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Development of Prediction Model for Real-Time Parking Availability for On-Street Paid Parking

Liu, Chang (2016) Development of Prediction Model for Real-Time Parking Availability for On-Street Paid Parking. Master's Thesis, University of Pittsburgh. (Unpublished)

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This research is based on the need for a parking information system to provide on- street parking availability information in the neighborhoods in the City of Pittsburgh has been established. This project intends to provide a methodology to determine the real-time parking availability information for on street parking operators.
As a part of this project, this research models the parking availability with no specialized hardware other than purchased parking time from a kiosk type pay system. The prediction model developed is based on the sample data collected with no real-time data required other than paid time information.
Because paid parking systems only record the time of arrival and paid parking time, the actual departure time and thus the availability of the parking space is unknown. This research determined the relationship between over-paid on-street parking time, which means that the owner pays more than the actual parking time needs and how this relates to parking space availability for next vehicle based on actual on-street parking time. Many variables may influence over-paid parking time, such as trip purpose types of users, weather conditions and temporal distribution. All of these variables were explored to develop the predictive model.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Liu, Changchl208@pitt.eduCHL2080000-0001-9335-6476
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorMagalotti, Mark mjm25@pitt.eduMJM25
Committee MemberPelechrinis, Konstantinoskpele@pitt.eduKPELE
Committee MemberJampole,
Date: 25 January 2016
Date Type: Publication
Defense Date: 18 November 2015
Approval Date: 25 January 2016
Submission Date: 16 November 2015
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 79
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Civil and Environmental Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Real-time parking information system, on-street paid parking, prediction model
Date Deposited: 25 Jan 2016 15:34
Last Modified: 15 Nov 2016 14:30


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