Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.

Mobile edge computing (MEC) is a promising paradigm to improve the quality of computation experience of mobile devices because it allows mobile devices to offload computing tasks to MEC servers, benefiting from the powerful computing resources of MEC servers. However, the existing computation-offloading works have also some open issues: 1) security and privacy issues, 2) cooperative computation offloading, and 3) dynamic optimization. To address the security and privacy issues, we employ blockchain technology that ensures the reliability and irreversibility of data in MEC systems. Meanwhile, we jointly design and optimize the performance of blockchain and MEC. In this paper, we develop a cooperative computation offloading and resource allocation framework for blockchain-enabled MEC systems. In the framework, we design a multi-objective function to maximize the computation rate of MEC systems and the transaction throughput of blockchain systems by jointly optimizing offloading decision, power allocation, block size and block interval. Due to the dynamic characteristics of the wireless fading channel and the processing queues at MEC servers, the joint optimization is formulated as a Markov decision process (MDP). To tackle the dynamics and complexity of the blockchain-enabled MEC system, we develop an A3C-based cooperation computation offloading and resource allocation algorithm to solve the MDP problem. In the algorithm, deep neural networks are optimized by utilizing asynchronous gradient descent and eliminating the correlation of data. Simulation results show that the proposed algorithm converges fast and achieves significant performance improvements over existing schemes in terms of total reward.

Edge intelligence (EI) is becoming one of the research hotspots among researchers, which is believed to help empower intelligent transportation systems (ITS). ITS generates a large amount of data at the network edge by millions of devices and sensors. Data-driven artificial intelligence (AI) is at the core of ITS development. By pushing the AI frontier to the network edge, EI enables ITS AI applications to have lower latency, higher security, less pressure on the backbone network and better use edge big data. This paper surveys Edge Intelligence in Intelligent Transportation Systems. We first introduce the challenges ITS faces and explain the motivation of using EI in ITS. We then explore the framework of using EI in ITS, including the EI-based ITS architecture, the data gathering and communication methods, the data processing and service delivery, and the performance indexes. The enabling technologies, such as AI models, the Internet of Things, and Edge Computing technologies used in EI-based ITS, are reviewed intensively. We discuss the edge intelligence applications and research fields in ITS in depth. Typical application scenarios, such as autonomous driving, vehicular edge computing, intelligent vehicular transportation system, unmanned aerial vehicle (UAV) in ITS environment, and rail transportation control and management, are explored. The general platforms of EI, the EI training and inference in ITS, as well as the benchmark datasets, are introduced. Finally, we discuss some of the challenges and future directions of using EI in ITS.

The application of blockchain to mobile edge computing (MEC) systems has attracted great interests. However, the design and optimization of blockchain and MEC in most existing works are done separately, which will result in sub-optimal performance. In this paper, we propose a joint optimization framework for blockchain-enabled MEC systems to achieve the optimal trade-off between the performance of the MEC system and the performance of the blockchain system. Specifically, both MEC and blockchain are considered as services in the framework, where energy consumption and delay/time to finality (DTF) are the performance metrics for the MEC system and the blockchain system, respectively. We formulate an optimization problem to achieve the optimal trade-off through jointly optimizing user association, data rate allocation, block producer scheduling, and computational resource allocation. To solve the problem, we decouple the optimization variables for efficient algorithm design. In addition, we develop an iterative algorithm for user association and data rate allocation and a bisection algorithm for computing resource allocation. Simulation results show the convergence of the proposed algorithms, and the proposed scheme can achieve the optimal trade-off between energy consumption and DTF.

This paper investigates the robust distributed cruise control problem of multiple high-speed trains under external disturbances. First, by modeling each train as a cascade of point masses connected by spring-like couplers, the longitudinal interaction between adjacent cars are represented by the connected topological graph. Then, under the framework of the communication-based train control technology, the interaction of desirable speed information among trains and the wayside control center is described by the directed topological graph. Next, a distributed cruise controller is designed by taking advantages of the graphic theory such that the multiple trains track different target speeds, and both the distance of neighboring cars and the headway of successive trains are kept in appropriate ranges. Finally, to eliminate the influence of external disturbances, we adopt the disturbance observer to approximate the perturbations, and present a sufficient condition for the existence of the distributed control strategy and the observer gain parameter in form of the linear matrix inequality (LMI). Numerical experiments illustrate that the composite control law is effective in inhibiting the external disturbances, and guaranteeing the safety, efficiency and comfort of high-speed trains' movement.

The application of network slicing to mobile edge computing (MEC) systems has aroused great interest from both academia and industry. However, the optimization of network slicing and MEC in most existing research works only focuses on resource slicing, energy scheduling, and power allocation from the perspective of mobile devices, without considering the operator's revenue. In this paper, we propose a novel framework for network slicing in MEC systems, including slice request admission and a revenue model, to investigate the operator's revenue escalation problem while considering traffic variations. The revenue model is mainly composed of longer-term revenue and short-term revenue. Particularly, we jointly optimize slice request admission in the long-term and resource allocation in the short-term to maximize the operator's average revenue. By employing the Lyapunov optimization technique, we develop an algorithm without requiring any prior-knowledge of traffic distributions, referred to as the DNSRA, to solve the problem. To reduce the computational complexity of directly solving the DNSRA, we decouple the optimization variables for efficient algorithm design. By this, the strategies for user association and CPU-cycle frequency are obtained in closed forms, respectively. Power allocation and subcarriers assignment are obtained by employing the successive convex approximation approach. Meanwhile, we develop a heuristic algorithm to obtain the dynamic slice request admission decision. Simulation results show that the proposed DNSRA can strike a flexible balance between the average revenue and the average delay, and can significantly increase the operator's revenue against existing schemes.