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PADaaV: Blockchain-Based Parking Price Prediction Scheme for Sustainable Traffic Management

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dc.contributor.author Dua, Amit
dc.date.accessioned 2024-10-08T10:35:31Z
dc.date.available 2024-10-08T10:35:31Z
dc.date.issued 2022-05
dc.identifier.uri https://ieeexplore.ieee.org/abstract/document/9770066
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16049
dc.description.abstract In most countries, traffic congestion has reached a level where managing traffic is tedious for regulatory bodies. The traffic management faced many issues such as route routing based on congestion, delivery of messages/emails to end-users, and real-time allocation of parking slots. There have been many works on predicting parking prices for traffic management, but most favor users or owners and are not secure. To address these issues, a blockchain and Interplanetary File System (IPFS)-based parking price prediction scheme ( PADaaV ) is proposed to facilitate the users to reserve a parking slot securely and efficiently. It mainly focuses on ensuring security, privacy, and transparency for parking slot owners and users. Furthermore, we employ a second price auction model to optimize the parking price for users, and parking slot owners can also get benefit from it. The performance of the PADaaV has been simulated for 100 users with 40 parking slots based on different auction models. The various performance parameters considered are profit for users, profit for parking slot owners, overall revenue of the system, scalability, computation time, and data storage cost. The performance results show that the PADaaV is secure and beneficial for users and parking slot owners. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject Computer Science en_US
dc.subject Blockchain en_US
dc.subject Smart contracts en_US
dc.subject Second price auction model en_US
dc.subject Traffic management en_US
dc.title PADaaV: Blockchain-Based Parking Price Prediction Scheme for Sustainable Traffic Management en_US
dc.type Article en_US


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