Digital Twins as Decision Support Systems for Sustainable Smart Cities: a Traffic Analysis Perspective
Bibliographic record
Abstract
Digital Twin (DT) technology has revived the interest in the \nSmart City concept, allowing to build accurate digital \nmodels of complex phenomena, serving as decision-making \nsupport systems for different urban stakeholders. In this \nthesis, we focus on main DT building blocks allowing for \nefficient analysis of the urban environment. The technology \ncould be employed as a simulation tool to analyze \nenvironmental impacts, exploring alternate scenarios. \nGrounding the study, we explore the use of the technology \nvia two use-cases: Acc2Twin for transport packaging and \nParallelTwin for traffic analysis — focusing on road network \npartitioning. The Acc2Twin cases showcases a data processing \npipeline aimed at building a continuous road network (path) \ntopology, given in input to a first-principles DT, tasked \nwith simulating stretch forces exerted on palletized \nproducts during transportation. Acc2Twin interpolates route \ndata to an arbitrary precision to allow for the replication \nof real transport conditions. The second use-case, \nParallelTwin, aims at optimizing parallel simulations of \nvehicular traffic with the SUMO software package, reducing \nsimulation time, and enhancing system performance without \naltering the core pre-existing simulation program. We report \non PartitionTwin’s framework, operational mechanisms, \ncommunication protocols, and the METIS algorithm for \nefficient graph partitioning, researching efficient \npartitioning weights to obtain a balanced simulation \nworkload.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".