Traffic forecasts under uncertainty and capacity constraints

Traffic forecasts provide essential input for the appraisal of transport investment projects. However, according to recent empirical evidence, long-term predictions are subject to high levels of uncertainty. This paper quantifies uncertainty in traffic forecasts for the tolled motorway network in Spain. Uncertainty is quantified in the form of a confidence interval for the traffic forecast that includes both model uncertainty and input uncertainty. We apply a stochastic simulation process based on bootstrapping techniques. Furthermore, the paper proposes a new methodology to account for capacity constraints in long-term traffic forecasts. Specifically, we suggest a dynamic model in which the speed of adjustment is related to the ratio between the actual traffic flow and the maximum capacity of the motorway. This methodology is applied to a specific public policy that consists of suppressing the toll on a certain motorway section before the concession expires.

Cell-based computational modeling of vascular morphogenesis using Tissue Simulation Toolkit.

Computational modeling has become a widely used tool for unraveling the mechanisms of higher level cooperative cell behavior during vascular morphogenesis. However, experimenting with published simulation models or adding new assumptions to those models can be daunting for novice and even for experienced computational scientists. Here, we present a step-by-step, practical tutorial for building cell-based simulations of vascular morphogenesis using the Tissue Simulation Toolkit (TST). The TST is a freely available, open-source C++ library for developing simulations with the two-dimensional cellular Potts model, a stochastic, agent-based framework to simulate collective cell behavior. We will show the basic use of the TST to simulate and experiment with published simulations of vascular network formation. Then, we will present step-by-step instructions and explanations for building a recent simulation model of tumor angiogenesis. Demonstrated mechanisms include cell-cell adhesion, chemotaxis, cell elongation, haptotaxis, and haptokinesis.

Enhancing the vertical resolution of surface georadar data

There are far-reaching conceptual similarities between bi-static surface georadar and post-stack, "zero-offset" seismic reflection data, which is expressed in largely identical processing flows. One important difference is, however, that standard deconvolution algorithms routinely used to enhance the vertical resolution of seismic data are notoriously problematic or even detrimental to the overall signal quality when applied to surface georadar data. We have explored various options for alleviating this problem and have tested them on a geologically well-constrained surface georadar dataset. Standard stochastic and direct deterministic deconvolution approaches proved to be largely unsatisfactory. While least-squares-type deterministic deconvolution showed some promise, the inherent uncertainties involved in estimating the source wavelet introduced some artificial "ringiness". In contrast, we found spectral balancing approaches to be effective, practical and robust means for enhancing the vertical resolution of surface georadar data, particularly, but not exclusively, in the uppermost part of the georadar section, which is notoriously plagued by the interference of the direct air- and groundwaves. For the data considered in this study, it can be argued that band-limited spectral blueing may provide somewhat better results than standard band-limited spectral whitening, particularly in the uppermost part of the section affected by the interference of the air- and groundwaves. Interestingly, this finding is consistent with the fact that the amplitude spectrum resulting from least-squares-type deterministic deconvolution is characterized by a systematic enhancement of higher frequencies at the expense of lower frequencies and hence is blue rather than white. It is also consistent with increasing evidence that spectral "blueness" is a seemingly universal, albeit enigmatic, property of the distribution of reflection coefficients in the Earth. Our results therefore indicate that spectral balancing techniques in general and spectral blueing in particular represent simple, yet effective means of enhancing the vertical resolution of surface georadar data and, in many cases, could turn out to be a preferable alternative to standard deconvolution approaches.

A decision-making Fokker-Planck model in computational neuroscience

Minimal models for the explanation of decision-making in computational neuroscience are based on the analysis of the evolution for the average firing rates of two interacting neuron populations. While these models typically lead to multi-stable scenario for the basic derived dynamical systems, noise is an important feature of the model taking into account finite-size effects and robustness of the decisions. These stochastic dynamical systems can be analyzed by studying carefully their associated Fokker-Planck partial differential equation. In particular, we discuss the existence, positivity and uniqueness for the solution of the stationary equation, as well as for the time evolving problem. Moreover, we prove convergence of the solution to the the stationary state representing the probability distribution of finding the neuron families in each of the decision states characterized by their average firing rates. Finally, we propose a numerical scheme allowing for simulations performed on the Fokker-Planck equation which are in agreement with those obtained recently by a moment method applied to the stochastic differential system. Our approach leads to a more detailed analytical and numerical study of this decision-making model in computational neuroscience.

