Experimental Design for Measuring Operational Performance of Truck Parking Terminal Using Simulation Technique
Why this work is in the frame
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Bibliographic record
Abstract
The paper presents the performance analysis of a well-designed truck parking terminal, which is planned for regulating truck traffic over a commercial port. The designed truck parking terminal is modeled using microscopic traffic simulation, which is validated based on the movement of vehicles to the parking bays. After validation, various scenarios were created to evaluate parking terminal performance by varying the parking volumes and number of operational parking bays. The operational efficiency of the parking terminal for design scenarios was evaluated using parking performance measures that included parking load, average parking duration, parking turnover, and load-to-capacity ratio (parking index). For the design peak load of 4,200 vehicles/day with a uniform arrival rate, the operational efficiency was found to be about 73%. Interestingly, it was observed that with an increase in the number of operational parking bays, the parking efficiency decreased for the given volume level. Considering this phenomenon, a methodology was developed to identify the optimum number of parking bays under varying demand-supply scenarios. The developed methodology can help identify the optimum number of parking bays for existing and future (expansion) conditions. Furthermore, this study highlights the importance of using simulation in evaluating operational and design aspects of transportation facilities, where the need for repeated empirical observations is eliminated. As such, this study should be of interest to traffic engineers and practitioners interested in the efficient operation of parking terminals.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 it