The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Learning transportation mode from raw gps data for geographic applications on the web
Abstract
Geographic information has spawned many novel Web applications where global positioning system (GPS) plays important roles in bridging the applications and end users. Learning knowledge from users ’ raw GPS data can provide rich context information for both geographic and mobile applications. However, so far, raw GPS data are still used directly without much understanding. In this paper, an approach based on supervised learning is proposed to automatically infer transportation mode from raw GPS data. The transportation mode, such as walking, driving, etc., implied in a user’s GPS data can provide us valuable knowledge to understand the user. It also enables context-aware computing based on user’s present transportation mode and design of an innovative user interface for Web users. Our approach consists of three parts: a change point-
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
Using GPS to learn significant locations and predict movement across multiple users
Ashbrook, D., & Starner, T. E. (2003) · Personal and Ubiquitous Computing · doi:10.1007/s00779-003-0240-0 · Cited by 1,067
Learning and inferring transportation routines
Liao, L., Patterson, D. J., Fox, D., & Kautz, H. (2007) · Artificial Intelligence · doi:10.1016/j.artint.2007.01.006 · Cited by 810
Inferring High-Level Behavior from Low-Level Sensors
Patterson, D. J., Liao, L., Fox, D., & Kautz, H. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39653-6_6 · Cited by 521
Predestination: Inferring Destinations from Partial Trajectories
Krumm, J. C., & Horvitz, E. (2006) · Lecture notes in computer science · doi:10.1007/11853565_15 · Cited by 463
Project Lachesis: Parsing and Modeling Location Histories
Hariharan, R., & Toyama, K. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30231-5_8 · Cited by 257
Geographic location tags on digital images
Toyama, K., Logan, R., & Roseway, A. (2003) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/957013.957046 · Cited by 254
Strachan, S., Eslambolchilar, P., Murray‐Smith, R., Hughes, S. A., & O’Modhrain, S. (2005) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/1085777.1085831 · Cited by 124
Building Personal Maps from GPS Data
Liao, L., Patterson, D. J., Fox, D., & Kautz, H. (2006) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1382.017 · Cited by 104
A mobile application framework for the geospatial web
Simon, R., & Fröhlich, P. (2007) · International World Wide Web Conference (WWW) · doi:10.1145/1242572.1242624 · Cited by 68
Flagged first, then by citations
Research works printed but not linked: 5
- Project, 2004, Proceedings of GIScienc'04 (Hariharan Toyama
- Liao L., 2005, IJCAI MOO05
- 10.5555/1597148.1597206 · DOI
- Lafferty J., 2001, Proceedings of the 18th International Conf. on Machine Learning (San Francisco CA
- Wasinger R., 2003, Exploration System. In Proceedings of MobileHCI '03
Cited by
Sorted by citations
Mining interesting locations and travel sequences from GPS trajectories
Zheng, Y., Zhang, L., Xie, X., & Ma, W. (2009) · International World Wide Web Conference (WWW) · doi:10.1145/1526709.1526816 · Cited by 1,957
Self-citationZheng, Y. (2015) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2743025 · Cited by 1,667
Self-citationZheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2629592 · Cited by 1,514
Self-citationA survey of methods for time series change point detection
Aminikhanghahi, S., & Cook, D. J. (2016) · Knowledge and Information Systems · doi:10.1007/s10115-016-0987-z · Cited by 1,246
GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory.
