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See detailDynamic modeling of VISSIM critical gap parameter at unsignalized intersections
Viti, Francesco UL; Wolput, Bart; Tampere, Chris M.J. et al

Poster (2013)

Detailed reference viewed: 71 (0 UL)
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See detailDynamic modeling of VISSIM's critical gap parameter at unsignalized intersections
Viti, Francesco UL; Wolput, Bart; Tampere, Chris M.J. et al

in Transportation Research Record: Journal of the Transportation Research Board (2014), 2395

Detailed reference viewed: 136 (7 UL)
See detailDynamic modelling of ground antennas
Breyer, Laurent UL

Doctoral thesis (2011)

Detailed reference viewed: 99 (6 UL)
See detailDynamic modelling of ROS management and ROS-induced mitophagy
Kolodkin, Alexey UL; Ignatenko, Andrew UL; Sangar, Vineet et al

Poster (2014, June)

Detailed reference viewed: 141 (16 UL)
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See detailDynamic Network Reconstruction in Systems Biology: Methods and Algorithms
Yue, Zuogong UL

Doctoral thesis (2018)

Dynamic network reconstruction refers to a class of problems that explore causal interactions between variables operating in dynamical systems. This dissertation focuses on methods and algorithms that ... [more ▼]

Dynamic network reconstruction refers to a class of problems that explore causal interactions between variables operating in dynamical systems. This dissertation focuses on methods and algorithms that reconstruct/infer network topology or dynamics from observations of an unknown system. The essential challenges, compared to system identification, are imposing sparsity on network topology and ensuring network identifiability. This work studies the following cases: multiple experiments with heterogeneity, low sampling frequency and nonlinearity, which are generic in biology that make reconstruction problems particularly challenging. The heterogeneous data sets are measurements in multiple experiments from the underlying dynamical systems that are different in parameters, whereas the network topology is assumed to be consistent. It is particularly common in biological applications. This dissertation proposes a way to deal with multiple data sets together to increase computational robustness. Furthermore, it can also be used to enforce network identifiability by multiple experiments with input perturbations. The necessity to study low-sampling-frequency data is due to the mismatch of network topology of discrete-time and continuous-time models. It is generally assumed that the underlying physical systems are evolving over time continuously. An important concept system aliasing is introduced to manifest whether the continuous system can be uniquely determined from its associated discrete-time model with the specified sampling frequency. A Nyquist-Shannon-like sampling theorem is provided to determine the critical sampling frequency for system aliasing. The reconstruction method integrates the Expectation Maximization (EM) method with a modified Sparse Bayesian Learning (SBL) to deal with reconstruction from output measurements. A tentative study on nonlinear Boolean network reconstruction is provided. The nonlinear Boolean network is considered as a union of local networks of linearized dynamical systems. The reconstruction method extends the algorithm used for heterogeneous data sets to provide an approximated inference but improve computational robustness significantly. The reconstruction algorithms are implemented in MATLAB and wrapped as a package. With considerations on generic signal features in practice, this work contributes to practically useful network reconstruction methods in biological applications. [less ▲]

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See detailDynamic neural network approach for atmospheric pollutant prediction: A pulp mill case study
Sainlez, Matthieu UL

Scientific Conference (2011, May 27)

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See detailDynamic OD estimation in congested networks: theoretical findings and implications in practice
Frederix, Rodric; Viti, Francesco UL; Tampere, Chris M.J.

in Transportmetrica (2013), 9(6), 494-513

In this study we analyse the impact of congestion in dynamic origin–destination (OD) estimation. This problem is typically expressed using a bi-level formulation. When solving this problem the ... [more ▼]

In this study we analyse the impact of congestion in dynamic origin–destination (OD) estimation. This problem is typically expressed using a bi-level formulation. When solving this problem the relationship between OD flows and link flows is linearised. In this article the effect of using two types of linear relationship on the estimation process is analysed. It is shown that one type of linearisation implicitly assumes separability of the link flows, which can lead to biased results when dealing with congested networks. Advantages and disadvantages of adopting non-separable relationships are discussed. Another important source of error attributable to congestion dynamics is the presence of local minima in the objective function. It is illustrated that these local minima are the result of an incorrect interpretation of the information from the detectors. The theoretical findings are cast into a new methodology, which is successfully tested in a proof of concept. [less ▲]

