3,250 research outputs found

    Application of Chaos Theory in the Prediction of Motorised Traffic Flows on Urban Networks

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    In recent times, urban road networks are faced with severe congestion problems as a result of the accelerating demand for mobility. One of the ways to mitigate the congestion problems on urban traffic road network is by predicting the traffic flow pattern. Accurate prediction of the dynamics of a highly complex system such as traffic flow requires a robust methodology. An approach for predicting Motorised Traffic Flow on Urban Road Networks based on Chaos Theory is presented in this paper. Nonlinear time series modeling techniques were used for the analysis of the traffic flow prediction with emphasis on the technique of computation of the Largest Lyapunov Exponent to aid in the prediction of traffic flow. The study concludes that algorithms based on the computation of the Lyapunov time seem promising as regards facilitating the control of congestion because of the technique’s effectiveness in predicting the dynamics of complex systems especially traffic flow

    Forecasting wind speeds at tall tower heights within Missouri

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    Forecasting of wind speeds is necessary for the planning and operations of the wind power generating plants. This research investigates the short term forecasting of wind speeds at tall tower heights for stations within Missouri: Columbia, Neosho and Blanchard. The first objective was to characterize the chaotic nature of this parameter using mono and multi fractal analysis using the Rescale Range Analysis (R/S Analysis) and the Multifractal Detrended Fluctuation Analysis respectively (MF-DFA). It was determined that the system was fractal and there were no trends indicative of increasing fractality and complexity with increasing height. The second objective was the qualitative and quantitative chaotic characterization of the wind speeds using phase-space portraits and the Largest Lyapunov Exponent (LLE) respectively. The methods confirm the results of the fractal analyses. A simple non-linear prediction algorithm, Empirical Dynamical Modeling (EDM) was then used to forecast the wind speeds using a moving window. It was determined that the EDM was comparable to persistence. It beats this benchmark model in the very short term range of one time step or 10 minutes. The third objective was to cluster the data using Self-Organizing Maps (SOMs), having identified the optimum number of clusters as 4 using the Elbow and Silhouette Methods, among others. Three continuous intervals belonging to a particular cluster, which represented approximately 50 percent and over of the input vectors or rows from the data frame were identified. These intervals were then used as inputs into a Long Short-Term Memory Network (LSTM) with variables, pressure and wind speeds, as well as a lagged series LSTM with embedding dimension, d, and time delay (tau). These were compared to the Moving window Auto Regressive Integrated Moving Average (ARIMA) and to persistence. It was determined that the lagged series LSTM improved on the LSTM with wind speed and pressure series inputs, and all models beat persistence. The lagged LSTM beats the Moving ARIMA for at least 2 of the forecasting times of 60 and 120 minutes for all intervals.Includes bibliographical references

    Brownian motion: a paradigm of soft matter and biological physics

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    This is a pedagogical introduction to Brownian motion on the occasion of the 100th anniversary of Einstein's 1905 paper on the subject. After briefly reviewing Einstein's work in its contemporary context, we pursue some lines of further developments and applications in soft condensed matter and biology. Over the last century Brownian motion became promoted from an odd curiosity of marginal scientific interest to a guiding theme pervading all of the modern (live) sciences.Comment: 30 pages, revie

    The application of chaos theory to forecast urban traffic conditions

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    PhD ThesisThis thesis explores the application of Chaos Theory to forecast urban traffic conditions. The research takes advantage of a highly resolved temporal and spatial data available from the Split Cycle Optimisation Technique (SCOOT) system, in order to overcome the limitations of previous studies to investigate applying Chaos Theory in traffic management. This thesis reports on the development of a chaos-based algorithm and presents results from its application to a SCOOT controlled region in the city of Leicester, UK. A Phase Space Reconstruction method is used to analyse non-linear data from the SCOOT system, and establishes that a 20 second resolved data is suitable for understanding the dynamics of the traffic system. The research develops the Lyapunov exponent as a chaos-based parameter to forecast link occupancy using a multiple regression model based on the temporal and spatial relationships across the links in the network. The model generates a unique forecast function for each link for every hour of the day. The study demonstrates that Lyapunov exponents can be used to predict the occupancy profile of links in the network to a reasonably high level of accuracy (R-values generally greater than 0.6). Evidence also suggests that the predictions from the Lyapunov exponents (rather than occupancy) make it possible to report on the impending conditions over a wider part of the network so that imminent congested conditions can be foreseen in advance and mitigation measures implemented. Thus, the thesis concludes that incorporating chaos-based algorithms in this way can enable urban traffic control systems to be one-step ahead of traffic congestion, rather than one-step behind. This would improve the management of traffic on a more strategic level rather than purely within smaller network regions thus playing an important role in improving journey times and air quality and making a vital contribution to mitigating climate change

    Proceedings of the USDA-ARS workshop "Real world" infiltration

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    Compiled and edited by L.R. Ahuja and Amy Garrison.Includes bibliographical references.Proceedings of the 1996 workshop held on July 22-25, 1996 in Pingree Park, Colorado

    The Structure of Climate Variability Across Scales

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    One of the most intriguing facets of the climate system is that it exhibits variability across all temporal and spatial scales; pronounced examples are temperature and precipitation. The structure of this variability, however, is not arbitrary. Over certain spatial and temporal ranges it can be described by scaling relationships in the form of power‐laws in probability density distributions and autocorrelation functions. These scaling relationships can be quantified by scaling exponents which measure how the variability changes across scales and how the intensity changes with frequency of occurrence. Scaling determines the relative magnitudes and persistence of natural climate fluctuations. Here, we review various scaling mechanisms and their relevance for the climate system. We show observational evidence of scaling and discuss the application of scaling properties and methods in trend detection, climate sensitivity analyses, and climate predictio
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