Data-driven traffic modeling in Intelligent Transportation Systems (ITS) often learns traffic trends offline from massive historical databases rather than continuously learning in near-real-time as new data are collected, which can help capture the impacts of irregular incidents with low periodic representation (nonrecurrent events (NREs)). Traditional approaches face challenges when predicting the impacts of NREs due to their low frequency in the training data, often leading to their removal or smoothing during preprocessing. Online federated learning (FL), a privacy-preserving and distributed approach, offers a promising alternative. It enables models to continuously learn and update their representations using newly collected data from online data streams. It presents the opportunity to improve the model’s ability to dynamically adapt to stochastic network changes. In conjunction with online FL, variable learning rate strategies can enhance the model’s ability to learn stochastic spatial-temporal trends reliably, including NREs. This paper designs and analyzes the impact of two novel traffic-aware adaptive learning rate strategies, one time-dependent strategy and another dependent on the network state and existence of NREs, for improving the traffic representations learned through online FL. We design a novel heuristic that operates at the client level and dynamically adapts the local learning rate in response to multiple parameters. We conducted extensive experiments using three datasets containing different traffic variables, and our results demonstrated that our learning rate strategies reduced the online inference error by about 15.77% and enhanced the FL models’ resilience to stochastic traffic scenarios.

Autonomous vehicles (AVs) can sense their environment and navigate without human input. However, when AVs are involved in accidents with other AVs or human subjects, liability must be indisputably determined based on accident forensics. However, existing methods rely on centralized authorities to collect and manage accident data, making them vulnerable to cybersecurity attacks (e.g., tampering, denial-of-service). Decentralized systems, such as blockchain networks, present a promising alternative; however, their data immutability properties present challenges in practice. Consequently, this paper introduces a redactable blockchain solution for vehicular event forensics. This novel approach, underpinned by a decentralized attribute-based chameleon hash function, supports modifying data from multiple blocks, as well as individual transaction-level modification operations, without the issue of a single point of failure. We implemented the proposed system using the Charm cryptography library and the Hyperledger Fabric blockchain platform. The Chameleon hash function can support redactable operations for on-chain vehicular event records with a latency of seconds. We also conducted extensive experiments on blockchain performance and the witness group formation cost, highlighting that the proposed redactable blockchain is efficient in practice for vehicular event forensics tasks.

Managing urban traffic dynamics is critical in Intelligent Transportation Systems (ITS), where short-term traffic prediction is vital for effective congestion management and vehicle routing. While existing centralized deep learning (DL) models have achieved high prediction accuracy, their applicability is limited in decentralized ITS environments. The increasing use of connected vehicles and mobile sensors has led to decentralized data generation in ITS, presenting an opportunity to improve traffic prediction through collaborative machine learning. Recently, blockchain technology has shown promise in improving ITS efficiency, security, and reliability. In conjunction with blockchain, Federated Learning (FL) is a suitable approach to leverage online data streams in ITS; however, most research on FL for traffic prediction focuses on offline learning scenarios. This paper researches a blockchain-enhanced architecture for training online traffic prediction models using FL. The proposed approach enables decentralized model training at the edge of the ITS network, and extensive experiments used dynamically collected arterial traffic data shards as a case study to evaluate online learning performance. The results demonstrate that our online FL approach outperforms the per-device, non-federated baseline models for most sensors while maintaining a suitable execution time and latency for real-world deployment.

Platooning technologies enable trucks to drive cooperatively and automatically, providing benefits including less fuel consumption, greater road capacity, and safety. To establish trust during dynamic platooning formation, ensure vehicular data integrity, and guard platoons against potential attackers in mixed fleet environments, verifying any given vehicle’s identity information before granting it access to join a platoon is pivotal. Besides, due to privacy concerns, truck owners may be reluctant to disclose private vehicular information, which can reveal their business data to untrusted third parties. To address these issues, this is the first study to propose an aggregated zero-knowledge proof and blockchain-empowered system for privacy-preserving identity verification in truck platooning. We provide the correctness proof and the security analysis of our proposed authentication scheme, highlighting its increased security and fast performance. The platooning formation procedure is re-designed to seamlessly incorporate the proposed authentication scheme, including the 1st catch-up and cooperative driving steps. The blockchain performs the role of verifier within the authentication scheme and stores platooning records on its digital ledger to guarantee data immutability and integrity. In addition, the proposed programmable access control policies enable truck companies to define who is allowed to access their platoon records. We implement the proposed system and perform extensive experiments on the Hyperledger platform. The results show that the blockchain can provide low latency and high throughput, the aggregated approach can offer a constant verification time of 500 milliseconds regardless of the number of proofs, and the platooning formation only takes seconds under different strategies. The experimental results demonstrate the feasibility of our design for use in real-world truck platooning.

Federated Learning (FL) is a privacy-preserving machine learning (ML) technology that enables collaborative training and learning of a global ML model based on aggregating distributed local model updates. However, security and privacy guarantees could be compromised due to malicious participants and the centralized FL server. This article proposed a bi-level blockchained architecture for secure federated learning-based traffic prediction. The bottom and top layer blockchain store the local model and global aggregated parameters accordingly, and the distributed homomorphic-encrypted federated averaging (DHFA) scheme addresses the secure computation problems. We propose the partial private key distribution protocol and a partially homomorphic encryption/decryption scheme to achieve the distributed privacy-preserving federated averaging model. We conduct extensive experiments to measure the running time of DHFA operations, quantify the read and write performance of the blockchain network, and elucidate the impacts of varying regional group sizes and model complexities on the resulting prediction accuracy for the online traffic flow prediction task. The results indicate that the proposed system can facilitate secure and decentralized federated learning for real-world traffic prediction tasks.

Accurate real-time traffic flow prediction can be leveraged to relieve traffic congestion and associated negative impacts. The existing centralized deep learning methodologies have demonstrated high prediction accuracy, but suffer from privacy concerns due to the sensitive nature of transportation data. Moreover, the emerging literature on traffic prediction by distributed learning approaches, including federated learning, primarily focuses on offline learning. This paper proposes BFRT, a blockchained federated learning architecture for online traffic flow prediction using real-time data and edge computing. The proposed approach provides privacy for the underlying data, while enabling decentralized model training in real-time at the Internet of Vehicles edge. We federate GRU and LSTM models and conduct extensive experiments with dynamically collected arterial traffic data shards. We prototype the proposed permissioned blockchain network on Hyperledger Fabric and perform extensive tests using virtual machines to simulate the edge nodes. Experimental results outperform the centralized models, highlighting the feasibility of our approach for facili-tating privacy-preserving and decentralized real-time traffic flow prediction.