Understanding transportation modes based on GPS data for web applications

By Yu Zheng, Yukun Chen, Quannan Li, Xing Xie, Wei-Ying Ma. TWEB, 2010

518 citations recorded by the source corpus.

Subfields: Geography, Planning and Development, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b2fa408eaced1fa8f4735ffa504981b30b469891

How researchers see this paper

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

In Geography, Planning and Development

1. Yu Zheng
2. Xing Xie
3. Wei-Ying Ma
4. Ouri Wolfson
5. Licia Capra
6. Hai Yang
7. Jing Yuan
8. Guangzhong Sun

In Transportation

1. Yu Zheng
2. Xing Xie
3. Jing Yuan
4. Ouri Wolfson
5. Licia Capra
6. Guangzhong Sun
7. Hai Yang
8. Wei-Ying Ma

In Signal Processing

1. Yu Zheng
2. Xing Xie
3. Jing Yuan
4. Ouri Wolfson
5. Guangzhong Sun
6. Licia Capra
7. Hai Yang
8. Wei-Ying Ma

Citations in the corpus

• Urban Computing: Concepts, Methodologies, and Applications (2014, methodology): “Y. Zheng, Y. Chen, X. Xie, and W. Y. Ma. 2009a. GeoLife2.”
• GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory (2010, background): “Refer to papers [8, 11, 15] for details.”
• Recruitment Framework for Participatory Sensing Data Collections (2010, background): “The location traces could be augmented with sensor-based information which can help in adding context such as transportation mode (still, walking, running, biking, or driving) [39,40,41].”
• Learning travel recommendations from user-generated GPS traces (2011, unknown): “…real-world location histories, some recommender systems, such as Geowhiz [Horozov et al. 2006], CityVoyager [Takeuchi and Sugimoto 2006], and GeoLife [Zheng et al. 2009a; Li et al. 2008; Zheng et al. 2010b], have been designed to recommend geographic locations like shops or restaurants to users.”
• Accelerometer-based transportation mode detection on smartphones (2013, methodology): “Furthermore, changes from one motorised transportation modality to another are typically separated by easily detectable walking segments [36].”
• Trajectory Data Mining (2015, methodology): “The noise filtering method, which has been used in T-Drive [Yuan et al. 2010a, 2011a, 2013a] and GeoLife [Zheng et al. 2009a; Zheng et al. 2010] projects, first calculates the travel speed of each point in a trajectory based on the time interval and distance between a point and its successor (we call…”
• T-drive: driving directions based on taxi trajectories (2010, background): “7.3 History-Learning-Based Approaches Zheng et al.[17, 16, 15] propose several novel approaches to learn the transportation modes from GPS data.”
• Travel time estimation of a path using sparse trajectories (2014, background): “Floating-Car-Data Approaches: Learning city transportation using floating car data has gained more attention recently [1, 5, 17, 23, 24].”
• NationTelescope: Monitoring and visualizing large-scale collective behavior in LBSNs (2015, background): “For example, understanding collective human mobility in urban area can improve the efficiency of urban transportation systems [3]; analyzing collective social activities can help design personalized location based services [4].”
• A Survey of Methods for Time Series Change Point Detection (2016, unknown): “Examples include decision tree [33][34][36][37], naïve Bayes [33], Bayesian net [34], support vector machine [33][34], nearest neighbor [33][20], hidden Markov model [38][39][33], conditional random field [34], and Gaussian mixture model (GMM) [38][39].”