Operational Impact of the Through-Traffic Signal Prioritization for Heavy Commercial Vehicle Platooning on Urban Arterials
Bibliographic record
Abstract
This study investigated the operational impact of heavy commercial vehicle (HCV) platooning on urban arterials. HCV platooning is an important application of vehicle-to-vehicle (V2V) technology, with urban arterials facilitating an essential component of HCV movements when picking up and delivering goods. HCV platooning has the potential to reduce fuel consumption and emissions. Moreover, the increasing HCV driver shortage problem can be alleviated if the vehicle following behind a lead vehicle can function without a driver by using autonomous technology enabling Society of Automotive Engineers Level 4 or higher. PTV VISSIM was used to develop a set of micro-simulation models that investigated the impact of traffic signal priority (TSP) and low levels (0%, 5%, and 10%) of HCV platooning. The performance measures include travel time and the number of stops. With the existing traffic control system, HCV platooning increased travel time and increased the number of stops for all vehicles including passenger cars and HCVs. TSP with 5% HCV platooning improved travel time and decreased the number of stops for all vehicles. TSP with 10% HCV platooning, however, only decreased travel time and the number of stops for passenger vehicles. The results suggest that a higher penetration rate of HCV platooning may create significant delays and overwhelm the traffic system even with the assistance of TSP. The findings of this study highlight the potential for TSP to mitigate the impact of HCV platooning on traffic congestion. However, the TSP system may not be a panacea that works for all traffic compositions.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".