Smartphone-based transport mode detection for elderly care

2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom) ◽

10.1109/healthcom.2016.7749465 ◽

2016 ◽

Author(s):

Nuno Cardoso ◽

Joao Madureira ◽

Nuno Pereira

Keyword(s):

Elderly Care ◽

Transport Mode ◽

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N-Gram Based Transport Mode Detection Models for Energy Constrained Devices

10.1109/inista52262.2021.9548116 ◽

2021 ◽

Author(s):

Kadir Can Cetin ◽

H. Irem Turkmen ◽

M. Amac Guvensan

Keyword(s):

Transport Mode ◽

Mode Detection ◽

Energy Constrained ◽

N Gram ◽

Constrained Devices

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Combining Bayesian Inference and Clustering for Transport Mode Detection from Sparse and Noisy Geolocation Data

Machine Learning and Knowledge Discovery in Databases - Lecture Notes in Computer Science ◽

10.1007/978-3-030-10997-4_35 ◽

2019 ◽

pp. 569-584 ◽

Author(s):

Danya Bachir ◽

Ghazaleh Khodabandelou ◽

Vincent Gauthier ◽

Mounim El Yacoubi ◽

Eric Vachon

Keyword(s):

Bayesian Inference ◽

Transport Mode ◽

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Transport Mode Detection when Fine-grained and Coarse-grained Data Meet

2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE) ◽

10.1109/icite.2018.8492673 ◽

2018 ◽

Author(s):

Fereshteh Asgari ◽

Stephan Clemencon

Keyword(s):

Coarse Grained ◽

Transport Mode ◽

Fine Grained ◽

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A fuzzy logic based transport mode detection framework in urban environment

Journal of Intelligent Transportation Systems ◽

10.1080/15472450.2018.1436968 ◽

2018 ◽

Vol 22 (6) ◽

pp. 478-489 ◽

Author(s):

Rahul Deb Das ◽

Stephan Winter

Keyword(s):

Fuzzy Logic ◽

Urban Environment ◽

Transport Mode ◽

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The Devil Is in the Details: An Efficient Convolutional Neural Network for Transport Mode Detection

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2021.3110949 ◽

2021 ◽

pp. 1-11

Author(s):

Hugues Moreau ◽

Andrea Vassilev ◽

Liming Chen

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transport Mode ◽

Mode Detection ◽

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A spatio-temporal approach for identifying the sample size for transport mode detection from GPS-based travel surveys: A case study of London’s road network

Transportation Research Part C Emerging Technologies ◽

10.1016/j.trc.2013.11.006 ◽

2014 ◽

Vol 43 ◽

pp. 176-187 ◽

Author(s):

Adel Bolbol ◽

Tao Cheng ◽

Ioannis Tsapakis

Keyword(s):

Sample Size ◽

Road Network ◽

Transport Mode ◽

Mode Detection ◽

Spatio Temporal

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Recognizing User’s Activity and Transport Mode Detection: Maintaining Low-Power Consumption

Machine Learning and Knowledge Discovery in Databases - Communications in Computer and Information Science ◽

10.1007/978-3-030-43887-6_3 ◽

2020 ◽

pp. 21-37

Author(s):

Fitore Muharemi ◽

Egzon Syka ◽

Doina Logofatu

Keyword(s):

Power Consumption ◽

Low Power ◽

Low Power Consumption ◽

Transport Mode ◽

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Fog Transport Mode Detection

Proceedings of the 21st International Middleware Conference Doctoral Symposium ◽

10.1145/3429351.3431743 ◽

2020 ◽

Author(s):

Mahdieh Kamalian

Keyword(s):

Transport Mode ◽

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Transport mode detection based on mobile phone network data: A systematic review

Transportation Research Part C Emerging Technologies ◽

10.1016/j.trc.2019.02.008 ◽

2019 ◽

Vol 101 ◽

pp. 297-312 ◽

Author(s):

Haosheng Huang ◽

Yi Cheng ◽

Robert Weibel

Keyword(s):

Systematic Review ◽

Mobile Phone ◽

Network Data ◽

Transport Mode ◽

Mode Detection ◽

Mobile Phone Network

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On the requirements on spatial accuracy and sampling rate for transport mode detection in view of a shift to passive signalling data

Transportation Research Part C Emerging Technologies ◽

10.1016/j.trc.2020.01.021 ◽

2020 ◽

Vol 114 ◽

pp. 99-117 ◽

Author(s):

O. Burkhard ◽

H. Becker ◽

R. Weibel ◽

K.W. Axhausen

Keyword(s):

Sampling Rate ◽

Spatial Accuracy ◽

Transport Mode ◽

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