Citation

Khan JA, Bangalore KU, Kurkcu A, Ozbay K. Transp. Res. Rec. 2022; 2676(2): 680-691.

Copyright

(Copyright © 2022, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981211045074

PMID

unavailable

Abstract

Trajectory data from connected vehicles (CVs) and other micromobility sources such as e-scooters, bikes, and pedestrians is important for researchers, policy makers, and other stakeholders for leveraging the location, speed, and heading, along with other mobility data, to improve safety and bolster technology development toward innovative location-based applications for citizens. Such raw data needs to be stored and accessed from a non-proprietary database while the obfuscation and encryption techniques on current cloud-based proprietary solutions incur data losses that are deemed inefficient for accurate usage, particularly in time-sensitive real-time operations. In this paper, we target the problem of scalably storing and retrieving potentially sensitive data generated by vehicles and propose TREAD, a blockchain-based system comprising smart contracts to store this mobility data on a distributed ledger such that multiple peers can access and utilize it in different location-based applications while not revealing users' sensitive personal information. It is, however, challenging to scalably store large amounts of constantly generated trajectories, and to achieve scalability we leverage InterPlanetary File System (IPFS), a scalable distributed peer-to-peer data storage system. To avoid users injecting malicious/fake trajectories into the ledger, we develop efficient consensus algorithms for the stakeholders to validate the storage and retrieval process in a distributed manner. We implemented TREAD on the open-source Hyperledger Fabric blockchain platform using trajectory data generated for 700 vehicles in a simulation environment well calibrated with vehicle trajectories from a real-world test-bed in New York City.

RESULTS show that TREAD scalably stores trajectory data with lower delay and overhead.

Language: en

Keywords

data analytics; data and data science; data and technology services related to CAEV (connected, automated, and electric vehicles); including big data; information systems and technology