## 1 Introduction
It also needs to be secure against attacks such as Sybil and 51% of attacks as well as energy efficient. Consensus is the core of any DLT. Most of the challenges faced are based on the consensus algorithm. In [22], a blockchain-enabled vehicular crowd-sensing technology secures 5G Internet of Vehicles user privacy and data safety by securing real-time traffic data. A deep reinforcement learning (DRL) algorithm selects the best active miners and transactions to optimize blockchain security and latency. A two-sided matching-based approach allocates non-orthogonal multiple access sub-channels to reduce uploading delay for all users. This technology safeguards vehicular crowd-sensing data collecting and user privacy. The consensus algorithm proposed in this work is PBFT, which is known for its high tolerance and security but has overhead computational requirements. Reference [28] proposes the Ethereum-based VNB (VANETs with a Blockchain). The VNB simulates a vehicle on-board unit (OBU), scanning adjacent vehicles, authenticating them, and communicating with blockchain accounts. The VNB correctly distinguished different vehicle types in simulations. Despite its limitations, the proposed VNB offers a promising security and trust architecture for autonomous vehicular networks and ITS in smart cities in the near future. Proof of Work (PoW) and Proof of Stake (PoS) are both used as consensus algorithms. PoW is utilized for its ease and security in determining the correct nonce, while PoS is utilized for its energy efficiency and decentralization prevention. Nonetheless, PoS is susceptible to double-spend attacks. PoW is known for its high security but needs resource-rich nodes, and thus, it is not suitable for IoV operations. PoS is known for its security and operations efficiency, but it is vulnerable to centralization and routing problems. Other conditions and criteria such as data quality: Information saved and transmitted through the system must be as accurate and trustworthy as possible by excluding any potentially misleading data. Governance: The many participants in the DLT-based ITS ecosystem need a well-defined governance framework that specifies their specific responsibilities and how they will make decisions. Legal and Regulatory Compliance: The system must follow all data protection, privacy, and cybersecurity legislation, both domestically and internationally. Incentive Mechanisms: Suitable incentive mechanisms, such as token-based rewards for users and service providers, should be built into the DLT-based ITS to encourage widespread adoption and active involvement. User Experience: The system needs to be simple and straightforward so that those who really utilize the DLT-based ITS services can get about with ease. By meeting these requirements, a DLT-based ITS can contribute to the development of a more productive, secure, and transparent transportation ecosystem, which will benefit users, operators, and regulators. In this paper, we compare multiple blockchain and non-blockchain consensus algorithms and their applicability to serve ITS applications based on the requirement [29]. We propose FlexiChain 3.0 Technology as a platform to host ITS digital assets collections and exchange in V2V, Vehicle-to-Machine (V2M), and Vehicle-to-Human (V2H) transactions. In addition, a detailed security analysis for certain types of attacks between the proposed work and related works is presented. Figure 1 illustrates the layered structure of employing DLT in intelligent transportation in applications such as auto vehicle driving data training, V2X communication, vehicles' history, and autonomous vehicles. The rest of the paper is organized as follows: Section 2 summarizes the novel contributions of this paper. Section 3 presents background information and previous related works. Section 4 presents the proposed system. Section 5 provides experimental results. Finally, Section 6 concludes the paper and presents directions for future research.