Learning transportation mode from raw gps data for geographic applications on the web

By Yu Zheng, Like Liu, Longhao Wang, Xing Xie. The Web Conference, 2008

672 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/19da5eedc984b7259c2be3f25b601aa72b7e02f6

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. Vincent Wenchen Zheng
5. Qiang Yang
6. Gao Cong
7. Ouri Wolfson
8. Licia Capra

In Transportation

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

In Signal Processing

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

Citations in the corpus

• PEIR, the personal environmental impact report, as a platform for participatory sensing systems research (2009, methodology): “Also, in addition to using GPS and GSM features [29, 30, 26], future activity classification techniques could leverage additional on-board sensor types (e.”
• Learning travel recommendations from user-generated GPS traces (2011, neutral): “INTRODUCTION Recently, people start recording their outdoor movements with GPS traces for many reasons, such as travel experience sharing [Counts and Smith 2007; GeoLife 2007], life logging [Zheng et al. 2008c, 2008d] and sports activity anal­ysis [SportsDo 2007; Bikely 2006].”
• Collaborative location and activity recommendations with GPS history data (2010, methodology): “Compared with raw GPS points, stay points are more meaningful in representing the locations a user stays by capturing the time duration and vicinity information, and they are commonly used as the basic units in representing the GPS data [ 4 ][6].”
• T-drive: driving directions based on taxi trajectories (2010, methodology): “This data is a part of the released GeoLife dataset[17, 16], and the average sampling interval is about 10s.”
• An Interactive-Voting Based Map Matching Algorithm (2010, methodology): “2) GPS Tracking Data In our experiments, all GPS tracking data is real data collected from the Geolife [25, 26] System.”
• A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, background): “[101, 102] split a trajectory into continuous segments and then design decision tree classifier to classify each segment into four kinds: Driving, Biking, Bus and Walking.”
• A Survey of Methods for Time Series Change Point Detection (2016, neutral): “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].”
• A Survey on Trajectory Data Management, Analytics, and Learning (2020, background): “In the urban data, there are mainly two kinds of trajectory classification problems: 1) similaritybased classification [100]; and 2) transportation modes and activities classification [50, 207] which was reviewed recently by [206].”
• Trajectory Data Mining (2015, methodology): “The dataset can be used to evaluate trajectory classification and activity recognition [Zheng et al. 2008a, 2008b].”
• Mining user similarity based on location history (2008, background): “[21] aim to infer users’ transportation mode, such as walking and driving, etc.”
• Transportation mode detection using mobile phones and GIS information (2011, methodology): “Using the transportation network data, we showed that it is possible to address the weakness of previously proposed solutions [1, 2, 4, 15, 16]; that is, to distinguish between motorized modes such as trains, buses, and cars with high accuracy.”
• Mining interesting locations and travel sequences from GPS trajectories (2009, background): “The work reported in this paper is an important component of our project GeoLife [12][ 19 ][20][21], whose prototype has been internally accessible within Microsoft since Oct.”
• Understanding mobility based on GPS data (2008, methodology): “Inference Model In our previous work [12], four kinds of classification models, including Support Vector Machine (SVM), Decision Tree, Bayesian Net and Conditional Random Field (CRF) were studied.”
• Urban Computing: Concepts, Methodologies, and Applications (2014, methodology): “Y. Zheng, Y. Chen, X. Xie, and W. Y. Ma. 2009a. GeoLife2.”
• T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence (2013, methodology): “This data are a part of the released GeoLife data set [11], and the average sampling interval is about 10 s.”