eco-driving | National Center for Sustainable Transportation

"Actual Results May Vary": A Behavioral Review of Eco-Driving for Policy Makers

Research Product Type

White Paper

This white paper presents a behavioral framework to understand and measure the impacts of eco-driving.

A Meta-Analysis of Eco-Driving Feedback Research

Principal Investigator Angela Sanguinetti, Ph.D.
University of California, Davis

This white paper presents a statistical meta-analysis of eco-driving feedback studies, with special focus on feedback interventions and how they impact fuel economy.

Project Status

Complete

An Adaptive Strategy for Connected Eco-Driving under Uncertain Traffic and Signal Conditions

Research Product Type

Research Brief

This brief summarizes research findings from the University of California, Riverside, on a prediction-based, adaptive connected eco-driving strategy to account for real-world uncertainties.

Average Impact and Important Features of Onboard Eco-Driving Feedback: A Meta-Analysis

Research Product Type

Associated Publication

This paper presents a statistical meta-analysis of eco-driving feedback studies in order to determine a weighted estimate of the average impact of feedback on fuel economy and explore potential moderators of its effectiveness.

Average Impact and Key Features of Onboard Eco-driving Feedback

Research Product Type

Policy Brief

This policy brief reviews various eco-driving studies to determine eco-driving's impacts on fuel economy and to evaluate most effective strategies.

Dataset: Deep Learning-based Eco-driving System for Battery Electric Vehicles

Research Product Type

Data

This dataset stems from the project "Deep Learning-based Eco-driving System for Battery Electric Vehicles".

Dataset: Speed Trajectory Data from Adaptive Eco-driving Applications

Research Product Type

Data

The eco-approach and departure (EAD) application for signalized intersections has been proved to be environmentally efficient in a Connected and Automated Vehicles (CAVs) system.

Deep Learning-Based Eco-Driving System for Battery Electric Vehicles

Principal Investigator Guoyuan Wu
University of California, Riverside

This project develops an algorithm to address technological research gaps in eco-driving.

Project Status

Complete

Deep Learning-Based Optimization of Eco-Driving Strategies with Connected and Autonomous Electric Vehicles on Transportation Networks

Principal Investigator Fengxiang Qiao, Ph.D.
Texas Southern University

This project will help both transportation and environmental agencies at all levels, and car manufacturers, to understand the design, operation, and impacts of optimal eco-driving strategies. The project will provide urgent science and test-based input to inform policy and practice development.

Project Status

In Progress

Developing Agent-Based Distributed Cooperative Vehicle-Infrastructure Systems in the Connected and Automated Vehicle Environment

Research Product Type

Dissertation / Thesis

The rapid development of our transportation systems has brought much convenience to our daily lives, while also introducing various issues related to safety, mobility, and environmental sustainabil