Results 241 to 250 of about 15,987,795 (287)

Balancing innovation and risk: artificial intelligence in travel medicine. [PDF]

Trop Dis Travel Med Vaccines
van Genderen PJJ.
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Prevalence of financial toxicity and construction of a risk prediction model for lung cancer patients at 6 months after discharge. [PDF]

Front Med (Lausanne)
Liu N, Wang X, Pan X, Tang C, Li X, Zhang Y, Zhao Y.   +6 more
europepmc   +1 more source

Uncertainty-aware estimation, planning, and control for tracking multiple drifting patches in flow fields. [PDF]

Front Robot AI
Akanji DO, Kaipa KN, Wei C.
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Sustainable EV adoption with clustering and predictive modelling for optimal charging infrastructure in the West Midlands and North East UK. [PDF]

Sci Rep
Cavus M, Wang S, Deb S, Sharma A, Bell M, Dissanayake D.   +5 more
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Semi-Active Suspension Systems: From Advanced Control Algorithms to Emerging Off-Road and Agricultural Applications. [PDF]

Sensors (Basel)
Jia W, Sun K, Dong X.
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Beyond the cruise ship: Andes hantavirus and the neglected one health dimensions of global outbreak preparedness. [PDF]

Lancet Reg Health Am
Iwu CD, Pettan-Brewer C, Iwu-Jaja C, Rabinowitz P, Meisner J.   +4 more
europepmc   +1 more source

Prediction model for travel time variability

, 2015
Fosgerau, Mogens, Hjorth, Katrine; id_orcid 0000-0002-6757-3335, Jensen, Thomas Christian; id_orcid 0000-0002-1942-2014, Prameswari, Niken   +3 more
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Prediction Intervals to Account for Uncertainties in Travel Time Prediction

IEEE Transactions on Intelligent Transportation Systems, 2011
The accurate prediction of travel times is desirable but frequently prone to error. This is mainly attributable to both the underlying traffic processes and the data that are used to infer travel time. A more meaningful and pragmatic approach is to view travel time prediction as a probabilistic inference and to construct prediction intervals (PIs ...
Abbas Khosravi, Saeid Nahavandi, Douglas Creighton   +2 more
exaly   +2 more sources

Short-term travel time prediction

Transportation Research Part C: Emerging Technologies, 2003
Abstract Effective prediction of travel times is central to many advanced traveler information and transportation management systems. In this paper we propose a method to predict freeway travel times using a linear model in which the coefficients vary as smooth functions of the departure time.
John A Rice
exaly   +2 more sources

Multimodal Traffic Travel Time Prediction

2021 International Joint Conference on Neural Networks (IJCNN), 2021
With the continuous growth of urban population, it is urgent for people to accurately plan the travel time. Therefore, travel time prediction of urban areas has become a key research direction in the field of smart cities. At present, several studies on travel time prediction are only conducted on a single mode, where the prediction process only treats
Shizhen Fan, Jianbo Li, Zhiqiang Lv, Aite Zhao   +3 more
openaire   +2 more sources