Eco-Drive Technology, Human Factors, and Environmental and Economic Benefits

Author:

Khan Ata M.1,Kent Greg2,Choudhry Omar2

Affiliation:

1. Civil and Environmental Engineering, Carleton University, Ottawa, Ontario, Canada

2. Traffic Services, City of Ottawa, Ontario, Canada

Abstract

The objective of eco-drive technology is to reduce fuel consumption and resulting emissions using advances in communication and traffic control technologies with capability to support infrastructure-to-vehicle connection in a signalized network. On the human factors side, there is growing interest across the world in advising drivers to take eco-drive actions by effectively using the green phase of the signal cycle time to save fuel and reduce emissions. This paper describes a large-scale real-world research project in Ottawa (Ontario, Canada) on this subject. The technology and methods that support the green light optimized speed advisory (GLOSA) system were refined and all 1,178 traffic signals in the city were equipped to connect with a fleet of vehicles. Field study data were analyzed for speed trajectories, fuel consumption, and GLOSA compliance. Greenhouse emissions and fuel cost changes were computed. An anonymous questionnaire study investigated driver perception of the usefulness of the signal data displayed on an in-vehicle unit as advice on driving adjustment decisions made under prevailing traffic conditions. The over 65% compliance with GLOSA and the results of the driver questionnaire were mutually consistent. The fuel saving amounted to 7.6% but was adjusted to 5% because of uncertainties in daily vehicle travel. The reduction in carbon dioxide equivalent and fuel cost reported in the paper are based on a 5% adjustment. These results can be used for cost–benefit studies. Also, simulation-based research projects can verify their findings with the real-world experience reported in this paper.

Publisher

SAGE Publications

Subject

Mechanical Engineering,Civil and Structural Engineering

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Accurate Bike Routing for Lane Prediction in GLOSA Apps via Infrastructure Reference Models;2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC);2023-09-24

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