A Data-Driven Systematic Approach to Identify, Classify, and Estimate Long-Haul Freight Truck Parking Supply

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dc.contributor.advisorPark, Peter
dc.contributor.advisorGingerich, Kevin
dc.contributor.authorNevland, Erik Alexander
dc.date.accessioned2020-08-11T12:37:24Z
dc.date.available2020-08-11T12:37:24Z
dc.date.copyright2020-01
dc.date.issued2020-08-11
dc.date.updated2020-08-11T12:37:23Z
dc.degree.disciplineCivil Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractCollisions involving trucks have increased fatality risks compared to passenger vehicles. Hours-of-Service (HOS) laws exist to reduce fatalities where truck driver fatigue is a contributing factor. Electronic logging devices (ELD) are being mandated to automatically track HOS and enforce compliance, creating a greater urgency for adequate truck parking. A lack of truck parking is often identified throughout North America; however, these studies are often limited to public rest areas despite evidence that drivers often utilize other types of parking. To adequately compare truck parking supply and demand, an exhaustive truck parking classification scheme is developed based on important location attributes identified through extensive literature review. This scheme can be systematically implemented using available geospatial data. This data is then used to develop a truck parking supply model based on a negative binomial regression. The Region of Peel is used as the study area due to its considerably large freight industry.
dc.identifier.urihttp://hdl.handle.net/10315/37681
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectGeographic information science
dc.subject.keywordsFreight
dc.subject.keywordsParking
dc.subject.keywordsFreight Parking
dc.subject.keywordsTruck Parking
dc.subject.keywordsHeavy Commercial Vehicles
dc.subject.keywordsHCV
dc.subject.keywordsParking Supply
dc.subject.keywordsTruck Parking Supply
dc.subject.keywordsHours-of-Service
dc.subject.keywordsElectronic Logging Devices
dc.subject.keywordsDriver Fatigue
dc.subject.keywordsGPS Data
dc.subject.keywordsNegative Binomial Regression
dc.subject.keywordsTruck Parking Classification
dc.subject.keywordsTruck Parking Supply Model
dc.subject.keywordsTransportation Engineering
dc.subject.keywordsTransportation Planning
dc.subject.keywordsRegion of Peel
dc.subject.keywordsPeel Region
dc.titleA Data-Driven Systematic Approach to Identify, Classify, and Estimate Long-Haul Freight Truck Parking Supply
dc.typeElectronic Thesis or Dissertation

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