A comparison between PARAMICS and VISSIM in estimating automated field‐measured traffic conflicts at signalized intersections
Bibliographic record
Abstract
Summary The main objective of this study is to investigate the relationship between field‐measured conflicts and simulated conflicts estimated from microsimulation model (PARAMICS) using the surrogate safety assessment model. An urban signalized intersection was selected for analysis. Automated video‐based computer vision techniques were used to identify field conflicts. The applicability of a two‐step model calibration procedure applied to VISSIM in a recent study was investigated using PARAMICS. In the first calibration step, the PARAMICS model was calibrated to ensure that the simulation gives reasonable results of average delay times. The second calibration step used a genetic algorithm procedure to calibrate PARAMICS parameters to enhance the correlation between simulated and field‐measured conflicts. Finally, the results obtained from PARAMICS were compared with results obtained from VISSIM. The comparison included three aspects: (i) the car‐following model and safety‐related parameters; (ii) the correlation between simulated and field‐measured conflicts; and (iii) the conflict spatial distributions. The results show that the default simulation model parameters give poor correlation with the field‐measured data, and therefore, using simulation models without a proper calibration should be avoided. Overall, good correlation between field‐measured and simulated conflicts was obtained after calibration for both models, especially at higher time‐to‐collision (TTC) values. At TTC threshold of 1.5 s, PARAMICS overestimates the number of conflicts and VISSIM underestimates it. Both models overestimated the number of conflicts at TTC threshold of 3.00 s. There were major differences between field‐measured and simulated conflicts spatial distributions for both simulation models. This indicates that despite the good correlation obtained from the calibration process, both PARAMICS and VISSIM do not capture the actual conflict occurrence mechanism. Copyright © 2016 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".