“Grand Paris Express”, the urban mobility board game, and the value of simulation tools in urban decision-making.
Bibliographic record
Abstract
Mobility is a critical subject in today’s environmental and social context. On the one hand, transportation represents almost a quarter of Europe's greenhouse gas emissions and is the main cause of air pollution in cities. On the other hand, mobility plays a crucial role in addressing social inequality and promoting inclusivity. In order to solve these issues, smart mobility and more precisely the intelligent use of data will be essential. Access to this data solutions is crucial to build an inclusive and democratic transportation system. The Urban Mobility Board Game aims to offer an intuitive understanding of agent-based simulation. It has been elaborated with the supervision of the Anthropolis Chair at IRT SystemX and the Industrial Engineering Laboratory from Université Paris Saclay. The Urban Mobility Board Game is a collaboration game. It allows its players to personify mobility stakeholders with the mission to build the best transportation network to optimise the trip of a set of passengers that move around the board. The team wins points by reducing CO2 emissions in the city, saving money, and making sure the passengers get to their destination on time. Those are, indeed, the three main indicators the simulators use to evaluate their scenarios. By offering its players a fun and interactive tangible platform, the game conveys how simulations are an innovative and useful tool for observing and improving urban mobility systems. Furthermore, it was designed to train people to better think during their decision- making process and acquire new skills. The game is meant to be used for educational purposes, such as a first introduction to agent-based simulation. It might also be employed in the working environment as a tool for simulators to better convey the utility of their work
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".