T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence

By Jing Yuan, Yu Zheng, Xing Xie, Guangzhong Sun. IEEE Transactions on Knowledge and Data Engineering, 2013

373 citations recorded by the source corpus.

Subfields: Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1d8b0082a40cdc02086fc31047bfc3c642a98f0c

How researchers see this paper

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

In Transportation

1. Guoliang Li
2. Haitao Yuan
3. Junbo Zhang
4. Yu Zheng
5. João Gama
6. Wei Cao
7. Jian Li
8. Sajal K. Das

In Signal Processing

1. Guoliang Li
2. Haitao Yuan
3. Junbo Zhang
4. João Gama
5. Francesco Restuccia
6. Sajal K. Das
7. Yu Zheng
8. Xing Xie

Citations in the corpus

• When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks (2018, background): “(Yuan et al. 2013) built a landmark graph based on the historical trajectories of taxis, where each landmark represents a single road.”
• FIRST (2018, methodology): “Finally, an optimization algorithm (MOA) efficiently solves the MOP by using the results from CVP and LEA, and computes the minimum number of MTPs to achieve desired classification accuracy; (3) We extensively evaluate the performance of FIRST by considering a mobile crowdsensing (MCS) application f”
• 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)).”
• Predicting Taxi–Passenger Demand Using Streaming Data (2013, background): “Intelligent transportation systems for efficient taxi dispatching [1], time-saving route finding [2], [3], fuel-saving routing [4], and taxi sharing [5] are already successfully exploring these kind of data and/or interfaces.”
• Urban Computing: Concepts, Methodologies, and Applications (2014, neutral): “…the geo­tagged tweets, photos, and check-ins, can help understand not only an individual s life patterns [Ye et al. 2009] but also a city s dynamics [Cranshaw 2012], topics [Yin et al. 2011], behavior patterns [Wakamiya et al. 2012], or lifestyles [Yuan et al. 2013a] when used aggregately.”
• A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, background): “For example, Yuan et al. [250] provide a dataset, which is a sample of trajectories from Microsoft Research T-Drive project, generated by over 10,000 taxicabs in a week of 2008 in Beijing.”