Title

Estimating the impact of green light optimized speed advisory (Glosa) on exhaust emissions through the integration ofvissim and moves

Document Type

Article

Publication Date

1-1-2020

Subject Area

ARRAY(0x557a947353f0)

Abstract

U.S. Environmental Protection Agency (EPA) states that transportation is the second leading source for air pollution. Therefore, any improvement in transportation technology can bring substantial benefits by reducing the vehicle exhaust emissions. Recently, connected vehicle (CV) technologies have become increasingly popular since their penetration to the market can bring crucial benefits. This makes it necessary to study their impact in a simulation environment to assess their benefits before their actual implementation. As such, objectives of this paper are as follows: (a) to provide a framework that can convert VISSIM vehicle trajectory output to an input for EPA’s Motor Vehicle Emission Simulator (MOVES), and (b) predict the impact of CV technologies on vehicle emissions developing an algorithm that makes benefit of a vehicle-to-infrastructure (V2I) communication application, namely Green Light Optimized Speed Advisory (GLOSA). For this purpose, an intersection is built in VISSIM, and GLOSA is implemented on a major leg of this intersection. The output data is also converted to a MOVES input file developing a new algorithm, named operating mode calculation algorithm (OMCA). Results of MOVES simulation for CO, NOx, PM2.5 and PM10 show that GLOSA application has a huge potential of reducing vehicle emission in the vicinity of traffic lights as it can lead to up-to 51.2% emission reduction. In addition, vehicle stop delay and number of stops were also reduced by 83.9% and 87.9%, respectively. Findings of the study can help understand the effect of stop-and-go driving operations on the exhaust emissions, and quantify the potential operational and environmental benefits of CVs in this context.

Publication Title

Advances in Transportation Studies

Volume

52

First Page

5

Last Page

22

Digital Object Identifier (DOI)

10.4399/97888255370311

ISSN

18245463

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