With the increasing number of vehicle and traffic jams, urban traffic management is becoming a serious issue. In this article, we propose novel four-tier architecture for urban traffic management with the convergence of VANETs, 5G networks, software-defined networks, and mobile edge computing technologies. The proposed architecture provides better communication and more rapid responsive speed in a more distributed and dynamic manner. The practical case of rapid accident rescue can significantly shorten the rescue time. Key technologies with respect to vehicle localization, data pre-fetching, traffic lights control, and traffic prediction are also discussed. Obviously, the novel architecture shows noteworthy potential for alleviating traffic congestion and improving the efficiency of urban traffic management. With the growth of urbanization, the problem of urban traffic congestion has become a serious concern. In 2014, urban commuters in the United States collectively lost 6.9 billion hours and 3.1 billion gallons of fuel to traffic delay, and the excess fuel and lost productivity cost US$160 billion. Similarly, as nearly a third of the world's 50 most congested cities are in China, the traffic problem is worse than in the United States. Researchers have added extra infrastructure to reduce the congestion such as building dedicated lanes for bus rapid transit. However, the effect is limited because the construction speed of extra roads is far slower than the increasing speed of new vehicles. Therefore, the new urban traffic management solution is expected to explore strategies to use emerging technologies to mitigate urban traffic congestion [1] . In order to achieve high-efficiency urban traffic management, there are at least three key issues that need to be addressed. Perception of real-time traffic conditions: Numerous high-resolution roadside sensors and onboard sensors need to be deployed to sense all real-time traffic conditions including vehicle speed, direction, location, road throughput, weather conditions, temperature/humidity and so on. Low-latency communication and massive data storage: Sensors intermittently produce huge amounts of raw data, and easily touch the petabyte order of magnitude in size. In view of the difference in data types, dimensionality, and huge volume, the bandwidth of communication networks, storage capability, and data processing speed need to expand than ever. Traffic prediction and real-time responsiveness: Massive traffic data helps city planners to monitor traffic density, throughput, and events in real time, and the traffic control systems should have real-time responsive ability to respond to traffic events, and make immediate decisions based on traffic prediction algorithms to guide traffic flow. Designing a context-aware traffic light control system to decrease the waiting time at intersections, a rapid accident rescue system to improve emergency responsiveness, and a novel data analysis and traffic prediction system based on massive traffic data to optimize the efficiency of the existing roads are considered as key elements. Unfortunately, the existing data collection system, vehicular ad hoc networks (VANETs), and the traditional traffic flow prediction model [2] are far from sufficient to solve the above-mentioned issues. For example, VANETs support a variety of services and achieve success in a certain aspect such as a vehicular collision avoidance system using vehicle-to-vehicle or vehicle-to-infrastructure communication [3] . However, its inherent defects such as unbalanced traffic flow and low bandwidth impact the deployment of urban traffic management [4] . Therefore, exploring a high-efficiency urban traffic management system is becoming extremely urgent. The rapid development of emerging technologies such as fifth generation (5G), software defined networking (SDN) [5] , and mobile edge computing (MEC) [6] is expected to boost the advancement of urban traffic management. Introducing the 5G and SDN technologies into the vehicular network, the new SDN-based heterogeneous vehicular network shall offer high-bandwidth communication service with flexibility and programmability [7] ; thus, the environmental sensing will become more agile. Meanwhile, MEC places the computing resource at the edge of the 35 mobile vehicular network, and performs critical missions with real-time or near-real-time response speeds. The main contributions of this article are to propose a novel architecture combined with 5G wireless network, SDN, and MEC technologies, use a paradigm of road accident rescue to validate the high efficiency of the proposed architecture, and discuss the key technologies and potential solutions. In short, the novel urban traffic management architecture would be more highly efficient than ever before with the features of intelligent sense, low-latency communication, and real-time response. The article is organized as follows. We propose a novel four-tier architecture. Afterward, rapid accident rescue, a practical case, is used to validate the high efficiency of the proposed architecture, and several key technologies toward the architecture are discussed. Finally, the conclusion is made. Nowadays, tremendous traffic congestion makes the commuter extremely stressed. However, various emerging technologies provide a potential opportunity to improve traffic congestion and exhaust emission by monitoring traffic conditions. Based on these technologies, it is necessary to construct a new architecture to improve emergency responsiveness, balance the traffic flow, and save fuel and time in the transportation of citizens. When artificial intelligence combines with big data, a new data-driven computing model, such as deep learning, is explored. Apart from data collection and communication, the new architecture needs to carefully take into consideration the storage, access, and analysis technologies. The four-tier architecture for urban traffic management is shown in Fig. 1 , including the environment sensing layer, communication layer, MEC server layer, and remote core cloud server (RCCS) layer. Similar to IoT applications, the data collection layer is the foundation and plays a vital role [8] . Traffic data is mainly derived from the roadside infrastructure and onboard sensors. As usual, the roadside infrastructure such as the inductive loop is responsible for counting the number of vehicles, identifying license plate numbers, and so on. Such data from the detector is classified as passive data. But now, the vehicles that act as a big intelligent sensor employ the onboard sensors to sense vehicular status including engine speed, velocity, direction, and surrounding environment information, including location, lane, temperature, and humidity. This type of data is classified as active data. In the environment sensing layer, the precision and integrity is better than in the traditional way since high-resolution sensors are deployed massively, and the vehicles report their status and environmental data actively. In particular, vehicle location, which is important traffic data, can also be estimated by new wireless localization technology in urban environments where GPS coverage is not available. VANETs have been viewed as one of the most enabling technologies for "connected cars," but huge amounts of traffic data bring some challenges to VANETs, such as unbalanced traffic flow [9] . In order to break the bottleneck of communication, we consider two emerging network paradigms: 5G and SDN. The 5G network employs multiple-input multiple-output (MIMO) and cognitive radio technologies to achieve 1.2 Gb/s communication speed in a mobile environment from a vehicle traveling at over 100 km/h, while device-to-device (D2D) technology can offer positioning service, in addition to more flexible and direct information exchange. SDN architecture, a new revolutionary idea in networking, decouples the network control (control plane) and the forwarding functions (data plane), enabling network control to become programmable. The underlying infrastructure can be abstracted from applications and network services. SDN-based networks provide flexibility, scalability, programmability, and global knowledge of the network. The SDN model operates with OpenFlow protocol, as shown in Fig. 2 , the components of which are as follows. SDN global controller: The central global controller maintains all the network behaviors of all the SDN-based heterogeneous wireless and wire networks. It belongs to the control plane, and communicates with the data plane using a data-controller plane interface (D-CPI) and with the application plane using an application-controller plane interface (A-CPI). 5G base station (BS): The BS plays three types of roles: • A router as global mobile communication device acts as a resource (router). • MEC could act as a server; the portable virtual machine (VM) is deployed on the BS. • The SDN local controller controls the network elements, roadside units (RSUs), and vehicles in a certain local area.
Based on this, Liu et al. REF proposed a four-tier centralized architecture for urban traffic management combined with 5G wireless network, SDN (software-defined networks), and MEC (mobile edge computing) technologies.
High-Efficiency Urban Traffic Management in Context-Aware Computing and 5G Communication
IEEE Communications Magazine
IEEE Communications Magazine
Computer Science