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

Riya Kakkar, Jafar Alzubi, Amit Dua, Smita Agrawal, Sudeep Tanwar, Rajat Agrawal, Gulshan Sharma, Pitshou N. Bokoro, Ravi Sharma

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)50125-50136
Number of pages12
JournalIEEE Access
Publication statusPublished - 2022


  • Blockchain
  • second price auction model
  • smart contracts
  • traffic management

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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