Download or read book Predicting Short Term Traffic Congestion on Urban Motorway Networks written by Taiwo Olubunmi Adetiloye and published by . This book was released on 2018 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traffic congestion is a widely occurring phenomenon caused by increased use of vehicles on roads resulting in slower speeds, longer delays, and increased vehicular queueing in traffic. Every year, over a thousand hours are spent in traffic congestion leading to great cost and time losses. In this thesis, we propose a multimodal data fusion framework for predicting traffic congestion on urban motorway networks. It comprises of three main approaches. The first approach predicts traffic congestion on urban motorway networks using data mining techniques. Two categories of models are considered namely neural networks, and random forest classifiers. The neural network models include the back propagation neural network and deep belief network. The second approach predicts traffic congestion using social media data. Twitter traffic delay tweets are analyzed using sentiment analysis and cluster classification for traffic flow prediction. Lastly, we propose a data fusion framework as the third approach. It comprises of two main techniques. The homogeneous data fusion technique fuses data of same types (quantitative or numeric) estimated using machine learning algorithms. The heterogeneous data fusion technique fuses the quantitative data obtained from the homogeneous data fusion model and the qualitative or categorical data (i.e. traffic tweet information) from twitter data source using Mamdani fuzzy rule inferencing systems. The proposed work has strong practical applicability and can be used by traffic planners and decision makers in traffic congestion monitoring, prediction and route generation under disruption.

Download or read book Inter urban Short term Traffic Congestion Prediction written by Giovanni Huisken and published by . This book was released on 2006 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Urban Area Traffic Flow Forecasting in Intelligent Transportation Systems written by Ziyue Wang and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Currently, Intelligent Transportation Systems (ITS), is revolutionizing the transportation industry. ITS incorporates advanced Internet of Things (IoT) technologies to implement "Smart City". These technologies produce tremendous amount of real time data from diverse sources that can be used to solve transportation problems. In this thesis, I focus on one such problem, traffic congestion in urban areas. A road segment affected by traffic affects the surrounding road segments. This is obvious. However, over a period of time, other roads not necessarily close in proximity to the congested road segment may also be affected. The congestion is not stationary. It is dynamic and it spreads. I address this issue by first formulating a similarity function using ideas from network theory. Using this similarity function, I then cluster the road points affected by traffic using affinity propagation clustering, a distributed message passing algorithm. Finally, I predict the effect of traffic on this cluster using long-short term memory neural network model. I evaluate and show the feasibility of my proposed clustering and prediction algorithm during peak and non-peak hours on open source traffic data set.

Download or read book Traffic Flow Dynamics written by Martin Treiber and published by Springer Science & Business Media. This book was released on 2012-10-11 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a comprehensive and instructive coverage of vehicular traffic flow dynamics and modeling. It makes this fascinating interdisciplinary topic, which to date was only documented in parts by specialized monographs, accessible to a broad readership. Numerous figures and problems with solutions help the reader to quickly understand and practice the presented concepts. This book is targeted at students of physics and traffic engineering and, more generally, also at students and professionals in computer science, mathematics, and interdisciplinary topics. It also offers material for project work in programming and simulation at college and university level. The main part, after presenting different categories of traffic data, is devoted to a mathematical description of the dynamics of traffic flow, covering macroscopic models which describe traffic in terms of density, as well as microscopic many-particle models in which each particle corresponds to a vehicle and its driver. Focus chapters on traffic instabilities and model calibration/validation present these topics in a novel and systematic way. Finally, the theoretical framework is shown at work in selected applications such as traffic-state and travel-time estimation, intelligent transportation systems, traffic operations management, and a detailed physics-based model for fuel consumption and emissions.

Download or read book Handbook of Neural Computation written by Pijush Samui and published by Academic Press. This book was released on 2017-07-18 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text. Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods

Download or read book Short Term Traffic Prediction in Large Scale Urban Networks written by Matej Cebecauer and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Freeway Short term Traffic Flow Forecasting by Considering Traffic Volatility Dynamics and Missing Data Situations written by Yanru Zhang and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Short-term traffic flow forecasting is a critical function in advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS). Accurate forecasting results are useful to indicate future traffic conditions and assist traffic managers in seeking solutions to congestion problems on urban freeways and surface streets. There is new research interest in short-term traffic flow forecasting due to recent developments in ITS technologies. Previous research involves technologies in multiple areas, and a significant number of forecasting methods exist in literature. However, forecasting reliability is not properly addressed in existing studies. Most forecasting methods only focus on the expected value of traffic flow, assuming constant variance when perform forecasting. This method does not consider the volatility nature of traffic flow data. This paper demonstrated that the variance part of traffic flow data is not constant, and dependency exists. A volatility model studies the dependency among the variance part of traffic flow data and provides a prediction range to indicate the reliability of traffic flow forecasting. We proposed an ARIMA-GARCH (Autoregressive Integrated Moving Average- AutoRegressive Conditional Heteroskedasticity) model to study the volatile nature of traffic flow data. Another problem of existing studies is that most methods have limited forecasting abilities when there is missing data in historical or current traffic flow data. We developed a General Regression Neural Network(GRNN) based multivariate forecasting method to deal with this issue. This method uses upstream information to predict traffic flow at the studied site. The study results indicate that the ARIMA-GARCH model outperforms other methods in non-missing data situations, while the GRNN model performs better in missing data situations.

