T-Finder: A Recommender System for Finding Passengers and Vacant Taxis

By Nicholas Jing Yuan, Yu Zheng, Liuhang Zhang, Xing Xie. IEEE Transactions on Knowledge and Data Engineering, 2013

443 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Mobile Crowd Sensing and Computing (2015, background): “For example, T-Finder [Yuan et al. 2013] was a recommending system that could guide taxi drivers to the places where passengers could more easily be picked up.”
Trajectory Data Mining (2015, methodology): “After the map-matching process, T-Drive regards the top-k road segments frequently traversed by taxicabs as landmark nodes (i.e., the red points shown in Figure 19(a)).”
Travel time estimation of a path using sparse trajectories (2014, background): “Real-time estimation of the travel time of a path, which is represented by a sequence of connected road segments, is of great importance for traffic monitoring [1], finding driving directions [20], ridesharing [13] and taxi dispatching [22].”
Urban Computing: Concepts, Methodologies, and Applications (2014, background): “2, where Yuan et al. [2012a] inferred the functional regions in a city using road network data, points of interest, and human mobility learned from a large number of taxi trips.”