Daily activity pattern recognition by using support vector machines with multiple classes
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References listed on IDEAS
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- Mengistu, Mulu Getachew & Simane, Belay & Eshete, Getachew & Workneh, Tilahun Seyoum, 2016. "Factors affecting households' decisions in biogas technology adoption, the case of Ofla and Mecha Districts, northern Ethiopia," Renewable Energy, Elsevier, vol. 93(C), pages 215-227.
- Han, Gain & Sohn, Keemin, 2016. "Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 121-135.
- Sun, Lijun & Axhausen, Kay W., 2016. "Understanding urban mobility patterns with a probabilistic tensor factorization framework," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 511-524.
- Mahdieh Allahviranloo & Will Recker, 2015. "Mining activity pattern trajectories and allocating activities in the network," Transportation, Springer, vol. 42(4), pages 561-579, July.
- Siyu Li & Der-Horng Lee, 2017. "Learning daily activity patterns with probabilistic grammars," Transportation, Springer, vol. 44(1), pages 49-68, January.
More about this item
KeywordsActivity pattern recognition; Activity sequence; Support Vector Machines (SVMs); Hidden Markov Models (HMMs);
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