With recent advances in mobile and sensor technologies, a large amount of efforts have been made on developing intelligent applications for taxi drivers, which provide beneficial guide and opportunity to improve the profit and work efficiency. However, limited scopes focus on the latent social interaction within cab drivers, and corresponding social propagation scheme to share driving behaviors has been largely ignored. To that end, in this paper, we propose a comprehensive study to reveal how the social propagation affects for better prediction of cab drivers’ future behaviors. To be specific, we first investigate the correlation between drivers’ skills and their mutual interactions in the latent vehicle-to-vehicle network, which intuitively indicates the effects of social influences. Along this line, by leveraging the classic social influence theory, we develop a two-stage framework for quantitatively revealing the latent driving pattern propagation within taxi drivers. Comprehensive experiments on a real-word data set collected from the New York City clearly validate the effectiveness of our proposed framework on predicting future taxi driving behaviors, which also support the hypothesis that social factors indeed improve the predictability of driving behaviors.

Filed under: Big Data | Time Series and Stream Mining | Mining Rich Data Types