Where to Find My Next Passenger?
Proceedings of the 13th ACM International Conference on Ubiquitous Computing (Ubicomp 2011) |
We present a recommender for taxi drivers and people expecting to take a taxi, using the knowledge of 1) passengers’ mobility patterns and 2) taxi drivers’ pick-up behaviors learned from the GPS trajectories of taxicabs. First, this recommender provides taxi drivers with some locations (and the routes to these locations), towards which they are more likely to pick up passengers quickly (during the routes or at the parking places) and maximize the profit. Second, it recommends people with some locations (within a walking distance) where they can easily find vacant taxis. In our method, we propose a parking place detection algorithm and learn the above knowledge (represented by probabilities) from trajectories. Then, we feed the knowledge into a probabilistic model which estimates the profit of a parking place for a particular driver based on where and when the driver requests for the recommendation. We validate our recommender using trajectories generated by 12,000 taxis in 110 days.
This video showcases three application scenarios that have been enabled in the urban computing project. 1) Finding smart driving direction based on taxi trajectories; 2) A passenger-cabbie recommender system; 3) Glean the flawed urban planning in terms of people's city-wide mobility patterns learned from taxi trajectories. Contact: Yu Zheng, Researcher at Microsoft Research Asia, firstname.lastname@example.org [video width="854" height="480" mp4="https://www.microsoft.com/en-us/research/wp-content/uploads/2011/10/urban_planning_Ubicomp2011_yuzheng-2.mp4"][/video]