Where to find my next passenger
By Jing Yuan, Yu Zheng, Liuhang Zhang, Xing Xie, Guangzhong Sun. Ubiquitous Computing, 2011
396 citations recorded by the source corpus.
Subfields: Automotive Engineering, Transportation
View Paper: https://www.semanticscholar.org/paper/514f06b1612e0b515ab5aceb9fc313dc1bed9b91
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In Automotive Engineering
In Transportation
Citations in the corpus
- T-share: A large-scale dynamic taxi ridesharing service (2013, background): “[23][24] proposed a system that suggests some parking places for an individual taxi driver towards which they can find passengers quickly and maximize the profit of the next trip.”
- 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)).”
- T-Finder: A Recommender System for Finding Passengers and Vacant Taxis (2013, methodology): “In our previous paper [18], we evaluated the parking place detection method and the taxi recommender (using strategies S1–S3).”
- Discovering Urban Functional Zones Using Latent Activity Trajectories (2015, background): “According report by the Beijing Transportation Bureau, taxi trips occupy over 12 percent of traffic flows on road surfaces[19].”
- Prediction of urban human mobility using large-scale taxi traces and its applications (2012, background): “[20] presented a recommender system, for taxi drivers and passengers wishing to hail a taxi, using the knowledge of passenger mobility patterns and taxi driver pick-up behaviors learned from the GPS trajectories of taxicabs.”
- Predicting Taxi–Passenger Demand Using Streaming Data (2013, methodology): “presented in [28] a complete work containing methods about the following: 1) how to divide the urban area into pick-up zones using spatial clustering;”
- Inferring human mobility patterns from taxicab location traces (2013, positive): “A similar approach was used in [21], where the authors differentiate be-tween a taxi cab that is parked and moving in slow traffic.”
- Discovering regions of different functions in a city using human mobility and POIs (2012, background): “According to the report of Beijing Transportation Bureau, the taxi trips occupy over 12 percent of traffic flows on road surfaces[20].”
- Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips (2013, background): “[39] proposed recommendation systems for taxi drivers to optimize the cost of finding passengers (and therefore reduce gas emission).”
- When things matter: A survey on data-centric internet of things (2016, background): “Taxi drivers in the city would also be able to better serve prospective passengers by learning passenger's mobility patterns and other taxi drivers' serving behaviors through the help of IoT technologies (Yuan et al., 2011).”