Zheng, Y., Xie, X., & Ma, W. (2010) · Cited by 1,117
Self-citationYuan, J. Y., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., & Huang, Y. (2010) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/1869790.1869807 · Cited by 1,109
Self-citationUnderstanding mobility based on GPS data
Zheng, Y., Li, Q., Chen, Y., Xie, X., & Ma, W.-Y. (2008) · Proceedings of the 10th international conference on Ubiquitous computing · doi:10.1145/1409635.1409677 · Cited by 1,132
Self-citationMap-matching for low-sampling-rate GPS trajectories
Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., & Huang, Y. (2009) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/1653771.1653820 · Cited by 836
Self-citationMining user similarity based on location history
Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., & Ma, W. (2008) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/1463434.1463477 · Cited by 684
Self-citationCollaborative location and activity recommendations with GPS history data
Zheng, V. W., Zheng, Y., Xie, X., & Yang, Q. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772795 · Cited by 665
Self-citationMun, M., Reddy, S., Shilton, K., Yau, N., Burke, J., Estrin, D. L., Hansen, M. H., Howard, E., West, R. G., & Boda, P. (2009) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/1555816.1555823 · Cited by 669
Understanding transportation modes based on GPS data for web applications
Zheng, Y., Chen, Y., Li, Q., Xie, X., & Ma, W. (2010) · ACM Transactions on the Web · doi:10.1145/1658373.1658374 · Cited by 487
Self-citationRecommending friends and locations based on individual location history
Zheng, Y., Zhang, L., Ma, Z., Xie, X., & Ma, W. (2011) · ACM Transactions on the Web · doi:10.1145/1921591.1921596 · Cited by 483
Self-citationTransportation mode detection using mobile phones and GIS information
Stenneth, L., Wolfson, O., Yu, P. S., & Xu, B. (2011) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2093973.2093982 · Cited by 451
Inferring transportation modes from GPS trajectories using a convolutional neural network
Dabiri, S., & Heaslip, K. (2018) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2017.11.021 · Cited by 405
Learning travel recommendations from user-generated GPS traces
Zheng, Y., & Xie, X. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1889681.1889683 · Cited by 398
Self-citationT-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence
Yuan, J., Zheng, Y., Xie, X., & Sun, G. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2011.200 · Cited by 396
Self-citationA Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation
Yuan, H., & Li, G. (2021) · Data Science and Engineering · doi:10.1007/s41019-020-00151-z · Cited by 412
Processing Raw Data from Global Positioning Systems without Additional Information
Rieser-Schüssler, N., & Axhausen, K. W. (2009) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2105-04 · Cited by 327
An Interactive-Voting Based Map Matching Algorithm
Yuan, J., Zheng, Y., Zhang, C., Xie, X., & Sun, G. (2010) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2010.14 · Cited by 326
Self-citationUrban flow prediction from spatiotemporal data using machine learning: A survey
Xie, P., Li, T., Liu, J., Du, S., Yang, X., & Zhang, J. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.01.002 · Cited by 311
Jahangiri, A., & Rakha, H. A. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2405759 · Cited by 296
Inferring trip purposes and uncovering travel patterns from taxi trajectory data
Li, G., Liu, X., Wu, L., & Liu, Y. (2015) · Cartography and Geographic Information Science · doi:10.1080/15230406.2015.1014424 · Cited by 254
From taxi GPS traces to social and community dynamics
Castro, P. S., Zhang, D., Chen, C., Li, S., & Pan, G. (2013) · ACM Computing Surveys · doi:10.1145/2543581.2543584 · Cited by 255
Dodge, S., Weibel, R., & Forootan, E. (2009) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2009.07.008 · Cited by 208
Transportation mode inference from anonymized and aggregated mobile phone call detail records
Wang, H., Calabrese, F., Lorenzo, G. D., & Ratti, C. (2010) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2010.5625188 · Cited by 207
Finding similar users using category-based location history
Xiao, X., Zheng, Y., Luo, Q., & Xie, X. (2010) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/1869790.1869857 · Cited by 198
Self-citationGeoLife2.0: A Location-Based Social Networking Service
Zheng, Y., Chen, Y., Xie, X., & Ma, W. (2009) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2009.50 · Cited by 192
Self-citationInferring hybrid transportation modes from sparse GPS data using a moving window SVM classification
