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Author: Farah Al-Ogaili
Publisher:
ISBN:
Category : Big data
Languages : en
Pages : 0
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Book Description
This dissertation investigates the potential of adopting spatial-temporal data and machine learning techniques to predict traffic speed for transportation networks. Traffic data, along with historical weather information from multi regions located in the state of Ohio, were analyzed. Different spatial-temporal cases are generated based on the preprocessed traffic data along with various weather conditions. The first part of the dissertation investigates vehicles' speed variation patterns for different peak periods and different days of the week under congested and non-congested conditions in order to measure and understand the variability patterns. Different spatial-temporal cases are generated based on the preprocessed traffic data along with various weather conditions. Results showed a noticeable difference between rural and urban interstates in terms of speed patterns under normal and event conditions. "The second aim of the dissertation is to investigate the characteristics of speed distribution patterns under free-flow and recurrent congestion by fitting different distribution models. Results showed that the Normal, Burr, and t-location distributions could provide superior fitting performance compared to its alternative models under free-flow conditions" (Hussein et al., 2021). Lastly, the dissertation investigates the potential of adopting spatial-temporal data using machine learning techniques to predict traffic speed. Based on the obtained results, it was indicated that the support vector machine with radial bases kernel outperformed other models. Support vector machine model captured the drivers' speed patterns with the best prediction accuracy among all machine learning algorithms. The findings of this dissertation assist transportation planners and transportation agencies in visualizing the impacts of recurring and non-recurring congestion on arterial and freeways. Knowledge of travel speed distribution is one of the essential aspects of evaluating the performance of the transportation system, which results in improving the reliability of traffic parameters forecasting. Accurate traffic speeds prediction enables a smooth and effective daily operation for logistics and people transport on the transportation network.
Author: Farah Al-Ogaili
Publisher:
ISBN:
Category : Big data
Languages : en
Pages : 0
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Book Description
This dissertation investigates the potential of adopting spatial-temporal data and machine learning techniques to predict traffic speed for transportation networks. Traffic data, along with historical weather information from multi regions located in the state of Ohio, were analyzed. Different spatial-temporal cases are generated based on the preprocessed traffic data along with various weather conditions. The first part of the dissertation investigates vehicles' speed variation patterns for different peak periods and different days of the week under congested and non-congested conditions in order to measure and understand the variability patterns. Different spatial-temporal cases are generated based on the preprocessed traffic data along with various weather conditions. Results showed a noticeable difference between rural and urban interstates in terms of speed patterns under normal and event conditions. "The second aim of the dissertation is to investigate the characteristics of speed distribution patterns under free-flow and recurrent congestion by fitting different distribution models. Results showed that the Normal, Burr, and t-location distributions could provide superior fitting performance compared to its alternative models under free-flow conditions" (Hussein et al., 2021). Lastly, the dissertation investigates the potential of adopting spatial-temporal data using machine learning techniques to predict traffic speed. Based on the obtained results, it was indicated that the support vector machine with radial bases kernel outperformed other models. Support vector machine model captured the drivers' speed patterns with the best prediction accuracy among all machine learning algorithms. The findings of this dissertation assist transportation planners and transportation agencies in visualizing the impacts of recurring and non-recurring congestion on arterial and freeways. Knowledge of travel speed distribution is one of the essential aspects of evaluating the performance of the transportation system, which results in improving the reliability of traffic parameters forecasting. Accurate traffic speeds prediction enables a smooth and effective daily operation for logistics and people transport on the transportation network.
