Eco-routing protocol reduces vehicle emissions in heavy traffic
Routing Around the Queue: An Evaluation of Eco-Routing Protocol on different Traffic Scenarios
Networking and Internet ArchitectureComputers and Society
Summary
Traffic jams cause cars to waste fuel and produce more pollution. The authors studied a system called ErouVe that helps cars find routes that lower both travel time and CO2 emissions by sharing information from other vehicles. They found it works better in busy traffic when congestion causes delays, helping drivers save fuel and reduce pollution. They also improved the system by fixing issues like outdated info and confusing route changes to make it work more reliably.
What this means in practice
- •For traffic management teams: Use ErouVe’s improved eco-routing methods to reduce vehicle emissions on congested urban routes by coordinating traffic information sharing.
- •For automotive navigation developers: Integrate crowd-sourced, per-segment CO2 and travel time data for adaptive route guidance that reduces fuel consumption under real-world traffic conditions.
Authors
Leandros Maglaras, George Boustras, Azidine Guezzaz, Naghmeh Moradpoor
Abstract
We evaluate ErouVe, a vehicular eco-routing protocol in which roadside units (RSUs) collect per-segment travel-time and CO2 reports from passing vehicles and answer route requests with the lowest-cost alternative, across a range of road architectures and traffic scenarios. We conduct the evaluation with a working implementation of the protocol inside Veins, adapted to drive SUMO's own router rather than a hand-rolled shortest-path search, on a network offering a short, signal-controlled route and a longer, signal-free bypass, under two traffic scenarios. This evaluation reveals clear strengths and weaknesses of the protocol. Under light, uncongested traffic, ErouVe's fleet-wide effect is light. Under traffic dense enough to stress the signalized route's throughput and produce a real, growing queue, the same protocol succeeds a medium fleet-wide CO2 reduction, with diverted vehicles individually saving even more fuels. We use these findings to propose three targeted corrective methods; a knowledge-gated cost-acceptance rule, a diversion-locking rule, and a distance-normalized CO2 cost function, that respectively address a cold-start knowledge asymmetry, decision flapping under noisy crowd-sourced data, and a per-second CO2 metric that rewards idling. Each of these weaknesses are generic pitfalls for any crowd-sourced routing signal and all proposed methods measurably restore protocol effectiveness.