# One-Shot Traffic Assignment with Forward-Looking Penalization

arXiv: https://arxiv.org/abs/2306.13704 · HTML: https://mlsanity.com/paper/2306.13704/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2023.

## Built on by (most cited first, 2 of 2)

- [A Data-driven Resilience Framework of Directionality Configuration based on Topological Credentials in Road Networks](https://mlsanity.com/paper/2401.07371/) (2024)
- [The Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendation](https://mlsanity.com/paper/2504.07911/) (2025)

## Abstract (arXiv)

Traffic assignment (TA) is crucial in optimizing transportation systems and consists in efficiently assigning routes to a collection of trips. Existing TA algorithms often do not adequately consider real-time traffic conditions, resulting in inefficient route assignments. This paper introduces METIS, a cooperative, one-shot TA algorithm that combines alternative routing with edge penalization and informed route scoring. We conduct experiments in several cities to evaluate the performance of METIS against state-of-the-art one-shot methods. Compared to the best baseline, METIS significantly reduces CO2 emissions by 18% in Milan, 28% in Florence, and 46% in Rome, improving trip distribution considerably while still having low computational time. Our study proposes METIS as a promising solution for optimizing TA and urban transportation systems.