Transportation problems are likely to occur in densely populated areas residents, especially for people who travel for their daily activities. People ultimately need a navigation application that they can use through their devices. Thus, the information to support their daily commuter activities can be accessed easily and quickly. Generally, the data is an alternative route to travel swiftly and comfortably in the middle of heavy traffic. Besides that, users need a rest area, the nearest gas station, or even find exciting places that they might visit while resting. Therefore, to provide travel information effectively and efficiently, research on the right algorithm to support map-based application development is an exciting topic to discuss.
Map-based applications are generally useful for finding information about a particular location or place we are headed. Google Maps is a map-based application widely known by the public to provide travel information or specific navigation. With Google Maps mobile version, users can get the information they want directly from their mobile devices. Google also has a direct interface that supports the user in finding a navigation path between two specified locations. In recognizing an area around the path, there is an algorithm to support that functionality, namely RouteBoxer. The area around which has been identified can then be used by the application to identify nearby places.
In finding the area around a path, RouteBoxer algorithm determines a rectangular grid covering the entire road and marks adjacent rectangles by performing a path trace. However, the algorithm performance becomes very slow when it contains many path points. RouteBoxer algorithm accepts a set of geolocation points representing the user’s path and distance, away from a trail, to identify the area.
The RouteBoxer library previously experienced a significant performance drop when calculating the area of a route that exceeded a certain distance due to the increasing number of points representing the path. Therefore, this research tries to improve the processing performance of the RouteBoxer algorithm by reducing the number of issues that the algorithm will process on the same route but by integrating the Douglas-Peucker Line Simplification algorithm.
This research combines the Douglas-Peucker Line Simplification algorithm to reduce the number of points processed by RouteBoxer algorithm to improve its processing performance. Experiments show that the algorithm’s path simplification process can reduce up to 99% of path points, enhance the performance of previous RouteBoxer calculations up to 87.8 times faster, and maintain precision above 92.6%. The full version of the article can be read in our published article.
Author: Chandrawati Putri Wulandari
Pinandito, A. and Wulandari, CP, 2020, November. Integrating douglas-peucker line simplification into routeboxer algorithm on a map-based Android application. In Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology (pp. 213-219). Publication link related to the article above: