Exploring the Social Learning of Taxi Drivers in Latent Vehicle-to-Vehicle Networks


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 guidance for improving the profit and work efficiency. However, limited scopes focus on the latent social interactions within cab drivers, and corresponding social learning mechanism to share driving behavior patterns has been largely ignored. To that end, in this paper, we propose a comprehensive study to discover how social learning affects taxi drivers’ driving behaviors. To be specific, by leveraging the classic social influence theory, we develop a two-stage framework for quantitatively measuring the latent propagation of driving patterns within taxi drivers. Validations on a real-word data set collected from the New York City clearly verify the effectiveness of our proposed framework with better explanation of future taxi driving pattern evolution, which prove the hypothesis that social factors indeed improve the predictability of taxi driving behaviors, and further reveal some interesting rules on social learning mechanism.

In IEEE Transactions on Mobile Computing (TMC)