An Agent-Based Approach to Emission-Aware Modal Assignment Strategies in Urban Mobility
Bibliographic record
Abstract
Urban mobility systems face growing challenges in balancing individual travel preferences with the need for environmental sustainability. This work proposes a simulation-based coordination mechanism for intelligent traveler assignment using a Multi-Armed Bandit (MAB) approach. In the proposed model, a central coordinator agent learns optimal priority orders for assigning travelers to multimodal paths across a city network, aiming to reduce transport emissions. Each traveler has origin-destination and mode preferences, and chooses paths based on a personalized utility function. The coordinator iteratively adapts its assignment strategy using an$\varepsilon$-decay exploration, guided by emission-driven rewards and regret-based updates. Extensive empirical assessment across diverse city layouts and population sizes demonstrates the framework's ability to reduce emissions and encourage shifts away from most pollutant modes. Also, behavioral incentive scenarios (e.g. free public transport fares and increased fuel costs) suggest to influence travel behavior, with results varying due to network structure and population scale. The findings validate the MAB-based coordination as a viable tool for advancing sustainable urban mobility.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".