Estimation of dynamic latent variable models using simulated nonparametric moments

Abstract. Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Because conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. It is shown that as the number of simulations diverges, the estimator is consistent and a higher-order expansion reveals the stochastic difference between the infeasible GMM estimator based on the same moment conditions and the simulated version. In particular, we show how to adjust standard errors to account for the simulations. Monte Carlo results show how the estimator may be applied to a range of dynamic latent variable (DLV) models, and that it performs well in comparison to several other estimators that have been proposed for DLV models.

Advanced mapping of environmental data: Geostatistics, Machine Learning and Bayesian Maximum Entropy

This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.

Evolutionary and convergence stability for continuous phenotypes in finite populations derived from two-allele models.

The evolution of a quantitative phenotype is often envisioned as a trait substitution sequence where mutant alleles repeatedly replace resident ones. In infinite populations, the invasion fitness of a mutant in this two-allele representation of the evolutionary process is used to characterize features about long-term phenotypic evolution, such as singular points, convergence stability (established from first-order effects of selection), branching points, and evolutionary stability (established from second-order effects of selection). Here, we try to characterize long-term phenotypic evolution in finite populations from this two-allele representation of the evolutionary process. We construct a stochastic model describing evolutionary dynamics at non-rare mutant allele frequency. We then derive stability conditions based on stationary average mutant frequencies in the presence of vanishing mutation rates. We find that the second-order stability condition obtained from second-order effects of selection is identical to convergence stability. Thus, in two-allele systems in finite populations, convergence stability is enough to characterize long-term evolution under the trait substitution sequence assumption. We perform individual-based simulations to confirm our analytic results.

Multinationals, R&D and productivity: Evidence for UK Manufactoring firms

In this study we analyze multinationality (domestic-based firms vs. multinationals) and foreignness (foreign vs. domestic firms) effects in the returns of R&D to productivity. We follow a two-step strategy. In the first step, we consistently ''s productivity by GMM and numerically compute the sample distribution of the R&D returns. In the second step, we use stochastic dominance techniques to make inferences on the multinationality and foreignness effects. Results for a panel of UK manufacturing firms suggest that multinationality and foreignness effects operate in an opposite way: whilst the multinationality effect enhances R&D returns, the foreignness diminishes them.

Climate-based empirical models show biased predictions of butterfly communities along environmental gradients

A better understanding of the factors that mould ecological community structure is required to accurately predict community composition and to anticipate threats to ecosystems due to global changes. We tested how well stacked climate-based species distribution models (S-SDMs) could predict butterfly communities in a mountain region. It has been suggested that climate is the main force driving butterfly distribution and community structure in mountain environments, and that, as a consequence, climate-based S-SDMs should yield unbiased predictions. In contrast to this expectation, at lower altitudes, climate-based S-SDMs overpredicted butterfly species richness at sites with low plant species richness and underpredicted species richness at sites with high plant species richness. According to two indices of composition accuracy, the Sorensen index and a matching coefficient considering both absences and presences, S-SDMs were more accurate in plant-rich grasslands. Butterflies display strong and often specialised trophic interactions with plants. At lower altitudes, where land use is more intense, considering climate alone without accounting for land use influences on grassland plant richness leads to erroneous predictions of butterfly presences and absences. In contrast, at higher altitudes, where climate is the main force filtering communities, there were fewer differences between observed and predicted butterfly richness. At high altitudes, even if stochastic processes decrease the accuracy of predictions of presence, climate-based S-SDMs are able to better filter out butterfly species that are unable to cope with severe climatic conditions, providing more accurate predictions of absences. Our results suggest that predictions should account for plants in disturbed habitats at lower altitudes but that stochastic processes and heterogeneity at high altitudes may limit prediction success of climate-based S-SDMs.

