Enhanced Spatio-Temporal Scalability in Data Management: Fostering Trusted On-Chain and Off-Chain Collaboration for Intelligent Transportation Systems

Intelligent transportation systems (ITS) involve data management and operation among multiple parties, making the trustworthiness and transparency of centralized data a highly challenging issue. Blockchain data storage, characterized by its immutability and multi-party collaboration, has emerged as a mainstream technology for trustworthy data sharing among multiple parties. However, the current on-chain data structures based on transactions, e.g., Ethereum’s MPT, struggle to address the scalability and efficient on-chain and off-chain collaboration required for data management with high spatio-temporal characteristics, as seen in intelligent transportation. In this paper, we propose a spatio-temporal scalable Merkle Patricia Tree (sMPT) structure for hierarchical organization of on-chain data objects (DOs), which is mapped to the off-chain transportation data entries through a DO packaging mechanism. The experimental evaluation results demonstrate the effectiveness of the proposed sMPT structure in basic functionality, like on-chain storage and sMPT compression. In terms of efficiency, sMPT outperforms the original MPT in retrieval and verification, particularly in batch verification.