This development calls for intelligent augmented spatial keyword queries (ASKQ), which intelligently takes into account the locations, unstructured information (in the form of keyword sets), structured information (in the form of boolean expressions) of 182MinzuAvespatial entities. Due to the popularity of the geographical devices deployed on the vehicles, a tremendous amount of spatial entities which include spatial information, unstructured information and structured information, are generated every second. Internet of Vehicles (IoV) has attracted wide attention from both academia and industry. The experimental simulation proves the accuracy of travel time prediction in real road networks and efficiency of algorithm in processing SGQ queries which provides best shortest path to reach point of interest. The behavior of the spatial query processing algorithms in terms of CPU time, network cost, node accessibility, and scalability of the dataset and the value of k is examined. The algorithm predicts the overall travel time in the user specified spatial region on the scheduled journey date. The future traffic is predicted by considering factors such as day significance of the user spatial region, day light and climatic conditions of the journey date & time by applying fuzzy rules in MATLAB. The SGQ is a spatial route search skyline query that retrieves nearest point of interest of the users for future journey schedule. A new spatial query called Skyline Geo-Calendar Query (SGQ) on time dependent road networks is proposed.
This paper provides future travel time prediction based advanced navigation solutions for user desired point of interest. The existing skyline query processing methods are based on travel time and not on a future date. With advancements in Wireless technologies, GIS, GPS and sensors, spatial route search is gaining importance under location based services.
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