Download or read book Statistical and Econometric Methods for Transportation Data Analysis written by Simon Washington and published by CRC Press. This book was released on 2020-01-30 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book's website (with databases and other support materials) can be accessed here. Praise for the Second Edition: The second edition introduces an especially broad set of statistical methods ... As a lecturer in both transportation and marketing research, I find this book an excellent textbook for advanced undergraduate, Master’s and Ph.D. students, covering topics from simple descriptive statistics to complex Bayesian models. ... It is one of the few books that cover an extensive set of statistical methods needed for data analysis in transportation. The book offers a wealth of examples from the transportation field. —The American Statistician Statistical and Econometric Methods for Transportation Data Analysis, Third Edition offers an expansion over the first and second editions in response to the recent methodological advancements in the fields of econometrics and statistics and to provide an increasing range of examples and corresponding data sets. It describes and illustrates some of the statistical and econometric tools commonly used in transportation data analysis. It provides a wide breadth of examples and case studies, covering applications in various aspects of transportation planning, engineering, safety, and economics. Ample analytical rigor is provided in each chapter so that fundamental concepts and principles are clear and numerous references are provided for those seeking additional technical details and applications. New to the Third Edition Updated references and improved examples throughout. New sections on random parameters linear regression and ordered probability models including the hierarchical ordered probit model. A new section on random parameters models with heterogeneity in the means and variances of parameter estimates. Multiple new sections on correlated random parameters and correlated grouped random parameters in probit, logit and hazard-based models. A new section discussing the practical aspects of random parameters model estimation. A new chapter on Latent Class Models. A new chapter on Bivariate and Multivariate Dependent Variable Models. Statistical and Econometric Methods for Transportation Data Analysis, Third Edition can serve as a textbook for advanced undergraduate, Masters, and Ph.D. students in transportation-related disciplines including engineering, economics, urban and regional planning, and sociology. The book also serves as a technical reference for researchers and practitioners wishing to examine and understand a broad range of statistical and econometric tools required to study transportation problems.

Download or read book Urban Traffic Networks written by Nathan H. Gartner and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problems of urban traffic in the industrially developed countries have been at the top of the priority list for a long time. While making a critical contribution to the economic well being of those countries, transportation systems in general and highway traffic in particular, also have detrimental effects which are evident in excessive congestion, high rates of accidents and severe pollution problems. Scientists from different disciplines have played an important role in the development and refinement of the tools needed for the planning, analysis, and control of urban traffic networks. In the past several years, there were particularly rapid advances in two areas that affect urban traffic: 1. Modeling of traffic flows in urban networks and the prediction of the resulting equilibrium conditions; 2. Technology for communication with the driver and the ability to guide him, by providing him with useful, relevant and updated information, to his desired destination.

Download or read book Traffic Congestion written by Alberto Bull and published by Santiago, Chile : United Nations, Economic Commission for Latin America and the Caribbean. This book was released on 2003 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Traffic State Estimation and Prediction in Freeways and Urban Networks written by Andrés Ladino lopez and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Centralization of work, population and economic growth alongside continued urbanization are the main causes of congestion. As cities strive to update or expand aging infrastructure, the application of big data, new models and analytics to better understand and help to combat traffic congestion is crucial to the health and development of our smart cities of XXI century. Traffic support tools specifically designed to detect, forecast and alert these conditions are highly requested nowadays.This dissertation is dedicated to study techniques that may help to estimate and forecast conditions about a traffic network. First, we consider the problem Dynamic Travel Time (DTT) short-term forecast based on data driven methods. We propose two fusion techniques to compute short-term forecasts from clustered time series. The first technique considers the error covariance matrix and uses its information to fuse individual forecasts based on best linear unbiased estimation principles. The second technique exploits similarity measurements between the signal to be predicted and clusters detected in historical data and it performs afusion as a weighted average of individual forecasts. Tests over real data were implemented in the study case of the Grenoble South Ring, it comprises a highway of 10.5Km monitored through the Grenoble Traffic Lab (GTL) a real time application was implemented and open to the public.Based on the previous study we consider then the problem of simultaneous density/flow reconstruction in urban networks based on heterogeneous sources of information. The traffic network is modeled within the framework of macroscopic traffic models, where we adopt Lighthill-Whitham-Richards (LWR) conservation equation and a piecewise linear fundamental diagram. The estimation problem considers two key principles. First, the error minimization between the measured and reconstructed flows and densities, and second the equilibrium state of the network which establishes flow propagation within the network. Both principles are integrated together with the traffic model constraints established by the supply/demand paradigm. Finally the problem is casted as a constrained quadratic optimization with equality constraints in order to shrink the feasible region of estimated variables. Some simulation scenarios based on synthetic data for a manhattan grid network are provided in order to validate the performance of the proposed algorithm.

