Modeling and Simulation of Transportation Systems: From Theory to Practice
Bibliographic record
Abstract
This paper presents a broad perspective on the modeling, optimization, and simulation in the planning, designing and controlling of modern transportation systems discussing its importance for operational performance and decision making. Highly sophisticated analytical tools can no longer be disregarded when considering the development of low-cost, environmentally friendly and sustainable transport systems in urban centers and to satisfy the increasing mobility demand at a global level. The paper begins by providing the theoretical background on transport modeling, which goes from macroscopic flow models to agent-based simulations. It further discusses the combined use of optimization and simulation tools for infrastructure project assessment and traffic management in real time. With a structured four-axis approach, the paper analyses: (1) Fundamental concepts of transport system modeling; (2) Optimization and Simulation in infrastructure projects; (3) Socio-economic and environmental impacts with Moroccan case studies; and (4) The transformative role of digital twins and artificial intelligence. Moroccan projects also point the way how strategically planned model-supported infrastructure investment can support economic growth and improve urban living, for instance in Casablanca and Rabat. However, it also reveals long-term issues of data governance, social equity and institutional capacity. This and other tools discussed have enormous potential but their successful use is dependent on strong data ecosystems, the breaking down of disciplinary silos and a firm commitment to equitable and sustainable urban development. We also elaborate remaining challenges in those models, including uncertainty treatment and algorithmic fairness, along with future research directions on filling the gaps which can help promote the development of new intelligent/ humanist transportation systems.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".