Ant Colony Algorithm for Determining Dynamic Travel Routes Based on Traffic Information from Twitter

Abstract

Combining the search method for fire suppression routes with ant colony algorithms and methods of analyzing twitter events on the highway is the basis of the problems to be studied. The results of the twitter data feature extraction are classified with Support Vector Machine after it is implemented in the Simple Additive Weighting method in calculating path weights with criteria of distance, congestion, multiple branching, and many holes. Line weights are also used as initial pheromone values. The C-means method is used to group the weights of each path and distance so that the path with the lowest weight and the shortest distance that will be simulated using the Ant Colony. The validation results with cross fold on SVM with linear kernels produce the greatest accuracy value is 97.93% for training data distribution: test data 6: 4. The simulation of the selection of the damkar car path from Feather to Pleburan with Ant Colony obtained 50 seconds of computation time, whereas with Ant Colony with Clustering the computation time was 15, resulting in a reduction in computing of 35. Ant colony with MinMax optimization gives the best computation time of 14.47 seconds with 100 iterations and 10 nodes.

Keywords

• data mining
• ant colony clustering
• support vector machine