Detailed DEM analysis of a rockslide scar to characterize the basal sliding surface of active rockslides

The basal sliding surfaces in large rockslides are often composed of several surfaces and possess a complex geometry. The exact morphology and location in three dimensions of the sliding surface remains generally unknown, in spite of extensive field and subsurface investigations, such as those at the Åknes rockslide (western Norway). This knowledge is crucial for volume estimations, failure mechanisms, and numerical slope stability modeling. This paper focuses on the geomorphologic characterization of the basal sliding surface of a postglacial rockslide scar in the vicinity of Åknes. This scar displays a stepped basal sliding surface formed by dip slopes of the gneiss foliation linked together by steeply dipping fractures. A detailed characterization of the rockslide scar by means of high-resolution digital elevation models permits statistical parameters of dip angle, spacing, persistence, and roughness of foliation surfaces and step fractures to be obtained. The characteristics are used for stochastic simulations of stepped basal sliding surfaces at the Åknes rockslide. These findings are compared with previous models based on geophysical investigations. This study discusses the investigation of rockslide scars and rock outcrops for a better understanding of potential rockslides. This work identifies possible basal sliding surface locations, which is a valuable input for volume estimates, design and location of monitoring instrumentation, and numerical slope stability modeling.

Public Monitoring with Uncertainty in the Time Repetitions

This paper study repeated games where the time repetitions of the stage game are not known or controlled by the players. We call this feature random monitoring. Kawamori's (2004) shows that perfect random monitoring is always better than the canonical case. Surprisingly, when the monitoring is public, the result is less clear-cut and does not generalize in a straightforward way. Unless the public signals are sufficiently informative about player's actions and/or players are patient enough. In addition to a discount effect, that tends to consistently favor the provision of incentives, we found an information effect, associated with the time uncertainty on the distribution of public signals. Whether payoff improvements are or not possible, depends crucially on the direction and strength of these effects. JEL: C73, D82, D86. KEYWORDS: Repeated Games, Frequent Monitoring, Random Public Monitoring, Moral Hazard, Stochastic Processes.

Cellular variability of RpoS expression underlies subpopulation activation of an integrative and conjugative element.

Conjugative transfer of the integrative and conjugative element ICEclc in the bacterium Pseudomonas knackmussii is the consequence of a bistable decision taken in some 3% of cells in a population during stationary phase. Here we study the possible control exerted by the stationary phase sigma factor RpoS on the bistability decision. The gene for RpoS in P. knackmussii B13 was characterized, and a loss-of-function mutant was produced and complemented. We found that, in absence of RpoS, ICEclc transfer rates and activation of two key ICEclc promoters (P(int) and P(inR)) decrease significantly in cells during stationary phase. Microarray and gene reporter analysis indicated that the most direct effect of RpoS is on P(inR), whereas one of the gene products from the P(inR)-controlled operon (InrR) transmits activation to P(int) and other ICEclc core genes. Addition of a second rpoS copy under control of its native promoter resulted in an increase of the proportion of cells expressing the P(int) and P(inR) promoters to 18%. Strains in which rpoS was replaced by an rpoS-mcherry fusion showed high mCherry fluorescence of individual cells that had activated P(int) and P(inR), whereas a double-copy rpoS-mcherry-containing strain displayed twice as much mCherry fluorescence. This suggested that high RpoS levels are a prerequisite for an individual cell to activate P(inR) and thus ICEclc transfer. Double promoter-reporter fusions confirmed that expression of P(inR) is dominated by extrinsic noise, such as being the result of cellular variability in RpoS. In contrast, expression from P(int) is dominated by intrinsic noise, indicating it is specific to the ICEclc transmission cascade. Our results demonstrate how stochastic noise levels of global transcription factors can be transduced to a precise signaling cascade in a subpopulation of cells leading to ICE activation.

Discrete time Non-homogeneous Semi-Markov Processes applied to Models for Disability Insurance

In this paper, we present a stochastic model for disability insurance contracts. The model is based on a discrete time non-homogeneous semi-Markov process (DTNHSMP) to which the backward recurrence time process is introduced. This permits a more exhaustive study of disability evolution and a more efficient approach to the duration problem. The use of semi-Markov reward processes facilitates the possibility of deriving equations of the prospective and retrospective mathematical reserves. The model is applied to a sample of contracts drawn at random from a mutual insurance company.

Multiple-scale hydrological and geophysical data integration through non-linear Bayesian sequential simulation

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the scale of a field site represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed downscaling procedure based on a non-linear Bayesian sequential simulation approach. The main objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity logged at collocated wells and surface resistivity measurements, which are available throughout the studied site. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariatekernel density function. Then a stochastic integration of low-resolution, large-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities is applied. The overall viability of this downscaling approach is tested and validated by comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure allows obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.