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Author: Hongyu Sun
Publisher:
ISBN:
Category :
Languages : en
Pages : 232
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Author: Hongyu Sun
Publisher:
ISBN:
Category :
Languages : en
Pages : 232
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Author: Rezaur Rahman
Publisher:
ISBN:
Category :
Languages : en
Pages : 58
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Keywords: Deep-learning, Long short-term memory, Data-driven, Traffic state, Real-time queue length, Adaptive Traffic Control System.
Author: Rafael Mena-Yedra
Publisher:
ISBN:
Category :
Languages : en
Pages : 304
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Book Description
This thesis has addressed the design and development of an integrated system for real-time traffic forecasting based on machine learning methods. Although traffic prediction has been the driving motivation for the thesis development, a great part of the proposed ideas and scientific contributions in this thesis are generic enough to be applied in any other problem where, ideally, their definition is that of the flow of information in a graph-like structure. Such application is of special interest in environments susceptible to changes in the underlying data generation process. Moreover, the modular architecture of the proposed solution facilitates the adoption of small changes to the components that allow it to be adapted to a broader range of problems. On the other hand, certain specific parts of this thesis are strongly tied to the traffic flow theory. The focus in this thesis is on a macroscopic perspective of the traffic flow where the individual road traffic flows are correlated to the underlying traffic demand. These short-term forecasts include the road network characterization in terms of the corresponding traffic measurements -traffic flow, density and/or speed-, the traffic state -whether a road is congested or not, and its severity-, and anomalous road conditions -incidents or other non-recurrent events-. The main traffic data used in this thesis is data coming from detectors installed along the road networks. Nevertheless, other kinds of traffic data sources could be equally suitable with the appropriate preprocessing.This thesis has been developed in the context of Aimsun Live -a simulation-based traffic solution for real-time traffic prediction developed by Aimsun-. The methods proposed here is planned to be linked to it in a mutually beneficial relationship where they cooperate and assist each other. An example is when an incident or non-recurrent event is detected with the proposed methods in this thesis, then the simulation-based forecasting module can simulate different strategies to measure their impact. Part of this thesis has been also developed in the context of the EU research project "SETA" (H2020-ICT-2015).The main motivation that has guided the development of this thesis is enhancing those weak points and limitations previously identified in Aimsun Live, and whose research found in literature has not been especially extensive. These include:•Autonomy, both in the preparation and real-time stages.•Adaptation, to gradual or abrupt changes in traffic demand or supply.•Informativeness, about anomalous road conditions. •Forecasting accuracy improved with respect to previous methodology at Aimsun and a typical forecasting baseline.•Robustness, to deal with faulty or missing data in real-time.•Interpretability, adopting modelling choices towards a more transparent reasoning and understanding of the underlying data-driven decisions.•Scalable, using a modular architecture with emphasis on a parallelizable exploitation of large amounts of data.The result of this thesis is an integrated system -Adarules- for real-time forecasting which is able to make the best of the available historical data, while at the same time it also leverages the theoretical unbounded size of data in a continuously streaming scenario. This is achieved through the online learning and change detection features along with the automatic finding and maintenance of patterns in the network graph. In addition to the Adarules system, another result is a probabilistic model that characterizes a set of interpretable latent variables related to the traffic state based on the traffic data provided by the sensors along with optional prior knowledge provided by the traffic expert following a Bayesian approach. On top of this traffic state model, it is built the probabilistic spatiotemporal model that learns the dynamics of the transition of traffic states in the network, and whose objectives include the automatic incident detection.
Author: Leszek Rutkowski
Publisher: Springer Nature
ISBN: 3030615340
Category : Computers
Languages : en
Pages : 547
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Book Description
The two-volume set LNCS 12415 and 12416 constitutes the refereed proceedings of of the 19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020, held in Zakopane, Poland*, in October 2020. The 112 revised full papers presented were carefully reviewed and selected from 265 submissions. The papers included in the first volume are organized in the following six parts: neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; pattern classification; bioinformatics, biometrics and medical applications; artificial intelligence in modeling and simulation. The papers included in the second volume are organized in the following four parts: computer vision, image and speech analysis; data mining; various problems of artificial intelligence; agent systems, robotics and control. *The conference was held virtually due to the COVID-19 pandemic.
