Dynamic Parking Pricing using Transaction Data

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dc.contributor.advisorNourinejad, Mehdi
dc.contributor.authorLuo, Wenhan
dc.date.accessioned2024-11-07T11:19:19Z
dc.date.available2024-11-07T11:19:19Z
dc.date.copyright2024-08-30
dc.date.issued2024-11-07
dc.date.updated2024-11-07T11:19:18Z
dc.degree.disciplineCivil Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractManaging on-street parking in dense urban areas poses challenges due to high demand and limited parking space availability, leading to increased congestion and search times for drivers. This thesis explores the efficacy of implementing a dynamic parking pricing policy inside a parking network to mitigate these challenges. Dynamic parking pricing adjusts prices based on parking demands, aiming to balance parking occupancy across different areas. The research investigates the feasibility of utilizing transaction data to predict parking occupancy, eliminating the need for expensive occupancy detection infrastructure. A predictive Neural Network is generated, and a price-setting algorithm is proposed to optimize and change parking prices to increase availability in high-occupied areas and attract drivers to underutilized spaces.
dc.identifier.urihttps://hdl.handle.net/10315/42517
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectTransportation planning
dc.subjectCivil engineering
dc.subjectComputer science
dc.subject.keywordsNeural network
dc.subject.keywordsAlgorithm
dc.subject.keywordsParking pricing
dc.subject.keywordsDynamic pricing
dc.subject.keywordsParking
dc.titleDynamic Parking Pricing using Transaction Data
dc.typeElectronic Thesis or Dissertation

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