One-Shot Traffic Assignment with Forward-Looking Penalization

TL;DR

This paper presents METIS, a novel one-shot traffic assignment algorithm that improves route efficiency and reduces CO2 emissions by incorporating real-time traffic conditions through edge penalization and informed scoring.

Contribution

Introduces METIS, a cooperative one-shot traffic assignment method that effectively integrates real-time traffic data for better route optimization.

Findings

Reduces CO2 emissions by up to 46% in Rome

Significantly improves trip distribution in tested cities

Maintains low computational time

Abstract

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.