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Maschinelles Lernen
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Smart Event Forecast for Seaports (SMECS) : Schlussbericht : Projektlaufzeit: 01.09.2017-29.02.2020
Estimating wildlife strike costs at US airports: A machine learning approach
Machine learning approach to ship fuel consumption: A case of container vessel
Exploring nonlinear effects of the built environment on ridesplitting: Evidence from Chengdu
Learning efficient haptic shape exploration with a rigid tactile sensor array
Examining threshold effects of built environment elements on travel-related carbon-dioxide emissions
Real-time demand forecasting for an urban delivery platform
Generating bunkering statistics from AIS data: A machine learning approach
Non-linear associations between the built environment and the physical activity of children
Examining nonlinear and interaction effects of multiple determinants on airline travel satisfaction
Machine learning in information systems - a bibliographic review and open research issues
Nonlinear effects of the built environment on metro-integrated ridesourcing usage
Machine learning estimates of plug-in hybrid electric vehicle utility factors
Modeling time-of-day car use behavior: A Bayesian network approach
Using explainable machine learning to understand how urban form shapes sustainable mobility
Spatial disparity of individual and collective walking behaviors: A new theoretical framework
Sharing behavior in ride-hailing trips: A machine learning inference approach