## Materials and Methods  
### Architecture of Mobile Service Computing for Traffic Static Estimate in an IoT Scenario  
Nowadays, information and communication technology (ICT) and intelligent control technology make great changes in the transportation system. Advanced technology including edge, cloud, V2X, and IoT is integrated into the transportation monitor and management. To address the operating mechanism of the proposed algorithm, a general and sustainable ITS architecture is shown as Figure 1 inspired by [44]. There are four parts in the proposed ITS architecture. In Part 1--traffic information collection, the IoT nodes, including connected cars, inductive loops, and traffic cameras can be accessed into the network. Measured data including travelling speed, number of passing vehicles, pictures of waiting vehicles, etc. can be collected and sent to Part 2--traffic states sensing and prediction model in upper level. In Part 2, data from Part 1 is received and stored. Figure 1: Architecture of Intelligent Transportation System. Then, real time, as well as historical traffic information can be utilized in many models to evaluate and predict abstracted traffic states including volumes, queue length, travelling delay, etc. Then, these states about traffic are delivered to the transport system control center and transport monitor and management center to incorporate with the decision support system (DSS). In Part 3--transport system control and traffic light control algorithm are designed with the overall objectives from transport monitor and management center, sensing traffic states from Part 2, and basic information from Part 1. Usually, Part 3 will impose great influence on the behaviors of traffic participants [45; 46] by adopting different control strategies. Part 2 and 3 can be treated as transport system models (TSMs) including sensing, prediction, and control. The high level of the ITS Part 4--transport monitor and management can get the whole information and set the objectives and constraints on Part 3. Many new developed models, algorithms, and equipment could be integrated in the architecture [47; 48]. This paper works on Part 2 in the general ITS architecture from the perspective of IoT. It aims to develop a queue estimation algorithm working in the TSMs. IoT provides information as inputs for the queue estimation algorithm working in the TSMs. A mixed traffic scenario including connected vehicles and traditional vehicles is considered, which will be the general case in the near future. The objective of this paper is to develop an algorithm utilizing the information from Part 2. Connected vehicles travel on the road and process the data, which can provide mobile service for traffic states estimate based on the computing of the onboard embedded processor. The cameras installed at the signal intersection can acquire the images of vehicles in the queue periodically and transmit those back to the edge server. Cameras are connected to the edge server on the roadside by wired or wireless networks, and image processing is done in the edge server, which is set in Part 2. Characteristics of the traffic including congestion, violated traffic regulations, and plate numbers can be obtained accordingly. From the view of server provider, all the connected devices in the traffic scenario can produce useful information or work with the edge server to complete more complex tasks. Service computing gives a unified information exchange and sharing mechanism in the IoT traffic scenario. Some useful traffic characteristics can be abstracted based on the information from IoT nodes, such as traffic flow and congestion, which is the key information to traffic management. Some traffic applications need the IoT nodes to work cooperatively. For example, traffic guidance needs the traffic state of a wide network of roads and exchanging information with roadside unit or other detection devices. The cloud server will give some advices based on the real time information.