Prediction of Taxi Destinations Using a Novel Data Embedding Method and Ensemble Learning

Abstract

The accurate and timely destination prediction of taxis is of great importance for location-based service applications. Over the last few decades, the popularization of vehicle navigation systems has brought the era of big data to the taxi industry. Existing destination prediction approaches are mainly based on various Markov chain models or trip matching ideas, which require geographical information and may encounter the problem of data sparsity. Other machine learning prediction models are still unsatisfactory in providing favorable results. In this paper, first, we propose use of a novel and efficient data embedding method for time-related feature pre-processing. The key idea behind this is to embed the data into a two-dimensional space before feature selection. Second, we propose use of a novel data-driven ensemble learning approach for destination prediction. This approach combines the respective superiorities of support vector regression and deep learning at different segments of the whole trajectory. Our experiments are conducted on two real data sets to demonstrate that the proposed ensemble learning model can get superior performance for taxi destination prediction. Comparisons also confirm the effectiveness of the proposed data embedding method in the deep learning model.