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Driving Range Polygon Designing Algorithm for Driving Range Figure 5.3: Example of hexagon driving range visualization. While designing this algorithm a problem occurred. When reducing deviation there comes unreliable result from Google server. Although I want to get driving distance on potential same great-circle line I could obtain a different route from each request. It means that the great-circle distance can be, in the next step, farther from our location but the result could be closer than before because of alternative Google route response. This problem means that I can get, with iteration, to the goal deviation, but it can take many more requests and the algorithm gets slower with each request. 5.5.4 Second Suggestion With regards to the above-mentioned problem, I deal with it using the Google Maps Directions API. This API returns JSON with more information such as individual steps in the route. Every step contains distance value and its location. To avoid the deviation problem in the first suggestion, I iterate, in this approach, through steps in response and the steps’ distances are added until the driving range is overcome. After that, a new request is called from the last added step’s location and it is iterated to the required driving range distance. With this suggestion, I avoid the deviation problem, so the good results could be obtained with fewer requests but a new problem could also have occurred. In some cases, it could happen that close points in multiple-side-polygon are returning a similar JSON object back. For example, the JSON object returns almost the same route for close points, and if an algorithm stops too early with the stop condition, then we get the same location for both points and it means we get a less accurate solution in some cases. The following are the described algorithm’s steps: 1. Create a circle with a great-circle based radius 18PDF Image | Driving Range Estimation and Trajectory Planning
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