| 1. |
- Acharya, B., et al.
(författare)
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Uncertainty quantification for proton-proton fusion in chiral effective field theory
- 2016
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Ingår i: Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics. - : Elsevier BV. - 0370-2693. ; 760, s. 584-589
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Tidskriftsartikel (refereegranskat)abstract
- We compute the S-factor of the proton-proton (pp) fusion reaction using chiral effective field theory (chi EFT) up to next-to-next-to-leading order (NNLO) and perform a rigorous uncertainty analysis of the results. We quantify the uncertainties due to (i) the computational method used to compute the pp cross section in momentum space, (ii) the statistical uncertainties in the low-energy coupling constants of chi EFT, (iii) the systematic uncertainty due to the chi EFT cutoff, and (iv) systematic variations in the database used to calibrate the nucleon-nucleon interaction. We also examine the robustness of the polynomial extrapolation procedure, which is commonly used to extract the threshold S-factor and its energy-derivatives. By performing a statistical analysis of the polynomial fit of the energy-dependent S-factor at several different energy intervals, we eliminate a systematic uncertainty that can arise from the choice of the fit interval in our calculations. In addition, we explore the statistical correlations between the S-factor and few-nucleon observables such as the binding energies and point-proton radii of H-2,H-3 and He-3 as well as the D-state probability and quadrupole moment of H-2, and the beta-decay of 3H. We find that, with the state-of-the-art optimization of the nuclear Hamiltonian, the statistical uncertainty in the threshold S-factor cannot be reduced beyond 0.7%.
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| 2. |
- Allström, Andreas, 1978-, et al.
(författare)
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A hybrid approach for short-term traffic state and travel time prediction on highways
- 2016
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Ingår i: TRB 95th annual meeting compendium of papers.
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Konferensbidrag (refereegranskat)abstract
- Traffic management and traffic information are essential in urban areas, and require a good knowledge about both the current and the future traffic state. Both parametric and non-parametric traffic state prediction techniques have previously been developed, with different advantages and shortcomings. While non-parametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during non-recurring traffic conditions such as incidents and events. Hybrid approaches, combining the two prediction paradigms have previously been proposed by using non-parametric methods for predicting boundary conditions used in a parametric method. In this paper we instead combine parametric and non-parametric traffic state prediction techniques through assimilation in an Ensemble Kalman filter. As non-parametric prediction method a neural network method is adopted, and the parametric prediction is carried out using a cell transmission model with velocity as state. The results show that our hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 minutes into the future, using a prediction horizon of up to 50 minutes ahead in time to allow the journey to be completed.
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| 3. |
- Allström, Andreas, 1978-, et al.
(författare)
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Hybrid Approach for Short-Term Traffic State and Travel Time Prediction on Highways
- 2016
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Ingår i: Transportation Research Record. - Washington, DC, USA : The National Academies of Sciences, Engineering, and Medicine. - 0361-1981 .- 2169-4052. ; 2554, s. 60-68
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Tidskriftsartikel (refereegranskat)abstract
- Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches combine the two prediction paradigms by using nonparametric methods for predicting boundary conditions used in a parametric method. In this paper, parametric and nonparametric traffic state prediction techniques are instead combined through assimilation in an ensemble Kalman filter. For nonparametric prediction, a neural network method is adopted; the parametric prediction is carried out with a cell transmission model with velocity as state. The results show that the hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 min into the future, with a prediction horizon of up to 50 min ahead in time to allow the journey to be completed
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| 4. |
- Allström, Andreas, et al.
(författare)
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Traffic management for smart cities
- 2016
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Ingår i: Designing, developing, and facilitating smart cities. - Switzerland : Springer. - 9783319449227 - 9783319449241 ; , s. 211-240
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Smart cities, participatory sensing as well as location data available in communication systems and social networks generates a vast amount of heterogeneous mobility data that can be used for traffic management. This chapter gives an overview of the different data sources and their characteristics and describes a framework for utilizing the various sources efficiently in the context of traffic management. Furthermore, different types of traffic models and algorithms are related to both the different data sources as well as some key functionalities of active traffic management, for example short-term prediction and control.
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