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See detailThe Dynamic of the EU Objectives in the Analysis of the External Competence
Neframi, Eleftheria UL

in Neframi, Eleftheria; Gatti, Mauro (Eds.) Constitutional Issues of EU External Relations Law (2018)

Detailed reference viewed: 163 (13 UL)
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See detailDynamic Origin-Destination Matrix Estimation on Large-Scale Congested Networks Using A Hierarchical Decomposition Scheme
Frederix, Rodric; Viti, Francesco UL; Tampere, Chris M.J.

in Journal of Intelligent Transportation Systems (2014), 18(1), 51-66

Despite the ever increasing computing power, dynamic Origin-Destination (OD) estimation in congested networks remains troublesome. In previous research, we have shown that an unbiased estimation requires ... [more ▼]

Despite the ever increasing computing power, dynamic Origin-Destination (OD) estimation in congested networks remains troublesome. In previous research, we have shown that an unbiased estimation requires the calculation of the sensitivity of the link flows to all Origin Destination flows, in order to incorporate the effects of congestion spillback. This is however computationally infeasible for large-scale networks. To overcome this issue, we propose a hierarchical approach for off-line application that decomposes the dynamic OD estimation procedure in space. The main idea is to perform a more accurate dynamic OD estimation only on subareas where there is congestion spillback. The output of this estimation is then used as input for the OD estimation on the whole network. This hierarchical approach solves many practical and theoretical limitations of traditional OD estimation methods. The main advantage is that different OD estimation method can be used for different parts of the network as necessary. This allows applying more advanced and accurate, but more time consuming methods only where necessary. The hierarchical approach is tested on a study network and on a real network. In both cases the proposed methodology performs better than traditional OD estimation approaches, indicating its merit. [less ▲]

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See detailDynamic Origin-Destination Matrix Estimation with Interacting Demand Patterns
Cantelmo, Guido UL

Doctoral thesis (2018)

It has become very fashionable to talk about Mobility as a Service, multimodal transport networks, electrified and green vehicles, and sustainable transportation in general. Nowadays, the transportation ... [more ▼]

It has become very fashionable to talk about Mobility as a Service, multimodal transport networks, electrified and green vehicles, and sustainable transportation in general. Nowadays, the transportation field is exploring new angles to solve mobility issues, applying concepts such as using machine learning techniques to profile user behaviour. While for many years “traffic pressure” and “congestion phenomena” were the most established keywords, there is now a widespread body of research pointing out how new technologies alone will solve most of these issues. One of the main reasons for this change of direction is that earlier approaches have been proven to be more “fair” than “effective” in tackling mobility issues. The main limitation was probably to rely on simple assumptions, such as in-elastic mobility travel demand (car users will stick to their choice), when modelling travel behaviour. However, while these assumptions were questionable twenty years ago, they simply do not hold in today's society. While it is still true that high-income people usually own a car, the concept of urban mobility evolved. First, new generations are likely to buy a car ten-twenty years later than their parents. Second, in many cases, users can choose options that are more effective by combining different transport modes. Wealthy people might decide to live next to their working place or to the city centre, rather than to buy a car. Thus, it becomes clear that to understand the evolution of the mobility demand we need to question some of these assumptions. While data can help in understanding this societal transformation, we argue in this dissertation that they cannot be considered as the sole source of information for the decision maker. Although data have been there for many years, congestion levels are increasing, meaning that data alone cannot solve the problem. Although successful in many case studies, data-driven approaches have the limitation of being capable of modelling only what they observed in the past. If there is no record of a specific event, then the model will simply provide a biased information. In this manuscript we point out that both elements – data and model – are equally relevant to represent the evolution of a transport system, and specifically how important is to consider the heterogeneity of the mobility demand within the modelling framework in order to fully exploit the available data. In this manuscript, we focus on the so-called Dynamic Demand Estimation Problem (DODE), which is the problem of estimating the mobility demand patterns that are more likely to best fit all the available traffic data. While this dissertation still focuses on car-users, we stress that the activity based structure of the demand needs to be explicitly represented in order to capture the evolution of a transport system. While data show a picture of the reality, such as how many people are travelling on a certain road segment or even along a certain path, this information represents a coarse aggregation of different individuals sharing a common resource (i.e. the infrastructure). However, the traffic flow is composed of different users with different trip purposes, meaning they react differently to a certain event. If we shut down a road from one day to another, commuting and not commuting demand will react in a different way. The same concept holds when dealing with different weather conditions, which also lead to a different demand pattern with respect to the typical one. This dissertation presents different frameworks to solve the DODE, which explicitly focus on the estimation of the mobility demand when dealing with typical and atypical user behaviour. Although the approach still focuses on a single mode of transport (car-users), the proposed formulation includes the generalized travel cost within the optimization framework. This key element allows accounting for the departure time choice and, in principle, it can be extended to the mode choice in future work. The methodologies presented in this thesis have been tested with a “state of the practice” dynamic traffic assignment model. Results suggest that the models can be used for real-life networks, but also that more efficient algorithm should be considered for practical implementations in order to unleash the full potential of this new approach. [less ▲]

