2023
DOI: 10.1109/ojits.2023.3235898
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Development of a Data-Driven On-Street Parking Information System Using Enhanced Parking Features

Abstract: On-street parking information (OSPI) systems help reduce congestion in the city by lessening parking search time. However, current systems use features mainly relying on costly manual observations to maintain a high quality. In this paper, on top of traditional location-based features based on spatial, temporal and capacity attributes, vehicle parked-in and parked-out events are employed to fill the quality assurance gap. The parking events (PEs) are used to develop dynamic features to make the system adaptive… Show more

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Cited by 6 publications
(1 citation statement)
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References 37 publications
(65 reference statements)
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“…Such an approach is mentioned in [20][21][22], where the parking authority receives information about the private parking spaces, and then manages them optimally with the public parking spaces in order to increase the net revenue, but at the cost of added complexity and a heavy computational burden. Moreover, the authors in [23] propose a new method for improving the reliability of onstreet parking information (OSPI) systems. The proposed method uses parking events (PEs) to develop dynamic features that can make the system more adaptive to changes that impact on-street parking availability.…”
Section: Introductionmentioning
confidence: 99%
“…Such an approach is mentioned in [20][21][22], where the parking authority receives information about the private parking spaces, and then manages them optimally with the public parking spaces in order to increase the net revenue, but at the cost of added complexity and a heavy computational burden. Moreover, the authors in [23] propose a new method for improving the reliability of onstreet parking information (OSPI) systems. The proposed method uses parking events (PEs) to develop dynamic features that can make the system more adaptive to changes that impact on-street parking availability.…”
Section: Introductionmentioning
confidence: 99%