Author: Yang Yue
Publisher: Open Dissertation Press
ISBN: 9781374674073
Category :
Languages : en
Pages :
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This dissertation, "Spatial-temporal Dependency of Traffic Flow and Its Implications for Short-term Traffic Forecasting" by Yang, Yue, 樂陽, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled Spatial‐temporal Dependency of Traffic Flow and Its Implications for Short‐term Traffic Forecasting Submitted by Yue Yang for the degree of Doctor of Philosophy at The University of Hong Kong in September 2005 Short-term traffic forecasting is of great significance to modern transport management. It can help management centres and individual travellers to make better travel decisions and offers a more rational and effective way to alleviate traffic congestion and redistribute traffic more evenly over a road network. Several forecasting models have been proposed in recent decades, but no single method has been able to consistently outperform its rivals under all conditions. Some of these models have produced even worse results than the traditional historical average method, making them practically valueless. Attempts at econometric forecasting also support the above observations. This thesis, which emphasizes the importance of a systematic approach in understanding traffic phenomena, first examines the spatial-temporal dependency of traffic flow (i.e. how traffic flows are related in both dimensions) by Cross Correlation Function (CCF) analysis which quantifies this dependency using correlation coefficient and time lag. Based on the implications of the spatial-temporal relationship of traffic flow, it then proposes a short-term traffic forecasting framework, using an adaptive forecasting model selection strategy which blends effective real-time and historical data in different proportions to suit different forecasting horizons instead of attempting to create a single model to deal with all forecasting settings. It also develops an improved Kalman filter - spatial-temporal Kalman filter (STKF), whose parameters and coefficients are determined by the effective real-time data and their weights. The forecasting strategy is examined using three typical examples, in which the forecasting horizon is respectively within, equal to, and beyond the network maximum up-trace time. The STKF is used for the first two situations, where higher forecasting accuracy is desirable. In the third situation, the historical average method is used, and the thesis demonstrates that this compares favourably with ARIMA (Autoregressive Integrated Moving Average) and NN (neural network) in terms of forecasting accuracy and overall costs. The thesis does not seek to propose an innovative forecasting model. Rather, it focuses on the traffic flow generation and evolution process among road links, and emphasizes that the propagation of traffic flow must be properly understood if traffic forecasting is to be effective. It makes the following novel contributions: (1) it explicitly quantifies the spatial-temporal relationships of traffic flows observed at different locations; (2) it suggests that the spatial-temporal dependency of traffic flows should be a major factor in traffic forecasting method; (3) it examines the effectivity of real-time data and forecastability of traffic conditions in forecasting; (4) it proposes an adaptive forecasting strategy based on these notions, in which the forecasting time horizon and the network storage for the provision of effective real- time data determine the choice of forecasting method; (5) it develops a spatial- temporal Kalman filter which outperforms ARIMA and
Author:
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Category :
Languages : en
Pages :
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Many Intelligent Transportation System (ITS) applications under the umbrella of Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Services (ATIS) call for the ability to anticipate future traffic conditions. Short-term traffic forecasting models play a central role in such applications. Previous research has shown that a three parameter SARIMA time series model is well suited for forecasting short-term freeway traffic flow. However, past application has been in a static form where the model has to be fitted separately for each location. This research implements the seasonal ARIMA model in a time-varying format imparting plug and play capability to the model. The properties of the SARIMA model for short-term traffic flow forecasting are discussed. Model sensitivity to the parameters is shown. Three different methods (Kalman filter, recursive least squares filter and least mean square filter) have been investigated for making the model adaptive. The stability and robustness of the SARIMA model has been demonstrated. Results show that all the three adaptive filters can be successfully used to make the model adaptive. The use of Kalman filter for practical implementation is recommended. Recommendations for further research in this regard are also presented.
Author: Vincenzo Piuri
Publisher: Springer Nature
ISBN: 9811674981
Category : Technology & Engineering
Languages : en
Pages : 241
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Book Description
This book provides a valuable combination of relevant research works on developing smart city ecosystem from the artificial intelligence (AI) and Internet of things (IoT) perspective. The technical research works presented here are focused on a number of aspects of smart cities: smart mobility, smart living, smart environment, smart citizens, smart government, and smart waste management systems as well as related technologies and concepts. This edited book offers critical insight to the key underlying research themes within smart cities, highlighting the limitations of current developments and potential future directions.
Author: Antonio Laganà
Publisher: Springer
ISBN: 3540247688
Category : Computers
Languages : en
Pages : 1066
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Book Description
The natural mission of Computational Science is to tackle all sorts of human problems and to work out intelligent automata aimed at alleviating the b- den of working out suitable tools for solving complex problems. For this reason ComputationalScience,thoughoriginatingfromtheneedtosolvethemostch- lenging problems in science and engineering (computational science is the key player in the ?ght to gain fundamental advances in astronomy, biology, che- stry, environmental science, physics and several other scienti?c and engineering disciplines) is increasingly turning its attention to all ?elds of human activity. In all activities, in fact, intensive computation, information handling, kn- ledge synthesis, the use of ad-hoc devices, etc. increasingly need to be exploited and coordinated regardless of the location of both the users and the (various and heterogeneous) computing platforms. As a result the key to understanding the explosive growth of this discipline lies in two adjectives that more and more appropriately refer to Computational Science and its applications: interoperable and ubiquitous. Numerous examples of ubiquitous and interoperable tools and applicationsaregiveninthepresentfourLNCSvolumescontainingthecontri- tions delivered at the 2004 International Conference on Computational Science and its Applications (ICCSA 2004) held in Assisi, Italy, May 14–17, 2004.
Author: Simon Washington
Publisher: CRC Press
ISBN: 0429520751
Category : Technology & Engineering
Languages : en
Pages : 496
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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.
Author: R. Sathiyaraj
Publisher: CRC Press
ISBN: 1000555909
Category : Computers
Languages : en
Pages : 145
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Book Description
The book emphasizes the predictive models of Big Data, Genetic Algorithm, and IoT with a case study. The book illustrates the predictive models with integrated fuel consumption models for smart and safe traveling. The text is a coordinated amalgamation of research contributions and industrial applications in the field of Intelligent Transportation Systems. The advanced predictive models and research results were achieved with the case studies, deployed in real transportation environments. Features: Provides a smart traffic congestion avoidance system with an integrated fuel consumption model. Predicts traffic in short-term and regular. This is illustrated with a case study. Efficient Traffic light controller and deviation system in accordance with the traffic scenario. IoT based Intelligent Transport Systems in a Global perspective. Intelligent Traffic Light Control System and Ambulance Control System. Provides a predictive framework that can handle the traffic on abnormal days, such as weekends, festival holidays. Bunch of solutions and ideas for smart traffic development in smart cities. This book focuses on advanced predictive models along with offering an efficient solution for smart traffic management system. This book will give a brief idea of the available algorithms/techniques of big data, IoT, and genetic algorithm and guides in developing a solution for smart city applications. This book will be a complete framework for ITS domain with the advanced concepts of Big Data Analytics, Genetic Algorithm and IoT. This book is primarily aimed at IT professionals. Undergraduates, graduates and researchers in the area of computer science and information technology will also find this book useful.
