Eco-routing system reduced travel distance by 5.18 percent in a Cleveland, Ohio network and 5.53 percent in a Columbus, Ohio network.

Two eco-routing algorithms developed and tested on networks in Cleveland and Columbus, Ohio.

January 2014

Summary Information

This study develops eco-routing algorithms and investigates and quantifies the system-wide impacts of their implementation. Two eco-routing algorithms are developed: one based on vehicle sub-populations (ECO-SFA) and one based on individual drivers (ECO-IFA).


To quantify the system-wide impacts of eco-routing strategies, the study utilized INTEGRATION software, which is a microscopic traffic assignment and simulation software. Both eco-routing algorithms initially assigned vehicles based on fuel consumption levels for travel at the facility free-flow speed. Subsequent fuel consumption estimates are then refined based on experienced of other vehicles within the same class. This stochastic, multi-class, dynamic traffic assignment framework was demonstrated to work for various scenarios. This study also quantifies the system-wide impacts of implementing a dynamic eco-routing system, considering various levels of market penetration and levels of congestion in downtown Cleveland and Columbus, Ohio.


Fuel savings achieved through eco-routing systems are sensitive to the network configuration and level of market penetration of the eco-routing system. The results also found that eco-routing systems typically reduce vehicle travel distance but not necessarily travel time. For example, the eco-routing system reduced the travel distance by 5.18 and 5.53 percent for the Cleveland and Columbus networks, respectively. The results also demonstrated a 4.8 and 3.21 percent increase in the average travel time for the Cleveland and Columbus networks, respectively.

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Developing an Eco-Routing Application

Author: Rakha, Hesham and Ahn, Kyoungho

Published By: U. S. Department of Transportation

Source Date: January 2014



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Typical Deployment Locations

Metropolitan Areas


Eco-routing, Eco-driving, fuel consumption estimation, transportation network emission modeling, navigation

Benefit ID: 2017-01225