Download or read book Innovative Approaches for Short Term Vehicular Volume Prediction In Intelligent Transportation System written by Yanjie Tao and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate and timely short-term traffic flow predictions can provide useful traffic volume information beforehand and help people make better route decisions, which plays a vital role in the Intelligent Transport System (ITS). Currently, mainly two problems are focused in this field. The first one is the spatiotemporal relations mining problem. With the road networking in ITS, the capture of spatiotemporal correlations is significant for conducting an accurate traffic flow prediction. However, most of the previous studies rely on the information collected from the single road point, which lost many useful road information. The second one is the model adaptability problem. In fact, simple road contexts such as suburban highways are preferred by previous researches due to simplex and easily captured features. However, with the progress of ITS, a great prediction model is supposed to fit into more complex road conditions. Therefore, how to make the designed models fit into more complicated prediction environments is necessary and critical. Currently, mainly two sorts of approaches, statistic-based and machine learning (ML)- based are used for short-term traffic flow predictions, but both of them face challenges mentioned above. Statistic-based models generally have better model interpretability, but delicate interpretative formulas conversely limit the model structure flexibility. As for the ML-based models, although they have a more flexible model structure and stronger non-linear pattern capture ability, the high training cost is a remarkable drawback. In this thesis, these two categories of models are both optimized to achieve a more accurate prediction. Based on the Vector Autoregressive Moving Average model (VARMA), an innovative Delay-based Spatiotemporal ARIMA (DSTARMA) is proposed to improve the spatiotemporal features mining ability of statistic-based models. This model focus on the travel delay problem, which is represented by a weighting matrix to help describe the real spatiotemporal correlations. As for the improvement of the ML-based category, an innovative Selected Stacked Gated Recurrent Units model (SSGRU) is proposed, particularly which includes a linear regression data pre-processing system to analyzes spatiotemporal relations. Further, for enhancing the model adaptability, an optimized model Multivariable Delay-based GRU (MDGRU), based on SSGRU is designed. This model extends the prediction scenario to a more complex traffic condition with a more compact model structure, and also the travel delay is considered into the prediction process. The prediction results show it outperforms many other similar models.

Download or read book Adaptive Short term Traffic Prediction in Real time Application written by Hongyu Sun and published by . This book was released on 2005 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Traffic Information and Control written by Ruimin Li and published by Institution of Engineering and Technology. This book was released on 2020-11-16 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by an international team of researchers, this book focuses on traffic information processing and signal control using emerging types of traffic data. It conveys advanced methods to estimate and predict traffic flows at different levels, including macroscopic, mesoscopic and microscopic. The aim of these predictions is to optimize traffic signal control for intersections and to mitigate ever-growing traffic congestion.

Download or read book Complex Dynamics of Traffic Management written by Boris S. Kerner and published by Springer. This book was released on 2019-04-17 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume in the Encyclopedia of Complexity and Systems Science (ECSS) covers such fascinating and practical topics as (i) Vehicular traffic flow theory, (ii) Studies of real field traffic data, (iii) Complex phenomena of self-organization in vehicular traffic, (iv) Effect of automatic driving (self-driving vehicles) on traffic flow, v) Complex dynamics of city traffic, (vi) Dynamic control and optimization of traffic and transportation networks, including dynamic traffic assignment in the network, (vii) Pedestrian traffic, (viii) Evacuation scenarios, and (ix) Network characteristics of air control. Review articles are written by international experts covering the diverse and complex dynamics of traffic management. Topics new to the Second Edition of ECSS include microscopic traffic flow models, self-driving, complex dynamics of bus, tram and elevator delays, and breakdown minimization.

Download or read book Learning Deep Architectures for AI written by Yoshua Bengio and published by Now Publishers Inc. This book was released on 2009 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

Download or read book Managing Urban Traffic Congestion written by European Conference of Ministers of Transport and published by OECD Publishing. This book was released on 2007-05-31 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offers policy-oriented, research-based recommendations for effectively managing traffic and cutting excess congestion in large urban areas.