Predicting Short Term Traffic Congestion On Urban Motorway Networks PDF Download
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| Author | : Taiwo Olubunmi Adetiloye |
| Publisher | : |
| Total Pages | : 147 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
Download Predicting Short-Term Traffic Congestion on Urban Motorway Networks Book in PDF, ePub and Kindle
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.
| Author | : Giovanni Huisken |
| Publisher | : |
| Total Pages | : 292 |
| Release | : 2006 |
| Genre | : Traffic congestion |
| ISBN | : |
Download Inter-urban Short-term Traffic Congestion Prediction Book in PDF, ePub and Kindle
| Author | : 葉政威 |
| Publisher | : |
| Total Pages | : |
| Release | : 2017 |
| Genre | : |
| ISBN | : |
Download Short-term Traffic Flow Prediction in Urban Areas Using Neural Networks Book in PDF, ePub and Kindle
| Author | : Daniel S. Yeung |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 89 |
| Release | : 2009-11-09 |
| Genre | : Computers |
| ISBN | : 3642025323 |
Download Sensitivity Analysis for Neural Networks Book in PDF, ePub and Kindle
Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters. This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks.
| Author | : Ziyue Wang |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2019 |
| Genre | : |
| ISBN | : |
Download Urban Area Traffic Flow Forecasting in Intelligent Transportation Systems Book in PDF, ePub and Kindle
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.
| Author | : Martin Treiber |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 505 |
| Release | : 2012-10-11 |
| Genre | : Science |
| ISBN | : 3642324592 |
Download Traffic Flow Dynamics Book in PDF, ePub and Kindle
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.
| Author | : Pijush Samui |
| Publisher | : Academic Press |
| Total Pages | : 660 |
| Release | : 2017-07-18 |
| Genre | : Technology & Engineering |
| ISBN | : 0128113197 |
Download Handbook of Neural Computation Book in PDF, ePub and Kindle
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
| Author | : Matej Cebecauer |
| Publisher | : |
| Total Pages | : |
| Release | : 2019 |
| Genre | : |
| ISBN | : 9789178732241 |
Download Short-Term Traffic Prediction in Large-Scale Urban Networks Book in PDF, ePub and Kindle
| Author | : Alberto Bull |
| Publisher | : Santiago, Chile : United Nations, Economic Commission for Latin America and the Caribbean |
| Total Pages | : 202 |
| Release | : 2003 |
| Genre | : Technology & Engineering |
| ISBN | : |
Download Traffic Congestion Book in PDF, ePub and Kindle
| Author | : Simon Washington |
| Publisher | : CRC Press |
| Total Pages | : 496 |
| Release | : 2020-01-30 |
| Genre | : Technology & Engineering |
| ISBN | : 0429520751 |
Download Statistical and Econometric Methods for Transportation Data Analysis Book in PDF, ePub and Kindle
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.
