Download or read book Incorporating Real time Spatial temporal Traffic Data for Traffic Prediction of Transportation Networks Using Machine Learning Yechniques written by Farah Al-Ogaili and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Video Based Machine Learning for Traffic Intersections written by Tania Banerjee and published by CRC Press. This book was released on 2023-10-17 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Deep Learning for Short term Network wide Road Traffic Forecasting written by Zhiyong Cui and published by . This book was released on 2021 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Handbook on Artificial Intelligence and Transport written by Hussein Dia and published by Edward Elgar Publishing. This book was released on 2023-10-06 with total page 649 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Emerging Cutting Edge Developments in Intelligent Traffic and Transportation Systems written by M. Shafik and published by IOS Press. This book was released on 2024-03-05 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Social enabled Urban Data Analytics written by Danqing Zhang and published by . This book was released on 2018 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Temporal and Structural Machine Learning from Transportation Data written by Hongyuan Zhan and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Advanced Intelligent Predictive Models for Urban Transportation written by R. Sathiyaraj and published by CRC Press. This book was released on 2022-03-27 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Road Traffic Modeling and Management written by Fouzi Harrou and published by Elsevier. This book was released on 2021-10-05 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Data Analytics and Computational Intelligence Novel Models Algorithms and Applications written by Gilberto Rivera and published by Springer Nature. This book was released on 2023-10-20 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Real time Traffic Flow Detection and Prediction Algorithm written by Bumjoon Bae and published by . This book was released on 2017 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traffic flows over time and space. This spatio-temporal dependency of traffic flow should be considered and used to enhance the performance of real-time traffic detection and prediction capabilities. This characteristic has been widely studied and various applications have been developed and enhanced. During the last decade, great attention has been paid to the increases in the number of traffic data sources, the amount of data, and the data-driven analysis methods. There is still room to improve the traffic detection and prediction capabilities through studies on the emerging resources. To this end, this dissertation presents a series of studies on real-time traffic operation for highway facilities focusing on detection and prediction.First, a spatio-temporal traffic data imputation approach was studied to exploit multi-source data. Different types of kriging methods were evaluated to utilize the spatio-temporal characteristic of traffic data with respect to two factors, including missing patterns and use of secondary data. Second, a short-term traffic speed prediction algorithm was proposed that provides accurate prediction results and is scalable for a large road network analysis in real time. The proposed algorithm consists of a data dimension reduction module and a nonparametric multivariate time-series analysis module. Third, a real-time traffic queue detection algorithm was developed based on traffic fundamentals combined with a statistical pattern recognition procedure. This algorithm was designed to detect dynamic queueing conditions in a spatio-temporal domain rather than detect a queue and congestion directly from traffic flow variables. The algorithm was evaluated by using various real congested traffic flow data. Lastly, gray areas in a decision-making process based on quantifiable measures were addressed to cope with uncertainties in modeling outputs. For intersection control type selection, the gray areas were identified and visualized.

Download or read book Smart Transportation written by Guido Dartmann and published by CRC Press. This book was released on 2021-11-10 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides a broad overview of the challenges and recent developments in the field of smart mobility and transportation, including technical, algorithmic and social aspects of smart mobility and transportation. It reviews new ideas for services and platforms for future mobility. New concepts of artificial intelligence and the implementation in new hardware architecture are discussed. In the context of artificial intelligence, new challenges of machine learning for autonomous vehicles and fleets are investigated. The book also investigates human factors and social questions of future mobility concepts. The goal of this book is to provide a holistic approach towards smart transportation. The book reviews new technologies such as the cloud, machine learning and communication for fully atomatized transport, catering to the needs of citizens. This will lead to complete change of concepts in transportion.

