Inferring transportation modes from GPS trajectories using a convolutional neural network
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There is work on inferring people's travel modes and destinations from social media or mobile phone sensors such as GPS.
“There is also work on inferring people's travel modes and destinations from social media or various mobile phone sensors such as GPS (transportation mode detection), e.g., [156,798].”
From Tackling climate change with machine learning (2023) · cites this paper as [156]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
(2014) · Cited by 33,419 · see citing works
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325 · see citing works
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831 · see citing works
(2010) · Cited by 14,134 · see citing works
What Is a Savitzky-Golay Filter? [Lecture Notes]
Schafer, R. W. (2011) · IEEE Signal Processing Magazine · doi:10.1109/msp.2011.941097 · Cited by 1,826 · see citing works
Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition
Scherer, D., Müller, A., & Behnke, S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15825-4_10 · Cited by 1,556 · see citing works
DIRECT AND INVERSE SOLUTIONS OF GEODESICS ON THE ELLIPSOID WITH APPLICATION OF NESTED EQUATIONS
Vincenty, T. (1975) · Survey Review · doi:10.1179/sre.1975.23.176.88 · Cited by 1,302 · 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
Learning transportation mode from raw gps data for geographic applications on the web
Zheng, Y., Liu, L., Wang, L., & Xie, X. (2008) · Proceedings of the 17th international conference on World Wide Web · doi:10.1145/1367497.1367532 · Cited by 616 · 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
Mobility Detection Using Everyday GSM Traces
Sohn, T. Y., Varshavsky, A., LaMarca, A., Chen, M. Y., Choudhury, T., Smith, I., Consolvo, S., Hightower, J. R., Griswold, W. G., & de Lara, E. (2006) · Lecture notes in computer science · doi:10.1007/11853565_13 · Cited by 277 · see citing works
Transportation mode recognition using GPS and accelerometer data
Feng, T., & Timmermans, H. J. P. (2013) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2013.09.014 · Cited by 237 · see citing works
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
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
Eluru, N., Chakour, V., & El-Geneidy, A. (2012) · Public Transport · doi:10.1007/s12469-012-0056-2 · Cited by 120 · see citing works
Development of a method for detecting jerks in safety critical events
Bagdadi, O., & Várhelyi, A. (2012) · Accident Analysis & Prevention · doi:10.1016/j.aap.2012.03.032 · Cited by 118 · see citing works
Vehicle classification using GPS data
Sun, Z., & Ban, X. J. (2013) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2013.09.015 · Cited by 110 · 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
Wu, L., Yang, B., & Jing, P. (2016) · Information · doi:10.3390/info7040067 · Cited by 90 · see citing works
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
Detecting Transportation Modes Using Deep Neural Network
Wang, H., GaoJun, L. I. U., Duan, J., et al. (2017) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2016edl8252 · Cited by 74 · see citing works
Geolife GPS trajectory dataset - User Guide
Zheng, Y. L., Fu, H., Xie, X., et al. (2011) · Cited by 70 · see citing works
Travel mode estimation for multi-modal journey planner
Mäenpää, H., Lobov, A., & Lastra, J. L. M. (2017) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2017.06.021 · Cited by 60 · see citing works
Classifying means of transportation using mobile sensor data
Nick, T., Coersmeier, E., Geldmacher, J., et al. (2010) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596549 · Cited by 51 · see citing works
Eftekhari, H. R., & Ghatee, M. (2016) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2016.06.005 · Cited by 47 · see citing works
Jahangiri, A., & Rakha, H. A. (2014) · Transportation Research Board 93rd Annual MeetingTransportation Research Board · Cited by 35 · see citing works
Wu, L., Yang, B., & Jing, P. (2016) · Preprints.org · doi:10.20944/preprints201607.0075.v1 · Cited by 12 · see citing works
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
TraMiner: vision-based analysis of locomotion traces for cognitive assessment in smart-homes
Zolfaghari, S., Khodabandehloo, E., & Riboni, D. (2022) · Cognitive Computation · doi:10.1007/s12559-020-09816-3 · Cited by 29 · see citing works
- Authors
- Dabiri, S., & Heaslip, K.
- Affiliations
- Virginia Tech (US)
- Corresponding author
- Dabiri, Sina
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 86
- Pages
- 360-371
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W2771098373
- Cited by
- 405
- References
- 31
- Citations by year
- 2026: 25 · 2025: 33 · 2024: 53 · 2023: 50 · 2022: 57 · 2021: 61 · 2020: 62 · 2019: 44 · 2018: 20
- FWCI
- 51.65
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Transportation Research Part C Emerging Technologies (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (public-domain)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Physical Sciences › Computer Science › Signal Processing › Data Management and Algorithms
- Keywords
- GPS tracks, convolutional neural networks, travel demand analysis, smartphone mobility data, motion features, CNN architecture design, CNN ensemble, travel behavior
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