Bolbol, A., Cheng, T., Tsapakis, I., & Haworth, J. (2012) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2012.06.001 · Cited by 191
Xiao, Z., Wang, Y., Fu, K., & Wu, F. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6020057 · Cited by 186
Location-Based Social Networks: Users
Zheng, Y. (2011) · Springer eBooks · doi:10.1007/978-1-4614-1629-6_8 · Cited by 193
Self-citationWhen Location Meets Social Multimedia
Ji, R., Gao, Y., Liu, W., Xie, X., Tian, Q. C., & Li, X. (2015) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2597181 · Cited by 183
Self-citationTowards mobile intelligence: Learning from GPS history data for collaborative recommendation
Zheng, V. W., Zheng, Y., Xie, X., & Yang, Q. (2012) · Artificial Intelligence · doi:10.1016/j.artint.2012.02.002 · Cited by 179
Self-citationTransportation mode-based segmentation and classification of movement trajectories
Biljecki, F., Ledoux, H., & van Oosterom, P. J. M. (2012) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2012.692791 · Cited by 166
Deriving Personal Trip Data from GPS Data: A Literature Review on the Existing Methodologies
Gong, L., Morikawa, T., Yamamoto, T., & Sato, H. (2014) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2014.07.239 · Cited by 162
Mining GPS data for mobility patterns: A survey
Lin, M., & Hsu, W.-J. (2013) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2013.06.005 · Cited by 157
Dabiri, S., Lu, C., Heaslip, K. P., & Reddy, C. K. A. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2896985 · Cited by 156
Explaining the power-law distribution of human mobility through transportationmodality decomposition
Zhao, K., Musolesi, M., Hui, P., Rao, W., & Tarkoma, S. (2015) · Scientific Reports · doi:10.1038/srep09136 · Cited by 154
Inferring social ties between users with human location history
Xiao, X., Zheng, Y., Luo, Q., & Xie, X. (2012) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-012-0117-z · Cited by 147
Self-citationOn Distributed Fuzzy Decision Trees for Big Data
Segatori, A., Marcelloni, F., & Pedrycz, W. (2017) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2016.2646746 · Cited by 145
Discovering places of interest in everyday life from smartphone data
Montoliu, R., Blom, J., & Gática-Pérez, D. (2012) · Multimedia Tools and Applications · doi:10.1007/s11042-011-0982-z · Cited by 137
Accelerometer Based Transportation Mode Recognition on Mobile Phones
Wang, S., Chen, C., & Ma, J. (2010) · Asia-Pacific Conference on Wearable Computing Systems · doi:10.1109/apwcs.2010.18 · Cited by 136
Personalized travel route recommendation using collaborative filtering based on GPS trajectories
Cui, G., Luo, J., & Wang, X. (2017) · International Journal of Digital Earth · doi:10.1080/17538947.2017.1326535 · Cited by 131
Classifying transportation mode and speed from trajectory data via deep multi-scale learning
Zhang, R., Xie, P., Wang, C., Liu, G., & Wan, S. (2019) · Computer Networks · doi:10.1016/j.comnet.2019.106861 · Cited by 129
Social itinerary recommendation from user-generated digital trails
Yoon, H., Zheng, Y., Xie, X., & Woo, W. (2011) · Personal and Ubiquitous Computing · doi:10.1007/s00779-011-0419-8 · Cited by 128
Self-citationInferring human activities from GPS tracks
Furletti, B., Cintia, P., Renso, C., & Spinsanti, L. (2013) · ACM SIGKDD International Workshop on Urban Computing (UrbComp) · doi:10.1145/2505821.2505830 · Cited by 123
Learning Transportation Modes From Smartphone Sensors Based on Deep Neural Network
Fang, S., Fei, Y.-X., Xu, Z., & Tsao, Y. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2017.2737825 · Cited by 122
Ellis, K., Godbole, S., Marshall, S., Lanckriet, G., Staudenmayer, J., & Kerr, J. (2014) · Frontiers in Public Health · doi:10.3389/fpubh.2014.00036 · Cited by 123
Discovering human places of interest from multimodal mobile phone data
Montoliu, R., & Gática-Pérez, D. (2010) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/1899475.1899487 · Cited by 121
Xu, Y., Shaw, S., Zhao, Z., Yin, L., Lu, F., Chen, J., Fang, Z., & Li, Q. (2016) · Annals of the American Association of Geographers · doi:10.1080/00045608.2015.1120147 · Cited by 121
Chen, S., Yuan, X., Wang, Z., Guo, C., Liang, J., Wang, Z., Zhang, X., & Zhang, J. (2015) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2467619 · Cited by 117
Bao, W., Gao, L., & Juan, Z. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2723523 · Cited by 114
Wang, Y., Liu, X., Wei, H., Forman, G., Chen, C., & Zhu, Y. (2013) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2462456.2464441 · Cited by 113
GeoLife: Managing and Understanding Your Past Life over Maps
