Adaptive Pushbutton Control for Signalized Pedestrian Midblock Crossings
Bibliographic record
Abstract
Pushbutton control is ideal for midblock crossings with low pedestrian and vehicle demand, but it causes significant interruptions to traffic flow with frequent pedestrian crossing requests. Therefore, we propose an adaptive midblock crossing control (AMCC) that minimizes the impact of the pushbutton on traffic flow while maintaining a reasonably short pedestrian wait time (PWT). We regard the midblock crossing and two adjacent intersections as an integrated system and propose two types of AMCCs—AMCC-band and AMCC-vehicle—based on different types of real-time information. AMCC-band seeks the best PWT at the midblock crossing to minimize the green band loss with downstream intersections using the signal control status of adjacent intersections. Alternatively, AMCC-vehicle leverages real-time vehicle location information [e.g., obtained from vehicle-to-infrastructure (V2I) communication, connected vehicles (CVs), or advanced sensors] to minimize the estimated number of affected vehicles. Our study tests AMCC in the software Simulation of Urban MObility (SUMO) with a two-intersection traffic network. Results show that using AMCC at a midblock crossing significantly reduces vehicle delay under a wide range of traffic conditions compared to using a fixed phase and timing (Fixed) control or a pedestrian light-controlled (Pelican) crossing. The average pedestrian delay of AMCC is slightly above Pelican but much lower than Fixed. In addition, the two types of AMCCs work equally well in reducing vehicle delay, but the AMCC-vehicle has a considerably lower pedestrian delay. The results demonstrate the advantages of AMCC in reducing vehicle and pedestrian delay and vehicle stops, improving traffic efficiency at the arterial. Furthermore, the sensitivity analysis shows that the AMCC approach is adaptive to a broad range of traffic demands. Our method extends the application scope of common pushbutton control methods. We conclude that AMCC contributes to a more traffic-efficient, more pedestrian-friendly, and safer transportation system.
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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 | 0.001 | 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".