Author: Tania Banerjee
Publisher: CRC Press
ISBN: 1000969770
Category : Computers
Languages : en
Pages : 213
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Book Description
Video Based Machine Learning for Traffic Intersections describes the development of computer vision and machine learning-based applications for Intelligent Transportation Systems (ITS) and the challenges encountered during their deployment. This book presents several novel approaches, including a two-stream convolutional network architecture for vehicle detection, tracking, and near-miss detection; an unsupervised approach to detect near-misses in fisheye intersection videos using a deep learning model combined with a camera calibration and spline-based mapping method; and algorithms that utilize video analysis and signal timing data to accurately detect and categorize events based on the phase and type of conflict in pedestrian-vehicle and vehicle-vehicle interactions. The book makes use of a real-time trajectory prediction approach, combined with aligned Google Maps information, to estimate vehicle travel time across multiple intersections. Novel visualization software, designed by the authors to serve traffic practitioners, is used to analyze the efficiency and safety of intersections. The software offers two modes: a streaming mode and a historical mode, both of which are useful to traffic engineers who need to quickly analyze trajectories to better understand traffic behavior at an intersection. Overall, this book presents a comprehensive overview of the application of computer vision and machine learning to solve transportation-related problems. Video Based Machine Learning for Traffic Intersections demonstrates how these techniques can be used to improve safety, efficiency, and traffic flow, as well as identify potential conflicts and issues before they occur. The range of novel approaches and techniques presented offers a glimpse of the exciting possibilities that lie ahead for ITS research and development. Key Features: Describes the development and challenges associated with Intelligent Transportation Systems (ITS) Provides novel visualization software designed to serve traffic practitioners in analyzing the efficiency and safety of an intersection Has the potential to proactively identify potential conflict situations and develop an early warning system for real-time vehicle-vehicle and pedestrian-vehicle conflicts
Author: Zhiyong Cui
Publisher:
ISBN:
Category :
Languages : en
Pages : 245
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Book Description
Traffic forecasting is a critical component of modern intelligent transportation systems for urban traffic management and control. Learning and forecasting network-scale traffic states based on spatial-temporal traffic data is particularly challenging for classical statistical and machine learning models due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. The existence of missing values in traffic data makes this task even harder. With the rise of deep learning, this work attempts to answer: how to design proper deep learning models to deal with complicated network-wide traffic data and extract comprehensive features to enhance prediction performance, and how to evaluate and apply existing deep learning-based traffic prediction models to further facilitate future research? To address those key challenges in short-term road traffic forecasting problems, this work develops deep learning models and applications to: 1) extract comprehensive features from complex spatial-temporal data to enhance prediction performance, 2) address the missing value issue in traffic forecasting tasks, and 3) deal with multi-source data, evaluate existing deep learning-based traffic forecasting models, share model results as benchmarks, and apply those models into practice. This work makes both original methodological and practical contributions to short-term network-wide traffic forecasting research. The traffic feature learning can categorized as learning traffic data as spatial-temporal matrices and learning the traffic network as a graph. Stacked bidirectional recurrent neural network is proposed to capture bidirectional temporal dependencies in traffic data. To learn localized features from the topological structure of the road network, two deep learning frameworks incorporating graph convolution and graph wavelet operations, respectively, are proposed to learn the interactions between roadway segments and predict their traffic states. To deal with missing values in traffic forecasting tasks, an imputation unit is incorporated into the recurrent neural network to increase prediction performance. Further, to fill in missing values in the graph-based traffic network, a graph Markov network is proposed, which can infer missing traffic states step by step along with the prediction process. In summary, the proposed graph-based models not only achieve superior forecasting performance but also increase the interpretability of the interaction between road segments during the forecasting process. From the practical perspective, to further facilitate future research, an open-source data and model sharing platform for evaluating existing traffic forecasting models as benchmarks is established. Additionally, a traffic performance measurement platform is presented which has the capability of taking the proposed network-wide traffic prediction models into practice.
Author: Hussein Dia
Publisher: Edward Elgar Publishing
ISBN: 1803929545
Category : Computers
Languages : en
Pages : 649
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Book Description
With AI advancements eliciting imminent changes to our transport systems, this enlightening Handbook presents essential research on this evolution of the transportation sector. It focuses on not only urban planning, but relevant themes in law and ethics to form a unified resource on the practicality of AI use.
