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dc.contributor.advisorHunter, Michael P.
dc.contributor.authorBolen, John
dc.date.accessioned2018-01-22T21:14:23Z
dc.date.available2018-01-22T21:14:23Z
dc.date.created2017-12
dc.date.issued2017-12-11
dc.date.submittedDecember 2017
dc.identifier.urihttp://hdl.handle.net/1853/59302
dc.description.abstractThis experiment uses VISSIM to replicate the message broadcasted by connected vehicles and plugs it into an energy calculator in order to determine how the energy usage of a vehicle fleet changes as the truck percentage of the fleet changes. By replicating the connected vehicle message, it also allows researchers to determine the extent to which connected vehicle data can be used in future experiments. This experiment began with the building of a microscopic simulation traffic model the North Avenue Corridor in Atlanta, GA, modeling the signal timing, traffic volumes, and overall characteristics of all 19 signalized intersections within the three mile corridor. With this done, the model was run ten different times for each of seven different fleet compositions, each with a different percentage of single unit delivery trucks and tractor trailers. The data files directly outputted into VISSIM were then processed in such a way that they mimicked the standardized message broadcasted by connected vehicles. After this, the processed files were run through the energy calculator in order to determine the energy for each vehicle type as well as for the entire fleet. From this experiment, it was determined that adding more trucks to a vehicle fleet has a small but definite change in the per-vehicle energy for passenger cars. The per-vehicle change for trucks was larger than that of cars, but due to extreme variability in the truck results, the extent to which increasing truck percentage affects trucks is inconclusive. Future research into this topic should include much larger sample sizes than ten runs per fleet composition, and should include more fleet compositions in the range of 10% trucks to 50% trucks. Future research may also include sampling the connected vehicle replica data to determine the expected sample error from various connected vehicle market penetration rates.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectVehicle energy
dc.subjectBasic safety message
dc.subjectConnected vehicles
dc.subjectTruck percentage
dc.titleAnalysis of trucking variability in roadway network energy using basic safety message data
dc.typeThesis
dc.description.degreeM.S.
dc.contributor.departmentCivil and Environmental Engineering
thesis.degree.levelMasters
dc.contributor.committeeMemberGuin, Angshuman
dc.contributor.committeeMemberRodgers, Michael O.
dc.date.updated2018-01-22T21:14:23Z


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