Support Vector Machine Based Route Classification and Description
Reshma M1, Pristy Paul T2, Surekha Mariam Varghese3
1Reshma M*, Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Ernakulam, India.
2Pristy Paul T, Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Ernakulam, India.
3Dr. Surekha Mariam Varghese, Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Ernakulam, India.
Manuscript received on April 05, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 14-18 | Volume-9 Issue-4, April 2020. | Retrieval Number: C6286029320/2020©BEIESP | DOI: 10.35940/ijeat.C6286.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Traveling is very much important in one’s life. Location-based services have developed a lot due to the development of communication technologies. It confines services that execute programs that use geographical data. Map services authorize travelers to look for the information surrounding them and organize an outing to his/her best-loved spot. Google Maps API is convenient for locating the shortest route information. However, the map system doesn’t supply any illustration about air quality or congestion in a path. At times, a substitute route with less congestion can take you quickly to your spot than a shorter route. Numerous crucial health concerns for human beings are caused due to pollution. The motive is to develop a system that offers the textual explanation of routes utilizing the sub-routes information from Google map and BreezoMeter. The end-user can choose the starting and ending points of his/her travel and the route map showing various routes from source to destination is exhibited along with a small description of each route. The illustration of the routes is obtained depending on three factors such as air quality, congestion and distance gathered from BreezoMeter, Google map traffic API and distance matrix API respectively. Multicategory Support Vector Machine (SVM) is an organized and guided categorization technique and is used here to classify factors into various levels. Since the textual illustration of the route is accessible, the end-user can effortlessly understand the details about the route and they can choose a particular route.
Keywords: BreezoMeter, Google Maps, SVM, Traffic Congestion