The Development of a Smart Taxicab Scheduling System: A Multi-source Data Fusion Perspective

摘要

Recent advances in vehicular networks, GPS and smartphone technologies have changed the paradigm of intelligent taxicab systems. Indeed, taxicab trajectories and online calling information have enabled us to provide more efficient and personalized services. However, existing approaches are not sufficient in exploiting cooperative scheduling techniques and utilizing real time calling information. To this end, in this paper, we model the time-varying regularities of traffic flows, activity ratios of passengers, and unoccupied taxicabs of road segments by mining statistical data on taxicab trajectories. Along this line, we propose a novel approach to calculate the expected revenue of possible routes for individual taxicabs while considering the influence of others, and at the same time, advance a dynamic taxicab scheduling mechanism with online taxicab calling information. Finally, we evaluate our algorithm on real-world taxicab data. Experimental results demonstrate that our approach outperforms existing alternative solutions in terms of average revenue of taxi drivers.