•The spatio-temporal distribution of floating litter was predicted at the Mediterranean level with a model coupled with a literature review.•The main litter accumulation patterns are located in the ...western and central sub-basins.•Litter in the Levantine sub-basin strand locally without outflow to other sub-regions.•Macro and micro-litter spatial accumulation patterns are not correlated.
The Mediterranean Sea is now acknowledged to be a hot spot for marine litter. However, little is known about Floating Macro Litter (FML) concentration at the scale of the entire basin; predictions regarding this would greatly help guide policymaking to fight this scourge. While previous studies have shown high spatio-temporal variability in FML distribution, the aim of this study was to accurately identify seasonal debris accumulation patterns on regional and local spatial scales across the Mediterranean basin. The objective was then to quantitatively compare this distribution model to other simulations and empirical data. We first studied FML distribution with a 2-D Lagrangian model coupled to an oceanic general circulation model (OGCM) at a horizontal resolution of 1/12°. From an initial homogeneous deployment, we deployed a set of >108 virtual particles across the whole basin and tracked each particle during 3-month journeys. Then we described the FML distribution model outputs and compared them both to empirical observations at the scale of the whole basin (gathered from a review of scientific papers on surface debris distribution), and to other numerical FML simulations from previous studies. The results of our offshore modeled distribution of FML fully agreed with characteristic debris accumulation patterns analyzed in our review of other studies. This indicates that our model could allow the prediction of monthly litter accumulation patterns at the scale of the entire Mediterranean Sea.
•NGSIM has become the de facto empirical microscopic traffic data set.•This study manually re-extracts vehicle trajectories from NGSIM video.•The raw NGSIM data exhibit trends not evident in the ...re-extracted trajectories.•The magnitude of NGSIM errors depends on speed, location and vehicle length.•As of publication the manually re-extracted data will be publically distributed.
A clear understanding of car following behavior and microscopic relationships is critical for advancing traffic flow theory. Without empirical microscopic data, plausible but incorrect hypotheses perpetuate in the vacuum. The Next Generation Simulation (NGSIM) project was undertaken to collect such data and the NGSIM data set has become the de facto standard, underlying the vast majority of empirically based advances of the past decade. But there has been a growing minority of researchers who have found unrealistic relationships in the NGSIM data. To date, the critical findings have almost exclusively come from the existing NGSIM database itself. Unfortunately, as this paper shows, the NGSIM errors are beyond anything that could be corrected strictly through cleaning or interpolation of the reported NGSIM data.
This paper takes the deepest evaluation yet of the NGSIM data. This research manually re-extracts the vehicle trajectories from a portion of the original NGSIM video to explicitly quantify NGSIM errors, e.g., piecewise constant speeds punctuated by brief periods of large acceleration exhibited by the NGSIM data were not evident in the newly extracted trajectories. This point is particularly troublesome for applications that rely on acceleration, e.g., most car following models. The magnitude of errors exhibit a dependency on speed, location and vehicle length. Examples are shown where a real vehicle stopped but the NGSIM trajectory does not and then overruns the location of the real leader. Needless to say, the re-extracted trajectories showed much cleaner speed-spacing relationships than the corresponding raw NGSIM trajectories. Finally, this work tracked the original NGSIM vehicles seen in one camera and added another 236 vehicles (11%) visible before/after the period of NGSIM tracking. As of publication, the manually re-extracted data from this paper will be released to the research community.
The Matthew effect describes the phenomenon that in societies, the rich tend to get richer and the potent even more powerful. It is closely related to the concept of preferential attachment in ...network science, where the more connected nodes are destined to acquire many more links in the future than the auxiliary nodes. Cumulative advantage and success-breads-success also both describe the fact that advantage tends to beget further advantage. The concept is behind the many power laws and scaling behaviour in empirical data, and it is at the heart of self-organization across social and natural sciences. Here, we review the methodology for measuring preferential attachment in empirical data, as well as the observations of the Matthew effect in patterns of scientific collaboration, socio-technical and biological networks, the propagation of citations, the emergence of scientific progress and impact, career longevity, the evolution of common English words and phrases, as well as in education and brain development. We also discuss whether the Matthew effect is due to chance or optimization, for example related to homophily in social systems or efficacy in technological systems, and we outline possible directions for future research.
