Forecasting transport mode use with support vector machines based approach (CROSBI ID 239349)
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Semanjski, Ivana ; Lopez Aguirre, Angel Javier ; Gautama, Sidharta
engleski
Forecasting transport mode use with support vector machines based approach
The paper explores potential to forecast what transport mode one will use for his/her next trip. The support vector machines based approach learns from individual's behavior (validated GPS tracks) to support smart city transport planning services. The overall success rate, in forecasting the transport mode, is 82 %, with lower confusion for private car, bike and walking.
GNSS, Transport mode, Crowdsourceing, Travel behavior, Smart city, Forecasting, Pre-travel information service, Support vector machines, Smartphones
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