Download or read book Spatial Temporal Dependency of Traffic Flow and Its Implications for Short Term Traffic Forecasting written by Yang Yue and published by Open Dissertation Press. This book was released on 2017-01-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Recurrent Neural Networks written by Larry Medsker and published by CRC Press. This book was released on 1999-12-20 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: With existent uses ranging from motion detection to music synthesis to financial forecasting, recurrent neural networks have generated widespread attention. The tremendous interest in these networks drives Recurrent Neural Networks: Design and Applications, a summary of the design, applications, current research, and challenges of this subfield of artificial neural networks. This overview incorporates every aspect of recurrent neural networks. It outlines the wide variety of complex learning techniques and associated research projects. Each chapter addresses architectures, from fully connected to partially connected, including recurrent multilayer feedforward. It presents problems involving trajectories, control systems, and robotics, as well as RNN use in chaotic systems. The authors also share their expert knowledge of ideas for alternate designs and advances in theoretical aspects. The dynamical behavior of recurrent neural networks is useful for solving problems in science, engineering, and business. This approach will yield huge advances in the coming years. Recurrent Neural Networks illuminates the opportunities and provides you with a broad view of the current events in this rich field.

Download or read book Spatial and Temporal Regularized Compressive Sensing for Urban Traffic Monitoring written by Tian Lan and published by . This book was released on 2017-01-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Spatial and Temporal Regularized Compressive Sensing for Urban Traffic Monitoring" by Tian, Lan, 蘭天, 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: Urban transport system plays an important role in the economic, social, and environmental dimensions of cities. However, transport system is still facing many challenges, such as the traffic congestion issue. With recent advancements in sensor technologies, urban traffic monitoring system is capable of collecting traffic information from new data sources to mitigate these challenges. Traffic information can be used for both real-time traffic management and long term transport planning. Nonetheless, data sparseness is a common issue among these traffic sensor data, which leads to inaccurate or even mistaken results for higher-level traffic data analysis. To solve the data sparseness issue of traffic sensors, real-world floating car data from Wuhan city is collected and examined in this research. By extracting link-based average traffic speed for road links at different time intervals, an incomplete traffic condition matrix is formulated with missing entries due to the data sparseness issue. The research question can be posed as how to interpolate the missing entries from known sample in the traffic condition matrix. The literature shows that the typical traffic interpolation models are vulnerable to high data loss. On the contrary, compressive sensing based interpolation models in the literature can still perform well under high data loss. However, current compressive sensing based traffic interpolation models are too general owing to their data-driven strategies. A spatial and temporal regularized compressive sensing model is proposed to fill in the research gap identified from the literature. The model framework is established primarily based on current compressive sensing interpolation models. Using non-negative matrix factorization, the traffic condition matrix can be decomposed into the spatial factor matrix and temporal factor matrix. The model framework further employs the spatial and temporal constraints on the two factor matrices respectively, such as the spatial correlation, network topology, and short-term stability. The proposed model is equivalent to an optimization problem that minimizes errors with the constraints from low rank and spatio-temporal properties. Stochastic gradient descent algorithm is provided to solve the minimization problem of the proposed model. The proposed model is evaluated using root mean square error with a 5-fold cross validation. The proposed model is competed with temporal KNN model, space-time KNN model, Kriging model, and baseline compressive sensing model under different data loss patterns and data loss ratios (e.g. from 50% to 90%). Results show that the proposed model performs generally better than these models under these scenarios. This research establishes a paradigm for regularized compressive sensing interpolation models. The regularization terms on the spatial factor matrix and temporal factor matrix can be substituted with alternative constraints from domain knowledge. With further extensions, the proposed model has potential to be applied in several future studies such as the traffic data compression and traffic prediction. DOI: 10.5353/th_b5689252 Subjects: Urban transportation Traffic monitoring

Download or read book Deep Learning Technologies for the Sustainable Development Goals written by Virender Kadyan and published by Springer Nature. This book was released on 2023-02-01 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides insights into deep learning techniques that impact the implementation strategies toward achieving the Sustainable Development Goals (SDGs) laid down by the United Nations for its 2030 agenda, elaborating on the promises, limits, and the new challenges. It also covers the challenges, hurdles, and opportunities in various applications of deep learning for the SDGs. A comprehensive survey on the major applications and research, based on deep learning techniques focused on SDGs through speech and image processing, IoT, security, AR-VR, formal methods, and blockchain, is a feature of this book. In particular, there is a need to extend research into deep learning and its broader application to many sectors and to assess its impact on achieving the SDGs. The chapters in this book help in finding the use of deep learning across all sections of SDGs. The rapid development of deep learning needs to be supported by the organizational insight and oversight necessary for AI-based technologies in general; hence, this book presents and discusses the implications of how deep learning enables the delivery agenda for sustainable development.

