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Predicting Short-Term Traffic Congestion on Urban Motorway Networks

Predicting Short-Term Traffic Congestion on Urban Motorway Networks
Author: Taiwo Olubunmi Adetiloye
Publisher:
Total Pages: 147
Release: 2018
Genre:
ISBN:
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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.

Highway Traffic Forecasting By Support Vector Regression Model With Tabu Search Algorithms

Highway Traffic Forecasting By Support Vector Regression Model With Tabu Search Algorithms
Author: Wei-Chiang Hong
Publisher:
Total Pages:
Release: 2007
Genre:
ISBN:
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Accurate forecasting of inter-urban traffic flow has been one of most important issues globally in the research on road traffic congestion. The information of inter-urban traffic with a cyclic data pattern presents a more challenging situation. This investigation presents a short-term traffic forecasting model which combines the support vector regression model with Tabu search algorithms (SVRTA) to forecast inter-urban traffic flow. The Tabu search algorithms (TA) are used to determine the three parame-ters of support vector regression (SVR) models. Additionally, a numerical ex-ample of traffic flow values from northern Taiwan is used to elucidate the fore-casting performance of the proposed SVRACO model. The simulation results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA) time-series model. Therefore, the SVRTA model is able to capture the cyclic data pattern and therefore a promising alternative in forecasting urban traffic.

Continuous Ant Colony Optimization in a SVR Urban Traffic Forecasting Model

Continuous Ant Colony Optimization in a SVR Urban Traffic Forecasting Model
Author: Wei-Chiang Hong
Publisher:
Total Pages: 0
Release: 2014
Genre:
ISBN:
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Accurate forecasting of inter-urban traffic flow has been one of most important issues in the research on road traffic congestion. The traffic flow forecasting involves a rather complex nonlinear data pattern. Recently, support vector regression (SVR) model has been widely used to solve nonlinear regression and time series problems. This investigation presents a short-term traffic forecasting model which combines SVR model with continuous ant colony optimization (SVRCACO), to forecast inter-urban traffic flow. A numerical example of traffic flow values from northern Taiwan is employed to elucidate the forecasting performance of the proposed model. The simulation results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA) time-series model.

Computational and Ambient Intelligence

Computational and Ambient Intelligence
Author: Francisco Sandoval
Publisher: Springer
Total Pages: 1192
Release: 2007-09-21
Genre: Computers
ISBN: 3540730079
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This book constitutes the refereed proceedings of the 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, held in San Sebastián, Spain in June 2007. Coverage includes theoretical concepts and neurocomputational formulations, evolutionary and genetic algorithms, data analysis, signal processing, robotics and planning motor control, as well as neural networks and other machine learning methods in cancer research.

Handbook of Neural Computation

Handbook of Neural Computation
Author: Pijush Samui
Publisher: Academic Press
Total Pages: 660
Release: 2017-07-18
Genre: Technology & Engineering
ISBN: 0128113197
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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

Statistical and Econometric Methods for Transportation Data Analysis

Statistical and Econometric Methods for Transportation Data Analysis
Author: Simon Washington
Publisher: CRC Press
Total Pages: 496
Release: 2020-01-30
Genre: Technology & Engineering
ISBN: 0429520751
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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.

Urban Area Traffic Flow Forecasting in Intelligent Transportation Systems

Urban Area Traffic Flow Forecasting in Intelligent Transportation Systems
Author: Ziyue Wang
Publisher:
Total Pages: 0
Release: 2019
Genre:
ISBN:
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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.