Zheng, Y., Wang, L., Zhang, R., Xie, X., & Ma, W. (2008) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2008.20 · Cited by 112
Self-citationAmbulation: A Tool for Monitoring Mobility Patterns over Time Using Mobile Phones
Ryder, J., Longstaff, B., Reddy, S., & Estrin, D. (2009) · doi:10.1109/cse.2009.312 · Cited by 111
Machine Learning Techniques in ADAS: A Review
Moujahid, A., Tantaoui, M. E., Hina, M., Soukane, A., Ortalda, A., Elkhadimi, A., & Ramdane-Cherif, A. (2018) · International Conference on Advances in Computing and Communication Engineering · doi:10.1109/icacce.2018.8441758 · Cited by 79
Transportation mode detection – an in-depth review of applicability and reliability
Prelipcean, A. C., Gidófalvi, G., & Susilo, Y. O. (2016) · Transport Reviews · doi:10.1080/01441647.2016.1246489 · Cited by 96
Quantifying Interdependent Privacy Risks with Location Data
Olteanu, A.-M., Huguenin, K., Shokri, R., Humbert, M., & Hubaux, J. (2016) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2016.2561281 · Cited by 92
Deep Feature Extraction from Trajectories for Transportation Mode Estimation
Endo, Y., Toda, H., Nishida, K., & Kawanobe, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-31750-2_5 · Cited by 91
Wu, L., Yang, B., & Jing, P. (2016) · Information · doi:10.3390/info7040067 · Cited by 90
Ranacher, P., & Tzavella, K. (2014) · Cartography and Geographic Information Science · doi:10.1080/15230406.2014.890071 · Cited by 89
Deep Trajectory Recovery with Fine-Grained Calibration using Kalman Filter
Wang, J., Wu, N., Lu, X., Zhao, X. W., & Feng, K. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2940950 · Cited by 90
Mining correlation between locations using human location history
Zheng, Y., Zhang, L., Xie, X., & Ma, W. (2009) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/1653771.1653847 · Cited by 89
Self-citationA SURVEY OF CONTEXT-AWARE MOBILE RECOMMENDATIONS
Liu, Q., Ma, H., Chen, E., & Xiong, H. (2013) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622013500077 · Cited by 88
Travel Mode Identification With GPS Trajectories Using Wavelet Transform and Deep Learning
Yu, J. J. Q. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2962741 · Cited by 89
Transportation mode identification with GPS trajectory data and GIS information
Li, J., Pei, X., Wang, X., Yao, D., Zhang, Y. L., & Yue, Y. (2021) · Tsinghua Science & Technology · doi:10.26599/tst.2020.9010014 · Cited by 87
Trip Purpose Identification from GPS Tracks
Montini, L., Rieser-Schüssler, N., Horni, A., & Axhausen, K. W. (2014) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2405-03 · Cited by 87
Effective and efficient trajectory outlier detection based on time-dependent popular route
Zhu, J. P., Jiang, W., Liu, A., Liu, G., & Zhao, L. (2016) · World Wide Web · doi:10.1007/s11280-016-0400-6 · Cited by 84
Automated time activity classification based on global positioning system (GPS) tracking data
Wu, J. Y., Jiang, C., Houston, D., Baker, D. B., & Delfino, R. J. (2011) · Environmental Health · doi:10.1186/1476-069x-10-101 · Cited by 84
Using Smart Phone Sensors to Detect Transportation Modes
Xia, H., Qiao, Y., Jian, J., & Chang, Y. (2014) · Sensors · doi:10.3390/s141120843 · Cited by 83
A Review of GPS Trajectories Classification Based on Transportation Mode
Yang, X., Stewart, K., Tang, L., Xie, Z., & Li, Q. (2018) · Sensors · doi:10.3390/s18113741 · Cited by 82
Cao, J., Truong, A. L., Banu, S., Shah, A. A., Sabharwal, A., & Moukaddam, N. (2019) · doi:10.2196/preprints.14045 · Cited by 81
Developing a passive GPS tracking system to study long-term travel behavior
Marra, A. D., Becker, H., Axhausen, K. W., & Corman, F. (2019) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2019.05.006 · Cited by 81
Trajectory simplification method for location-based social networking services
Chen, Y., Jiang, K., Zheng, Y., Li, C., & Yu, N. (2009) · doi:10.1145/1629890.1629898 · Cited by 79
Self-citationLearning Location Correlation from GPS Trajectories
Zheng, Y., & Xie, X. (2010) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2010.42 · Cited by 76
Self-citationBantis, T., & Haworth, J. (2017) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2017.05.003 · Cited by 76
Semi-Supervised Federated Learning for Travel Mode Identification From GPS Trajectories
Zhu, Y., Liu, Y., Yu, J. J. Q., & Yuan, X. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3092015 · Cited by 79
Activity recognition: linking low-level sensors to high-level intelligence
Yang, Q. (2009) · Cited by 73
Algorithms for compressing GPS trajectory data
Muckell, J., Hwang, J.-H., Lawson, C. T., & Ravi, S. S. (2010) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/1869790.1869847 · Cited by 71
