Sustainable Transportation

By Poulad Moradi, Joachim Arts from University of Luxembourg Centre for Logistics and Supply Chain Management, and Dr. Josué C. Velázquez Martínez from MIT Center for Transportation & Logistics.

The reduction of fuel consumption has become increasingly more important as its price and volatility continue to rise. This objective aligns with the sustainable efforts to reduce CO2 pollution.

Recent research has found that high-resolution elevation data reshapes the paths of diverse trucks for maximum fuel efficiency. Then, the greenest path swiftly departs from the shortest route, reaching its optimal state even with moderate payloads. Leveraging a refined Comprehensive Modal Emission Model (CMEM), we’ve transitioned from static to dynamic optimization, adapting to terrain elevations.

In flat road networks, fuel efficiency aligns with the shortest route and constant speed. Introduce elevation changes, and the greenest path deviates, adapting its optimal speed dynamically. Also, the analysis identifies road gradient fluctuations and relative elevation as key factors shaping a sustainable future.

This groundbreaking discovery reveals the greenest path converges to an asymptotic state with increasing payload, achieved remarkably with a finite load. Numerical experiments across 25 major cities on 6 continents showcase the CO2 reduction prowess of our dynamic speed and greenest path policies.

Moradi, Poulad, Joachim Arts, and Josué Velázquez-Martínez. “Load Asymptotics and Dynamic Speed Optimization for the Greenest Path Problem: A Comprehensive Analysis.” arXiv preprint arXiv:2306.01687 (2023).