Continuous increments in world population demands transportation with essential vehicle facilities and directly effect on road traffic volume or congestion, mostly in metropolitan cities, and thus it needs significant investigation, analysis, and maintenance. In these regards, an Intelligent Traffic Management System (ITMS) with a Deep-Neuro-Fuzzy model was proposed and implemented. Dijkstra algorithm is used to select optimum path from source to destination on the basis of calculated road segment weights from Deep-Neuro-Fuzzy framework. However, Deep-Neuro-Fuzzy framework needs some comprehensive analysis, other means some simulation or emulation, and etc, to proof the efficiency and workability of the model. In this paper, we are going to explore the Deep-Neuro-Fuzzy model in pragmatic style with an open-source traffic simulation model (SUMO) and helps to explore traffic-related issues including route choice, simulate traffic light or vehicular communication, etc in our ITMS. In addition, a new GUI is developed to control the simulation input attributes and presents the feedbacks into the traffic flow in SUMO environment. Results highlight that the proposed SUMO model can realistically simulate ITMS based on the road segment weights from Deep-Neuro-Fuzzy model. Different built-in routing algorithms are also used to proof the workability of this model.
Traffic is an inevitable problem for metro cities around the globe. Intelligent traffic management system helps to improve the traffic flow by detecting congestions or incidents and suggesting appropriate actions on traffic routing. A new and dynamic internet-based decision-making tool for traffic management system was proposed and implemented in authors' previous works. The tool needs weather, road, and vehicle-related integrated information from different data repositories. Several online web portals host real-time weather data streams. However, road and vehicle information are missing in those portals. In addition, their coverage is limited to city-level congregate information but precise road segment-based information is necessary for real-time TMS decision. Internet of things (IoT)-based online sensors can be a solution for this circumstance. As a consequence, in this chapter, an IoT-based framework is proposed and implemented with several remote mobile agents. Agents are securely interconnected to the cloud, and able to collect and exchange data through wireless communication.
Intelligent Traffic Management (ITM) helps to solve real-time traffic problems and guides efficient and effective routes to reach a destination. It aggregates information from various sensors located in different places of roads and in vehicles to collect different kinds of data about vehicles, weather, roads, and traffic, etc. These data are filtered and processed to generate results from which ITM generates appropriate communication-related decisions. The full ITM must be able to cooperate by allowing communication with and among vehicles and/or IoT devices. It creates pressure on the network, requiring high data transmission bandwidth, and demands short response time and latency for our time-sensitive traffic applications. Besides these, massive amounts of data also demand faster processing and secure storage. In this context, a data center is deemed an ideal companion for ITM which will be used for storage, processing, and transmission of data and results back to different clients. In this article, we are going to present a data center that is specially built for ITM. Our designed data center uses WebSocket-based bi-directional communication, load balance, fault tolerable module, data replicability, and provides road-vehicle-traffic-related web services and distributes Open Data with API supports.
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