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

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 12,953 · see citing works

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 · see citing works

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 798 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

GpsTunes

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 · see citing works

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 · see citing works

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 · see citing works

Toward tighter integration of web search with a geographic information system

Tezuka, T., Kurashima, T., & Tanaka, K. (2006) · International World Wide Web Conference (WWW) · doi:10.1145/1135777.1135821 · Cited by 58 · see citing works

Geotracker

Chen, Y.-F. R., Fabbrizio, G. D., Gibbon, D. C., Jana, R., Jora, S., Renger, B., & Wei, B. (2007) · International World Wide Web Conference (WWW) · doi:10.1145/1242572.1242579 · Cited by 53 · see citing works

Six in the city

Mitchell, K., McCaffery, D., Metaxas, G., Finney, J., Schmid, S., & Scott, A. C. (2003) · Annual Workshop on Network and Systems Support for Games (NetGames) · doi:10.1145/963900.963909 · Cited by 49 · see citing works

M3I in a Pedestrian Navigation & Exploration System

Wasinger, R., Stahl, C., & Krüger, A. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45233-1_51 · Cited by 40 · see citing works

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 · see citing works

Trajectory Data Mining

Zheng, Y. (2015) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2743025 · Cited by 1,667 · see citing works

Urban Computing

Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2629592 · Cited by 1,514 · see citing works

A 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 · see citing works

GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory.

Zheng, Y., Xie, X., & Ma, W. (2010) · Cited by 1,117 · see citing works

T-drive

Yuan, 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 · see citing works

Understanding 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 · see citing works

Map-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 817 · see citing works

Mining 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 668 · see citing works

Collaborative 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 · see citing works

PEIR, the personal environmental impact report, as a platform for participatory sensing systems research

Mun, 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 665 · see citing works

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 485 · see citing works

Recommending 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 479 · see citing works

Transportation 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 · see citing works

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 · see citing works

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 · see citing works

T-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 · see citing works

A 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 397 · see citing works

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 · see citing works

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 · see citing works

Urban 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 300 · see citing works

Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data

Jahangiri, A., & Rakha, H. A. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2405759 · Cited by 292 · see citing works

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 · see citing works

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 · see citing works

Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects

Dodge, S., Weibel, R., & Forootan, E. (2009) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2009.07.008 · Cited by 208 · see citing works

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 · see citing works

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 · see citing works

GeoLife2.0: 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 · see citing works

Inferring 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 · see citing works

Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers

Xiao, Z., Wang, Y., Fu, K., & Wu, F. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6020057 · Cited by 186 · see citing works

Location-Based Social Networks: Users

Zheng, Y. (2011) · Springer eBooks · doi:10.1007/978-1-4614-1629-6_8 · Cited by 187 · see citing works

When 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 · see citing works

Towards 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 · see citing works

Transportation 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 · see citing works

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 · see citing works

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 · see citing works

Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data

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 · see citing works

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 · see citing works

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 148 · see citing works

On 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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Inferring 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 · see citing works

Learning Transportation Modes From Smartphone Sensors Based on Deep Neural Network

Fang, S.-H., Fei, Y.-X., Xu, Z.-Z., & Tsao, Y. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2017.2737825 · Cited by 97 · see citing works

Identifying Active Travel Behaviors in Challenging Environments Using GPS, Accelerometers, and Machine Learning Algorithms

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 · see citing works

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 · see citing works

Another Tale of Two Cities: Understanding Human Activity Space Using Actively Tracked Cellphone Location Data

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 · see citing works

Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data

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 · see citing works

Travel Mode Detection Using GPS Data and Socioeconomic Attributes Based on a Random Forest Classifier

Bao, W., Gao, L., & Juan, Z. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2723523 · Cited by 114 · see citing works

CrowdAtlas

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 · see citing works

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 · see citing works

Ambulation: 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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Travel Mode Detection Based on GPS Raw Data Collected by Smartphones: A Systematic Review of the Existing Methodologies

Wu, L., Yang, B., & Jing, P. (2016) · Information · doi:10.3390/info7040067 · Cited by 90 · see citing works

How to compare movement? A review of physical movement similarity measures in geographic information science and beyond

Ranacher, P., & Tzavella, K. (2014) · Cartography and Geographic Information Science · doi:10.1080/15230406.2014.890071 · Cited by 89 · see citing works

Deep Trajectory Recovery with Fine-Grained Calibration using Kalman Filter

Wang, J., Wu, N., Lu, X.-X., Zhao, W., & Feng, K. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2940950 · Cited by 105 · see citing works

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 · see citing works

A 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 · see citing works

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 87 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Tracking and Predicting Depressive Symptoms of Adolescents Using Smartphone-Based Self-Reports, Parental Evaluations, and Passive Phone Sensor Data: Development and Usability Study (Preprint)

Cao, J., Truong, A. L., Banu, S., Shah, A. A., Sabharwal, A., & Moukaddam, N. (2019) · doi:10.2196/preprints.14045 · Cited by 81 · see citing works

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 · see citing works

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 · see citing works

Learning 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 · see citing works

Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics

Bantis, T., & Haworth, J. (2017) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2017.05.003 · Cited by 76 · see citing works

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 · see citing works

Activity recognition: linking low-level sensors to high-level intelligence

Yang, Q. (2009) · Cited by 73 · see citing works

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 · see citing works

Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data

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 · see citing works

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 69 · see citing works

Automatic Transportation Mode Classification Using a Deep Reinforcement Learning Approach With Smartphone Sensors

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 65 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Transportation 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 · see citing works

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 · see citing works

Smartphone Transportation Mode Recognition Using a Hierarchical Machine Learning Classifier and Pooled Features From Time and Frequency Domains

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 · see citing works

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 · see citing works

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 · see citing works

A generic data model for moving objects

Xu, J., & Güting, R. H. (2012) · GeoInformatica · doi:10.1007/s10707-012-0158-7 · Cited by 58 · see citing works

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 · see citing works

RP-DBSCAN

Song, H., & Lee, J.-G. (2018) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3183713.3196887 · Cited by 57 · see citing works

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 · see citing works

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 52 · see citing works

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 485 · see citing works

How are you Riding? Transportation Mode Identification from Raw GPS Data

Andrade, T., & da Gama, J. M. P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16474-3_53 · Cited by 3 · see citing works

GPS Trajectories with transportation mode labels

Zheng, Y. L. (2010) · Cited by 4 · see citing works

Identifying Transportation Modes from Raw GPS Data

Zhu, Q., Zhu, M., Li, M., Fu, M., Huang, Z., Gan, Q., & Zhou, Z. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-2053-7_35 · Cited by 31 · see citing works

Understanding 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 · see citing works