A Classification and Predication Framework for Taxi-Hailing Based on Big Data

As an important public transportation, Taxi is used for passengers every day, which is one of the primary causes for traffic jams. For passengers, knowing the difficulty degree of taking a taxi at a particular time and place can help us plan the journey effectively. Nevertheless, the existing predication models for traffic are not able to express the difficulty degree of choosing a taxi. In order to solve this problem, we can use historical data of taxi status to analysis and predict the possibility of taxi-hailing at a specific time and place. In this paper, we present a classification and predication framework for taxi-hailing. In this framework, firstly we use K-Means clustering algorithm to divide the taxi data into different clusters. Then we use Echarts to extract the features of each cluster in order to show the different difficulty degree. Next we use neural network to generate the predication result using the result of K-Means. On this basis, we propose a method to make the predication of taxihailing at a particular time and place, which can calculate the possibility score of taxi-hailing. Finally, we make a prediction using this framework and compare the predication results with the actual travelling data report. The comparison results verify the reliability of this framework.