•Multimodal regressions are defined for mean speed with respect to accumulations or stop durations (two-fluid models).•A Macroscopic traffic states analysis comes from an unprecedent complete trips ...database from pNEUMA Experiment in Athens.•A two-stage trip-based macroscopic model is proposed to enhance traffic modelling at urban regional scale.
Multi-modal interactions at the network-level remain unexplored due to the lack of high-resolution data for all transportation modes involved. The current work investigates the effect of multi-modal interactions at space-mean network speed for each mode using the dataset from pNEUMA experiment that was carried out in a congested city centre network of Athens, Greece. Explanatory variables considered are the accumulation and the stopped fraction of vehicles of each mode. Firstly, a multi-modal mean speed MFD is considered by assuming that the mean speed of each mode can be expressed in terms of accumulations of all modes. The quality of multi-modal MFD fits is compared to the uni-modal ones, where the mean speed of a given mode is assumed to be a function of the accumulation of that mode only. Secondly, the classical two-fluid model is extended to multi-modal networks. An analysis on the ergodicity assumption in the context of stopped fraction is also presented. This work also introduces a network-level dynamic model that uses the stopped fraction of vehicles. This so-called extended trip-based model simulates the stop-and-go pattern of the vehicles thereby reproducing the evolution of network congestion. This work is the first to explore in this direction. The results from the classical trip-based and the extended trip-based models are compared and validated with the empirical data.
We use Deep Artificial Neural Networks (ANNs) to estimate GARCH parameters for empirical financial time series. The algorithm we develop, allows us to fit autocovariance of squared returns of ...financial data, with certain time lags, the second order statistical moment, and the fourth order standardised moment. We have compared the time taken for the ANN algorithm to predict parameters for many time windows (around 4000), to that of the time taken for the Maximum Likelihood Estimation (MLE) methods of MatLabs’s inbuilt statistical and econometric toolbox. The algorithm developed predicts all GARCH parameters in around 0.1 s, compared to the 11 seconds of the MLE method. Furthermore, we use a Model Confidence Set analysis to determine how accurate our parameter prediction algorithm is, when predicting volatility. The volatility prediction of different securities obtained employing the ANN has an error of around 25%, compared to 40% for the MLE methods.
•We present an ANN to predict GARCH parameters using statistical features.•The ANN is near one hundred times faster than MLE methods.•The algorithm forecasts volatility with a significant increase in accuracy over MLE.
We present a new way of estimation of the role of chance in achieving success, by comparing the empirical data from 100-m dash competitions (one of the sports disciplines with the most stringent ...controls of external randomness), with the results of an agent-based computer model, which assumes that success depends jointly on the intrinsic talent of the agent and on unpredictable luck. We find a small, but non-zero contribution of random luck to the performance of the best sprinters, which may serve as a lower bound for the randomness role in other, less stringently controlled competitive domains. Additionally we discuss the perception of the payoff differences among the top participants, and the role of random luck in the resulting inequality.
•Inequalities play an enormous role in societies.•Their perception depends on whether they are deserved or not.•Even in sports, random chance influences the effects of talent and hard training.•The use of an Agent Based Model allows a new way to estimate this role.•Chance is non-negligible even in the most controlled disciplines (100 m dash).
Urban road transportation performance is the result of a complex interplay between the network supply and the travel demand. Fortunately, the framework around the macroscopic fundamental diagram ...(MFD) provides an efficient description of network-wide traffic performance. In this paper, we show how temporal patterns of vehicle traffic define the performance of urban road networks. We present two high-resolution traffic datasets covering a year each. We introduce a methodology to quantify the similarity of macroscopic traffic patterns. We do so by using the concepts of the MFD and a dynamic time warping (DTW) based algorithm for time series. This allows us to derive a few representative MFD clusters that capture the essential macroscopic traffic patterns. We then provide an in-depth analysis of traffic heterogeneity in the network which is indicative of the previously found clusters. Thereupon, we define a parsimonious classification approach to predict the expected MFD clusters early in the morning with high accuracy.
•Proposing a similarity measure for MFDs based on dynamic time warping.•Application to two one-year empirical loop detector datasets from Lucerne and Zurich.•Clustering MFD patterns and link them to bottleneck activation patterns in the city.
PDF
