-Based Framework for Estimating Crash Modification Factors (CMFs): A Case Study for ITS Countermeasures
Bibliographic record
Abstract
The efficient movement of users and goods is the primary purpose of the surface transportation system. Roadway traffic crashes have devastating impacts on quality of life of the users as well as health of the system. While researchers are utilizing advanced computing and communication tools to reduce number of crashes on the roadways, there is still an absence of appropriate method to evaluate the safety performances of these advanced technologies in the planning stage. Development of crash modification factors (CMFs) is a standard method to evaluate the safety effect of proposed countermeasures. Though, the current practices of developing CMFs are not efficient and cost-effective in case of addressing impacts of Intelligent Transportation System (ITS) countermeasures. This study demonstrated a proof of concept of simulation-based framework for determining CMFs for ITS countermeasures. The proposed framework includes the application of traffic microsimulation model and Surrogate Safety Assessment Model (SSAM) developed by Federal Highway Administration (FHWA). The integration of these two models is suggested to estimate CMFs efficiently. However, the calibration of traffic microsimulation model and SSAM model is essential to portrait the real-world scenarios. A case study for estimating CMFs of ITS countermeasures was conducted to validate the proposed simulation-based approach. Four ITS countermeasures were considered: ramp metering, variable speed limit, junction control, and dynamic lane assignment. They were coded in traffic microsimulation environment and vehicle trajectory files were generated to import into SSAM model. After analyzing these trajectory files in SSAM tool, it was found that all proposed ITS countermeasures, except variable speed limit assignment, could reduce the number of crashes at crash prone locations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".