Eco-Routing based on a Data Driven Fuel Consumption Model

TL;DR

This paper develops a data-driven nonparametric fuel consumption model and proposes eco-routing algorithms that reduce fuel use while balancing travel time, based on six months of city driving data.

Contribution

It introduces a novel nonparametric fuel consumption model and eco-routing algorithms that optimize fuel efficiency with minimal travel time increase.

Findings

Eco-routing reduces fuel consumption compared to shortest distance and shortest time routes.

Travel-time-constrained eco-routing maintains most fuel savings with minimal travel time increase.

The model and algorithms are validated using real-world driving data from Ann Arbor.

Abstract

A nonparametric fuel consumption model is developed and used for eco-routing algorithm development in this paper. Six months of driving information from the city of Ann Arbor is collected from 2,000 vehicles. The road grade information from more than 1,100 km of road network is modeled and the software Autonomie is used to calculate fuel consumption for all trips on the road network. Four different routing strategies including shortest distance, shortest time, eco-routing, and travel-time-constrained eco-routing are compared. The results show that eco-routing can reduce fuel consumption, but may increase travel time. A travel-time-constrained eco-routing algorithm is developed to keep most the fuel saving benefit while incurring very little increase in travel time.