Air pollution is a very critical non-natural hazard that adversely affects human health as well as the environment itself in the context of climate change. One of the biggest contributors to air pollution is the transportation industry. According to the U.S. Environmental Protection Agency (EPA), transportation is the second leading source for greenhouse gas (GHG) emissions, contributing to GHG emissions by 28%. Researchers and practitioners have been working on developing techniques to estimate and reduce transportation-related emissions by the help of various types of technologies. As such, this study aimed to investigate the effect of vehicle operating modes (i.e., constant running, idling, accelerating, and braking) on vehicle exhaust emissions in order to highlight the importance of occasionally disregarded factors that exacerbate the transportation-related air pollution problem. In order to accomplish this goal, this study adopted an approach involving two frequently used software for estimating emissions, namely VISSIM (a microscopic traffic simulation software) and EPA’s Motor Vehicle Emission Simulator (MOVES). The input data required for these software was collected, processed, and introduced into the models in order to estimate the emissions. First, a corridor was simulated within the VISSIM. This corridor is located in the City of Tallahassee, Florida, which is highly congested during the peak hours, and approximately 7.7 miles long, with 22 signalized intersections. Next, the outputs of VISSIM were collected and provided to MOVES by developing an integration tool. First, average speed and volume data were provided to MOVES only for the whole corridor, and VISSIM and MOVES emissions for carbon monoxide (CO) and nitrogen oxides (NOx) were compared. Note that VISSIM provides only emissions for CO and NOx. After observing the massive difference between VISSIM and MOVES emissions, the importance of using operating mode distribution file in MOVES was pointed out. To meet this end, the integration tool was enhanced to compute the vehicle operating mode distribution file based on second-by-second vehicle trajectory output. This was provided to MOVES in order obtain more accurate emission estimation results since only average speed and volume data could not provide accurate emission values disregarding the different vehicle operating modes. For this purpose, an algorithm, named as operating mode calculation algorithm (OMCA), was developed in Python 3.0 to create operating mode distribution input by using second-by-second vehicle trajectory data of VISSIM. This type of analysis focusing on the emissions of individual vehicles provided more accurate emission results. Now that these results were obtained, the focus of the thesis shifted towards analyzing the impact of vehicle connectedness on the air pollution. Two intersections of the selected highway corridor were modelled and simulated with a connected environment using one of the widely used vehicle-to-infrastructure (V2I) communication application called Green Light Optimized Speed Advisory (GLOSA). The GLOSA was implemented on the major leg of these intersections only with different Connected Vehicle (CV) penetration rates. One of the selected legs was the most congested link of the corridor. After extensive simulations, second-by-second VISSIM trajectory data were provided to OMCA, which converted them to MOVES operating mode distribution input files. Finally, MOVES was run in order to estimate carbon monoxide (CO), nitrogen oxides (NOx), primary exhaust smaller than 2.5 micrometer (PM2.5) and primary exhaust smaller than 10 micrometer (PM10) emissions. Findings of the study can aid researchers in understanding the effect of different operation modes on the exhaust emissions, understanding the effect of smoother and lower number of stop-and-go driving operations in the context of the connected vehicle impact on the exhaust emission, and quantifying the potential operational and environmental benefits of connected vehicles (CV’s). / A Thesis submitted to the Department of Civil and Environmental Engineering in partial fulfillment of the requirements for the degree of Master of Science. / Spring Semester 2019. / March 27, 2019. / Connected vehicle, Emission, GLOSA, MOVES, Simulation, Traffic / Includes bibliographical references. / Eren Erman Ozguven, Professor Directing Thesis; John O. Sobanjo, Committee Member; Ren Moses, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_710028 |
| Contributors | Karabag, Hasan H. (Hasan Huseyin) (author), Ozguven, Eren Erman (Professor Directing Thesis), Sobanjo, John Olusegun (Committee Member), Moses, Ren (Committee Member), Florida State University (degree granting institution), FAMU-FSU College of Engineering (degree granting college), Department of Civil and Environmental Engineering (degree granting departmentdgg) |
| Publisher | Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, master thesis |
| Format | 1 online resource (87 pages), computer, application/pdf |
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