Download or read book Spatio temporal Analyses for Prediction of Traffic Flow Speed and Occupancy on I 4 written by Srinivasa Ravi Chandra Chilakamarri Venkata and published by . This book was released on 2009 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traffic data prediction is a critical aspect of Advanced Traffic Management System (ATMS). The utility of the traffic data is in providing information on the evolution of traffic process that can be passed on to the various users (commuters, Regional Traffic Management Centers (RTMCs), Department of Transportation (DoT) ... etc) for user-specific objectives. This information can be extracted from the data collected by various traffic sensors. Loop detectors collect traffic data in the form of flow, occupancy, and speed throughout the nation. Freeway traffic data from I-4 loop detectors has been collected and stored in a data warehouse called the Central Florida Data Warehouse (CFDW[trademark symbol]) by the University of Central Florida for the periods between 1993-1994 and 2000 - 2003. This data is raw, in the form of time stamped 30-second aggregated data collected from about 69 stations over a 36 mile stretch on I-4 from Lake Mary in the east to Disney-World in the west. This data has to be processed to extract information that can be disseminated to various users. Usually, most statistical procedures assume that each individual data point in the sample is independent of other data points. This is not true to traffic data as they are correlated across space and time. Therefore, the concept of time sequence and the layout of data collection devices in space, introduces autocorrelations in a single variable and cross correlations across multiple variables. Significant autocorrelations prove that past values of a variable can be used to predict future values of the same variable. Furthermore, significant cross-correlations between variables prove that past values of one variable can be used to predict future values of another variable. The traditional techniques in traffic prediction use univariate time series models that account for autocorrelations but not cross-correlations. These models have neglected the cross correlations between variables that are present in freeway traffic data, due to the way the data are collected. There is a need for statistical techniques that incorporate the effect of these multivariate cross-correlations to predict future values of traffic data. The emphasis in this dissertation is on the multivariate prediction of traffic variables. Unlike traditional statistical techniques that have relied on univariate models, this dissertation explored the cross-correlation between multivariate traffic variables and variables collected across adjoining spatial locations (such as loop detector stations). The analysis in this dissertation proved that there were significant cross correlations among different traffic variables collected across very close locations at different time scales. The nature of cross-correlations showed that there was feedback among the variables, and therefore past values can be used to predict future values. Multivariate time series analysis is appropriate for modeling the effect of different variables on each other. In the past, upstream data has been accounted for in time series analysis. However, these did not account for feedback effects. Vector Auto Regressive (VAR) models are more appropriate for such data. Although VAR models have been applied to forecast economic time series models, they have not been used to model freeway data. Vector Auto Regressive models were estimated for speeds and volumes at a sample of two locations, using 5-minute data. Different specifications were fit--estimation of speeds from surrounding speeds; estimation of volumes from surrounding volumes; estimation of speeds from volumes and occupancies from the same location; estimation of speeds from volumes from surrounding locations (and vice versa). These specifications were compared to univariate models for the respective variables at three levels of data aggregation (5-minutes, 10 minutes, and 15 minutes) in this dissertation. For data aggregation levels of [less than]15 minutes, the VAR models outperform the univariate models. At data aggregation level of 15 minutes, VAR models did not outperform univariate models. Since VAR models were used for all traffic variables reported by the loop detectors, this made the application of VAR a true multivariate procedure for dynamic prediction of the multivariate traffic variables--flow, speed and occupancy. Also, VAR models are generally deemed more complex than univariate models due to the estimation of multiple covariance matrices. However, a VAR model for k variables must be compared to k univariate models and VAR models compare well with AutoRegressive Integrated Moving Average (ARIMA) models. The added complexity helps model the effect of upstream and downstream variables on the future values of the response variable. This could be useful for ATMS situations, where the effect of traffic redistribution and redirection is not known beforehand with prediction models. The VAR models were tested against more traditional models and their performances were compared against each other under different traffic conditions. These models significantly enhance the understanding of the freeway traffic processes and phenomena as well as identifying potential knowledge relating to traffic prediction. Further refinements in the models can result in better improvements for forecasts under multiple conditions.