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See detailDynamic Pricing in the Presence of Myopic and Strategic Consumers: Theory and Experiment
Kremer, Mirko; Mantin, Benny UL; Ovchinnikov, Anton

in Production and Operations Management (2017), 26(1), 116--133

Detailed reference viewed: 126 (9 UL)
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See detailDynamic pricing using wavelet neural network under uncertain demands
Sadegh AmalNick, Mohsen; Qorbanian, Roozbeh UL

in Decision Science Letters (2017)

Detailed reference viewed: 169 (2 UL)
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See detailDynamic problem solving competency: More than intelligence?
Wüstenberg, Sascha UL; Greiff, Samuel UL; Funke, Joachim

Scientific Conference (2011, July 05)

Detailed reference viewed: 41 (1 UL)
See detailDynamic Problem Solving in large-scale assessments: Perspectives
Greiff, Samuel UL

Presentation (2010, February 09)

Detailed reference viewed: 24 (1 UL)
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See detailDynamic problem solving. Multiple-item testing based on minimal complex systems.
Funke, Joachim; Greiff, Samuel UL

in Leutner, Detlev; Fleischer, Jens; Grünkorn, Jiliane (Eds.) et al Competence assessment in education : Research, models, and instruments. (2017)

Detailed reference viewed: 40 (2 UL)
See detailDynamic Problem Solving: A new computer-based perspective for large-scale assessments
Greiff, Samuel UL; Funke, Joachim

Scientific Conference (2011, March 30)

Detailed reference viewed: 21 (1 UL)
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See detailDynamic Problem Solving: A new measurement perspective
Greiff, Samuel UL; Wüstenberg, Sascha UL; Funke, Joachim

in Applied Psychological Measurement (2012), 36

This article addresses two unsolved measurement issues in dynamic problem solving (DPS) research: (a) unsystematic construction of DPS tests making a comparison of results obtained in different studies ... [more ▼]

This article addresses two unsolved measurement issues in dynamic problem solving (DPS) research: (a) unsystematic construction of DPS tests making a comparison of results obtained in different studies difficult and (b) use of time-intensive single tasks leading to severe reliability problems. To solve these issues, the MicroDYN approach is presented, which combines (a) the formal framework of linear structural equation models as a systematic way to construct tasks with (b) multiple and independent tasks to increase reliability. Results indicated that the assumed measurement model that comprised three dimensions, information retrieval, model building, and forecasting, fitted the data well (n = 114 students) and could be replicated in another sample (n = 140), showing excellent reliability estimates for all dimensions. Predictive validity of school grades was excellent for model building but nonexistent for the other two MicroDYN dimensions and for an additional measure of DPS. Implications are discussed. [less ▲]

Detailed reference viewed: 335 (148 UL)
See detailDynamic Problem Solving: A New Perspective for Large-scale Assessments
Greiff, Samuel UL; Wüstenberg, Sascha UL; Funke, Joachim

Scientific Conference (2010, April 14)

Detailed reference viewed: 42 (0 UL)
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See detailDynamic problem solving: Just intelligence or something different?
Wüstenberg, Sascha UL; Greiff, Samuel UL; Funke, Joachim

Scientific Conference (2011, September)

Detailed reference viewed: 29 (1 UL)
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See detailDynamic protonation equilibrium of solvated acetic acid
Gu, Wei UL; Frigato, Tomaso; Straatsma, Tjerk P. et al

in Angewandte Chemie International Edition (2007), 46(16), 2939-2943

Detailed reference viewed: 76 (0 UL)