Author: M. Shafik
Publisher: IOS Press
ISBN: 1643685058
Category : Transportation
Languages : en
Pages : 342
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Book Description
With the advent and development of AI and other new technologies, traffic and transportation have changed enormously in recent years, and the need for more environmentally-friendly solutions is also driving innovation in these fields. This book presents the proceedings of ICITT 2023, the 7th International Conference on Intelligent Traffic and Transportation, held from 18-20 September 2023 in Madrid, Spain. This annual conference is becoming one of the leading international conferences for presenting novel and fundamental advances in the fields of intelligent traffic and transportation. It also serves to foster communication among researchers and practitioners working in a wide variety of scientific areas with a common interest in intelligent traffic and transportation and related techniques. ICITT welcomes scholars and researchers from all over the world to share experiences and lessons with other enthusiasts, and develop opportunities for cooperation. The 27 papers included here represent an acceptance rate of 64% of submissions received, and were selected following a rigorous review process. Topics covered include autonomous technology; industrial automation; artificial intelligence; machine, deep and cognitive learning; distributed networking; transportation in future smart cities; hybrid vehicle technology; mobility; cyber-physical systems; design and cost engineering; enterprise information management; product design; intelligent automation; ICT-enabled collaborative global manufacturing; knowledge management; product-service systems; optimization; product lifecycle management; sustainable systems; machine vision; Industry 4.0; and navigation systems. Offering an overview of recent research and current practice, the book will be of interest to all those working in the field.
Author: Hongyuan Zhan
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Transportation is arguably speaking one of the most critical functions of human society. It has been an important societal problem since the ancient age, yet the solution is still far from perfect in the twenty-first century. The needs for efficient and safe transportation are ever-growing, due to prolonging life expectancy and diminishing reserves of fossil fuels which most transportation modes rely on in the present day. At the same time, we are facing unprecedented growth of data. Can the society utilize data, a cyber-resource, to solve the physical challenges in modern transportation needs? This question motivates the research in my dissertation. Machine learning, broadly speaking, are algorithms that aim to generalize a set of rules from existent data for describing the data generating process, predicting future events, and producing informed decision making. This dissertation studies previous machine learning methods, improves upon them, and develops new algorithms to contribute in essential aspects of transportation systems. Two important topics in transportation systems are addressed in this dissertation, traffic flow prediction and traffic safety analysis. Traffic flow prediction is a fundamental component in an intelligent transportation system. Accurate traffic predictions are building blocks to achieve efficient routing, smart city planing, reduced energy consumption and among others. Traffic flows are multi-modal and possibly non-stationary due to unusual events. Hence, the learning algorithms for traffic flow prediction need to be robust and adaptive. In addition, the models must be able to learn from latest traffic flow without severely comprising the computational efficiency, in order to meet real-time computation requirements during online deployment. Therefore, learning algorithms for traffic flow prediction developed in this dissertation are designed with the goal to achieve robustness, adaptiveness, and computational efficiency.Traffic safety in transportation systems is as important as efficiency. Rather than predicting the outcome of crashes, it is more valuable to prevent future accidents by learning from past experiences. The second theme in this dissertation studies machine learning models for analyzing factors contributing to the outcome of crashes. The same accident factor may have diverse degrees of influence on different people, due to the unobserved individual heterogeneity. Capturing heterogeneous effect is difficult in general. A viable approach is to impose structure on the unobserved heterogeneity of different individuals. Under some structural assumptions, it is possible to account for the individual differences with respect to accident factors. Temporal learning addressed problems arisen from traffic flow prediction. Structural learning is an approach for modeling individual heterogeneity, aiming to quantify the influence of accident factors.
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.
