Digital twin-based evaluation of tactical interventions for high-density pedestrian environments
Bibliographic record
Abstract
Recent urban practice has highlighted the need for safe and efficient pedestrian movement in urban settings. Urban planners and policymakers are tasked with ensuring that individuals can navigate cities while avoiding crowding and minimizing safety risks. Among the various proposed solutions to achieve these planning objectives, enhancing public space adaptability to changing circumstances, such as fluctuations in pedestrian demand and crowding, has garnered significant interest. However, constrained municipal budgets limit the feasibility of large-scale, capital-intensive upgrades to pedestrian infrastructure. As a result, there is an urgent need for a short-term, temporary, and cost-effective strategy to redesign pedestrian public space. This paper introduces a novel tactical urban planning approach, combining evidence-based urbanism. Using a campus digital twin system as an urban simulation platform, this study presents a unique evidence-based planning approach to improve pedestrian spacing on sidewalks by dispersing pedestrians without overly interfering with existing infrastructure. As interventions within existing pedestrian public spaces, one-way sidewalk systems and building entrance control are considered. Through the proposed agent-based simulation of the campus digital twin system, the optimization of their application and the demonstration of its effectiveness are achieved. The outcome of the simulations verifies that these measures can enhance pedestrian spacing by dispersing them without causing substantial adverse effects. While these findings stem from experiments conducted on a particular campus, limiting their generalizability, they still hold significance as they verify the potential for practical implementation of tactical urban planning concepts utilizing a digital twin system for urban environments.
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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 | 0.006 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".