Machine Learning
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A Survey of Context-Aware Mobile Computing Research
A Survey of Context-Aware Mobile Computing Research
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Geographic location tags on digital images
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Where were we: communities for sharing space-time trails
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Learning transportation mode from raw gps data for geographic applications on the web
Proceedings of the 17th international conference on World Wide Web
GeoLife: Managing and Understanding Your Past Life over Maps
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
A Flexible Spatio-Temporal Indexing Scheme for Large-Scale GPS Track Retrieval
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Mobility detection using everyday GSM traces
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Activity classification using realistic data from wearable sensors
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Collaborative location and activity recommendations with GPS history data
Proceedings of the 19th international conference on World wide web
T-drive: driving directions based on taxi trajectories
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Cooperative transit tracking using smart-phones
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Learning travel recommendations from user-generated GPS traces
ACM Transactions on Intelligent Systems and Technology (TIST)
Recommending friends and locations based on individual location history
ACM Transactions on the Web (TWEB)
Smart itinerary recommendation based on user-generated GPS trajectories
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
SeMiTri: a framework for semantic annotation of heterogeneous trajectories
Proceedings of the 14th International Conference on Extending Database Technology
SeTraStream: semantic-aware trajectory construction over streaming movement data
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Predicting handoffs in 3G networks
MobiHeld '11 Proceedings of the 3rd ACM SOSP Workshop on Networking, Systems, and Applications on Mobile Handhelds
Balancing energy, latency and accuracy for mobile sensor data classification
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
ER'11 Proceedings of the 30th international conference on Conceptual modeling
Predicting handoffs in 3G networks
ACM SIGOPS Operating Systems Review
Recruitment framework for participatory sensing data collections
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
Social itinerary recommendation from user-generated digital trails
Personal and Ubiquitous Computing
Manage and query generic moving objects in SECONDO
Proceedings of the VLDB Endowment
A privacy-by-design approach to location sharing
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
GMOBench: a benchmark for generic moving objects
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
A generic data model for moving objects
Geoinformatica
GPS-based framework towards more realistic and real-time construction equipment operation simulation
Proceedings of the Winter Simulation Conference
Annotating mobile phone location data with activity purposes using machine learning algorithms
Expert Systems with Applications: An International Journal
Proceedings of the 5th Workshop on Mobile Video
Inferring User Context from Spatio-Temporal Pattern Mining for Mobile Application Services
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Semantic trajectories: Mobility data computation and annotation
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
On heterogeneity in mobile sensing applications aiming at representative data collection
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Semantic trajectories modeling and analysis
ACM Computing Surveys (CSUR)
Accelerometer-based transportation mode detection on smartphones
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
On the Management and Analysis of Our LifeSteps
ACM SIGKDD Explorations Newsletter
Hi-index | 0.01 |
User mobility has given rise to a variety of Web applications, in which the global positioning system (GPS) plays many important roles in bridging between these applications and end users. As a kind of human behavior, transportation modes, such as walking and driving, can provide pervasive computing systems with more contextual information and enrich a user's mobility with informative knowledge. In this article, we report on an approach based on supervised learning to automatically infer users' transportation modes, including driving, walking, taking a bus and riding a bike, from raw GPS logs. Our approach consists of three parts: a change point-based segmentation method, an inference model and a graph-based post-processing algorithm. First, we propose a change point-based segmentation method to partition each GPS trajectory into separate segments of different transportation modes. Second, from each segment, we identify a set of sophisticated features, which are not affected by differing traffic conditions (e.g., a person's direction when in a car is constrained more by the road than any change in traffic conditions). Later, these features are fed to a generative inference model to classify the segments of different modes. Third, we conduct graph-based postprocessing to further improve the inference performance. This postprocessing algorithm considers both the commonsense constraints of the real world and typical user behaviors based on locations in a probabilistic manner. The advantages of our method over the related works include three aspects. (1) Our approach can effectively segment trajectories containing multiple transportation modes. (2) Our work mined the location constraints from user-generated GPS logs, while being independent of additional sensor data and map information like road networks and bus stops. (3) The model learned from the dataset of some users can be applied to infer GPS data from others. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change-point-based segmentation method and Decision Tree-based inference model, we achieved prediction accuracy greater than 71 percent. Further, using the graph-based post-processing algorithm, the performance attained a 4-percent enhancement.