Author: Danqing Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 99
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Book Description
Increasing traffic congestion, vehicle emissions and commuters delay have been major challenges for urban transportation systems for years. The economic cost of traffic congestion in the US is Increasing from 200 billion in 2013 to 293 billion in 2030. There is an increasing need for a better solution to long-term transportation demand forecasting for urban infrastructure planning, and solution to short-term traffic prediction for managing existing urban infrastructure. Accordingly, understanding how urban systems operate and evolve through modeling individuals' daily urban activities has been a major focus of transportation planners, urban planners, and geographers. Traffic data (loop sensors, surveillance cameras, and GPS in taxis, buses), survey data (ACS, CHTS), mobile phone signals (CDR and GPS) and Location Based Social Network (LBSN) data (Facebook, Twitter, Yelp, and Foursquare) have enabled data-driven research on transportation behavior research. The data-driven research, urban data analytics, is an interdisciplinary field where machine learning/ deep learning methods from computer science and optimization/ simulation methods from operation research are applied in conventional city-related fields using spatial-temporal data. In this dissertation, we aim to add the third dimension, social, to urban data analytics research using social-spatial-temporal data, whose key topic is understanding how friendship influences human behavior over time and space. In this era of transformative mobility, this can help better design policies and investment strategies for managing existing urban infrastructure and forecasting future urban infrastructure planning. In this dissertation, we explored two research directions on social-enabled urban data analytics. First, we developed new machine learning models for social discrete choice model, bridging the gap between discrete choice modeling research and computer science research. Second, we developed a methodology framework for synthetic population synthesis using both small data and big data. The first part of the dissertation focus on modeling social influence on human behavior from a graph modeling perspective, while conforming to the discrete choice modeling framework. The proposed models can be used to model how friends influence individual's travel mode choice and other transportation related choices, which is important to transportation demand forecasting. We propose two novel models with scalable training algorithms: local logistics graph regularization (LLGR) and latent class graph regularization (LCGR) models. We add social regularization to represent similarity between friends, and we introduce latent classes to account for possible preference discrepancies between different social groups. Training of the LLGR model is performed using alternating direction method of multipliers (ADMM), and training of the LCGR model is performed using a specialized Monte Carlo expectation maximization (MCEM) algorithm. Scalability to large graphs is achieved by parallelizing computation in both the expectation and the maximization steps. The LCGR model is the first latent class classification model that incorporates social relationships among individuals represented by a given graph. To evaluate our two models, we consider three classes of data: small synthetic data to illustrate the knobs of the method, small real data to illustrate one social science use case, and large real data to illustrate a typical large-scale use case in the internet and social media applications. We experiment on synthetic datasets to empirically explain when the proposed model is better than vanilla classification models that do not exploit graph structure. We illustrate how the graph structure and labels, assigned to each node of the graph, need to satisfy certain reasonable properties. We also experiment on real-world data, including both small scale and large scale real-world datasets, to demonstrate on which types of datasets our model can be expected to outperform state-of-the-art models. This dissertation also develops an algorithmic procedure to incorporate social information into population synthesizer, which is an essential step to incorporate social information into the transportation simulation framework. Agent-based modeling in transportation problems requires detailed information on each of the agents that represent the population in the region of a study. To extend the agent-based transportation modeling with social influence, a connected synthetic population with both synthetic features and its social networks need to be simulated. However, either the traditional manually-collected household survey data (ACS) or the recent large-scale passively-collected Call Detail Records (CDR) alone lacks features. This work proposes an algorithmic procedure that makes use of both traditional survey data as well as digital records of networking and human behaviors to generate connected synthetic populations. This proposed framework for connected population synthesis is applicable to cities or metropolitan regions where data availability allows for the estimation of the component models. The generated populations coupled with recent advances in graph (social networks) algorithms can be used for testing transportation simulation scenarios with different social factors.
Author: Fouzi Harrou
Publisher: Elsevier
ISBN: 0128234334
Category : Transportation
Languages : en
Pages : 270
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Book Description
Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing road traffic monitoring and management. The book examines commonly used traffic analysis methodologies as well the emerging methods that use deep learning methods. Other sections discuss how to understand statistical models and machine learning algorithms and how to apply them to traffic modeling, estimation, forecasting and traffic congestion monitoring. Providing both a theoretical framework along with practical technical solutions, this book is ideal for researchers and practitioners who want to improve the performance of intelligent transportation systems. Provides integrated, up-to-date and complete coverage of the key components for intelligent transportation systems: traffic modeling, forecasting, estimation and monitoring Uses methods based on video and time series data for traffic modeling and forecasting Includes case studies, key processes guidance and comparisons of different methodologies
Author: Gilberto Rivera
Publisher: Springer Nature
ISBN: 3031383257
Category : Computers
Languages : en
Pages : 597
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Book Description
In the age of transformative artificial intelligence (AI), which has the potential to revolutionize our lives, this book provides a comprehensive exploration of successful research and applications in AI and data analytics. Covering innovative approaches, advanced algorithms, and data analysis methodologies, this book addresses complex problems across topics such as machine learning, pattern recognition, data mining, optimization, and predictive modeling. With clear explanations, practical examples, and cutting-edge research, this book seeks to expand the understanding of a wide readership, including students, researchers, practitioners, and technology enthusiasts eager to explore these exciting fields. Featuring real-world applications in education, health care, climate modeling, cybersecurity, smart transportation, conversational systems, and material analysis, among others, this book highlights how these technologies can drive innovation and generate competitive advantages.
