Resumen
This paper proposes a novel four-pronged solution for estimating routes and total time required to travel in a transportation network. The proposed solution attempts to optimize the Google?s Map API request flow using open street map (OSM). As it is known that each request to Google Map API will incur some latency, since different computation are performed on Google server for each request. To avoid this latency, route estimations (distance and time) are calculated using a four-pronged approach based on Google map API, open street map (OSM), routing cache and logical grid of locations. The objective is to create a generalized routing system that tries to use Google services in optimized fashion. The proposed approach stores each requests/ response from Google Map API into an optimized data structure called cache. After passage of time, these cache records are moved to another data structure called random access memory (RAM) file. In scenarios where requests can?t be served via stored data, the proposed system attempts to give approximate estimations based on open street map (OSM) using an A* search for finding route. In addition to this, data that has been cached in proposed approach can be used for further analysis or applying machine learning algorithm for off-line route calculations later.