Li, L., Zhu, J., Zhang, H., Tan, H., Du, B., & Ran, B. (2020) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2020.04.005 · Cited by 70
Semi-supervised deep ensemble learning for travel mode identification
Yu, J. J. Q. (2020) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2020.01.003 · Cited by 70
Taherinavid, S., Moravvej, S. V., Chen, Y., Yang, J., Ku, C. S., & Por, L. Y. (2023) · IEEE Access · doi:10.1109/access.2023.3346875 · Cited by 69
Mining human mobility patterns from social geo-tagged data
Comito, C., Falcone, D., & Talia, D. (2016) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2016.06.005 · Cited by 66
Detecting Travel Modes Using Rule-Based Classification System and Gaussian Process Classifier
Xiao, G., Qin, C., & Zhang, C. (2019) · IEEE Access · doi:10.1109/access.2019.2936443 · Cited by 65
Classifying spatial trajectories using representation learning
Endo, Y., Toda, H., Nishida, K., & Ikedo, J. (2016) · International Journal of Data Science and Analytics · doi:10.1007/s41060-016-0014-1 · Cited by 65
Vehicle classification from low-frequency GPS data with recurrent neural networks
Simoncini, M., Taccari, L., Sambo, F., Bravi, L., Salti, S., & Lori, A. (2018) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2018.03.024 · Cited by 68
Theoretically-Efficient and Practical Parallel DBSCAN
Wang, Y., Gu, Y., & Shun, J. (2020) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3318464.3380582 · Cited by 65
Review and evaluation of methods in transport mode detection based on GPS tracking data
Sadeghian, P., Håkansson, J., & Zhao, X. (2021) · Journal of Traffic and Transportation Engineering (English Edition) · doi:10.1016/j.jtte.2021.04.004 · Cited by 64
Map++: A crowd-sensing system for automatic map semantics identification
Aly, H., Basalamah, A., & Youssef, M. (2014) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/sahcn.2014.6990394 · Cited by 64
A Flexible Spatio-Temporal Indexing Scheme for Large-Scale GPS Track Retrieval
Wang, L., Zheng, Y., Xie, X., & Ma, W. (2008) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2008.24 · Cited by 64
Self-citationTransportation Modes Classification Using Sensors on Smartphones
Fang, S., Liao, H.-H., Fei, Y.-X., Chen, K.-H. S., Huang, J.-W., Lu, Y.-D., & Tsao, Y. (2016) · Sensors · doi:10.3390/s16081324 · Cited by 64
Using Context Annotated Mobility Profiles to Recruit Data Collectors in Participatory Sensing
Reddy, S., Shilton, K., Burke, J., Estrin, D. L., Hansen, M. H., & Srivastava, M. B. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-01721-6_4 · Cited by 63
Ashqar, H. I., Almannaa, M., Elhenawy, M. M. Z., Rakha, H. A., & House, L. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2817658 · Cited by 62
Transportation Mode Split with Mobile Phone Data
Qu, Y., Gong, H., & Wang, P. (2015) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2015.56 · Cited by 60
Time-Dependent Popular Routes Based Trajectory Outlier Detection
Zhu, J. P., Jiang, W., Liu, A., Liu, G., & Zhao, L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-26190-4_2 · Cited by 59
A generic data model for moving objects
Xu, J., & Güting, R. H. (2012) · GeoInformatica · doi:10.1007/s10707-012-0158-7 · Cited by 58
On the properties of human mobility
Papandrea, M., Jahromi, K. K., Zignani, M., Gaito, S., Giordano, S., & Rossi, G. P. (2016) · Computer Communications · doi:10.1016/j.comcom.2016.03.022 · Cited by 58
Song, H., & Lee, J.-G. (2018) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3183713.3196887 · Cited by 57
Taxi-RS: Taxi-Hunting Recommendation System Based on Taxi GPS Data
Xu, X., Zhou, J., Liu, Y., Xu, Z. Z., & Zha, X. W. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2371815 · Cited by 52
Convolutional LSTM based transportation mode learning from raw GPS trajectories
Nawaz, A., Zhiqiu, H., Wang, S., Hussain, Y., Khan, I. A., & Khan, Z. U. (2020) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2019.0017 · Cited by 53
Metadata
- Authors
- Zheng, Y., Liu, L., Wang, L., & Xie, X.
- Affiliations
- Microsoft Research Asia (China) (CN)
- Venue
- Proceedings of the 17th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2008-04-21
- Pages
- 247-256
- OpenAlex
- W2143394441
- Cited by
- 616
- References
- 14
- Citations by year
- 2026: 10 · 2025: 27 · 2024: 27 · 2023: 38 · 2022: 29 · 2021: 44 · 2020: 43 · 2019: 46 · 2018: 51 · 2017: 36 · 2016: 56 · 2015: 41 · 2014: 38 · 2013: 33 · 2012: 34
- FWCI
- 80.79
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- GPS data, supervised learning, context-aware computing, change point detection, user activity recognition, user interface design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review