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Search Engine Sequence Diagram UML Model Description: A .NET API "System.Web" namespace supplies classes and interfaces to enable communication between the browser and the server. It includes the HttpRequest class, the HttpResponse class, and the HttpServerUtility class. It also includes classes for cookie manipulation, file transfer, exception information, and output cache control. An application of type "HttpApplication" is selected and it calls a private method named "AddScheduledTask" with local parameters including method name and Seconds, and registers a cache object dependency callback for the web framework.
Source publication
Intelligence traffic management system (ITMS) provides effective and efficient solutions toward the road traffic management and decision-making problems, and thus helps to reduce fuel consumption and emission of greenhouse gases. Software-based real-time bi-directional TMS with a neural network was proposed and implemented. The proposed TMS solves...
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... an HTML tag is detected, the crawler scraps the adjacent data rows using XPath (XPath, 2016), interprets unstructured data, converts them to simplified structured format, and then stores in the database table. Figure 4 represents the total search engine crawling sequence diagram. NET API "System.Web" namespace supplies classes and interfaces to enable communication between the browser and the server. ...
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... grid search finds optimum window size 20 with minimum Mean Square Error (MSE) is 44.8 for rainfall, with minimum Mean Square Error (MSE Mean Square Error (MSE) is 15.7 for wind, (MSE) is 106.67 for humidity, and 0.02 for peek hour. The grid search results are presented in Figure 10 to Figure 14. optimum window sizes from each present in Figure 15 to Figure 19. ...
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... window sizes from each present in Figure 15 to Figure 19. window size 20 with minimum Mean Square Error (MSE) is 44.8 for rainfall, optimum window size 30 with minimum Mean Square Error (MSE) is 5.8 for temperature, optimum window size 5 with minimum Mean Square Error (MSE) is 15.7 for wind, optimum window size 40 with minimum Mean Square Error optimum window size 5 with minimum Mean Square Error (MSE) is The grid search results are presented in Figure 10 to Figure 14. from each feature are used to generate their corresponding forecast results and Search for Rainfall optimum window size 30 ) is 5.8 for temperature, optimum window size 5 with minimum optimum window size 40 with minimum Mean Square Error optimum window size 5 with minimum Mean Square Error (MSE) is The grid search results are presented in Figure 10 to Figure 14. ...
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... window sizes from each present in Figure 15 to Figure 19. window size 20 with minimum Mean Square Error (MSE) is 44.8 for rainfall, optimum window size 30 with minimum Mean Square Error (MSE) is 5.8 for temperature, optimum window size 5 with minimum Mean Square Error (MSE) is 15.7 for wind, optimum window size 40 with minimum Mean Square Error optimum window size 5 with minimum Mean Square Error (MSE) is The grid search results are presented in Figure 10 to Figure 14. from each feature are used to generate their corresponding forecast results and Search for Rainfall optimum window size 30 ) is 5.8 for temperature, optimum window size 5 with minimum optimum window size 40 with minimum Mean Square Error optimum window size 5 with minimum Mean Square Error (MSE) is The grid search results are presented in Figure 10 to Figure 14. Afterward, the generate their corresponding forecast results and ...
Citations
... We also did a comparative study with other methods and implemented a low-cost but flexible ITMS method in [20][21] [22]. Our Deep-Neuro-Fuzzy model [7] [8] uses multivariate and multi-attributes data and simulates properly in SUMO environment. Our IoT based embedded sensor system helps the ITMS to collect the real-time environment, road and vehicle related information with low cost, less bandwidth, but flexible environment. ...
This paper focuses on Sumo Urban Mobility Simulation (SUMO) and real-time Traffic Management System (TMS) simulation for evaluation, management, and design of Intelligent Transportation Systems (ITS). Such simulations are expected to offer the prediction and on-the-fly feedback for better decision-making. In these regards, a new Intelligent Traffic Management System (ITMS) was proposed and implemented-where a path from source to destination was selected by Dijkstra algorithm, and the road segment weights were calculated using real-time analyses (Deep-Neuro-Fuzzy framework) of data collected from infrastructure systems, mobile, distributed technologies, and socially-build systems. We aim to simulate the ITMS in pragmatic style with micro traffic, open-source traffic simulation model (SUMO), and discuss the challenges related to modeling and simulation for ITMS. Also, we expose a new model-Ant Colony Optimization (ACO) in SUMO tool to support a multi-agent-based collaborative decision-making environment for ITMS. Beside we evaluate ACO model performance with exiting built-in optimum route-finding SUMO models (Contraction Hierarchies Wrapper)-CHWrapper, A-star (A*), and Dijkstra) for optimum route choice. The results highlight that ACO performs better than other algorithms.
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.
Continuous increments in the world population demand transportation with essential vehicle facilities and direct effects on road traffic volume or congestion, mostly in metropolitan cities like Dhaka, Bangladesh, and thus it needs significant investigation, analysis, and maintenance. An up-to-date technological-based Intelligent Traffic Management Support System (ITMS)/tool can help to improve traffic flow by integrating information from different data repositories and online sensors, detecting incidents, and taking actions on traffic routing, and thus can help to reduce both fuel consumption and associated emission of greenhouse gases. 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 the optimum path from source to destination based on calculated road segment weights from the Deep-Neuro-Fuzzy framework. However, it is still running on a hypothesis and does not have any empirical evidence of working. Even data are also generated hypothetically. Therefore, the ITMS needs some comprehensive analysis, other means some simulation or emulation, etc., to provide that the hypothesis is valid. Thus, exploring the whole ITMS in pragmatic style with a micro traffic source traffic simulation model, and real IoT based vehicle explore traffic-related issues including route choice, simulate traffic light or vehicular communication, etc. Thus, there are several directions in this research including-1) Implementation of ITMS with the traffic simulation model. 2) Improvement of ITMS workability by integrating the SWARM-Intelligence algorithm for routing decisions in a more robust environment. An initial step towards V2V communication. 3) Design and implementation of IoT devices-to collect real-time road, weather, and vehicle-related data, and to process and integrate them for appropriate information. 4) Design and implementation of modern machine learning models for detecting and locating road defects. 5) Design and development of an ASP.NET based Data Center to support OPEN DATA applications. 4 | P a g e
