Download or read book A Deep Learning Approach for Spatiotemporal data driven Traffic State Estimation written by Amr Abdelraouf and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade witnessed rapid developments in traffic data sensing technologies in the form of roadside detector hardware, vehicle on-board units, and pedestrian wearable devices. The growing magnitude and complexity of the available traffic data has fueled the demand for data-driven models that can handle large scale inputs. In the recent past, deep-learning-powered algorithms have become the state-of-the-art for various data-driven applications. In this research, three applications of deep learning algorithms for traffic state estimation were investigated. Firstly, network-wide traffic parameters estimation was explored. An attention-based multi-encoder-decoder (Att-MED) neural network architecture was proposed and trained to predict freeway traffic speed up to 60 minutes ahead. Att-MED was designed to encode multiple traffic input sequences: short-term, daily, and weekly cyclic behavior. The proposed network produced an average prediction accuracy of 97.5%, which was superior to the compared baseline models. In addition to improving the output performance, the model’s attention weights enhanced the model interpretability. This research additionally explored the utility of low-penetration connected probe-vehicle data for network-wide traffic parameters estimation and prediction on freeways. A novel sequence-to-sequence recurrent graph networks (Seq2Se2 GCN-LSTM) was designed. It was then trained to estimate and predict traffic volume and speed for a 60-minute future time horizon. The proposed methodology generated volume and speed predictions with an average accuracy of 90.5% and 96.6%, respectively, outperforming the investigated baseline models. The proposed method demonstrated robustness against perturbations caused by the probe vehicle fleet’s low penetration rate. Secondly, the application of deep learning for road weather detection using roadside CCTVs were investigated. A Vision Transformer (ViT) was trained for simultaneous rain and road surface condition classification. Next, a Spatial Self-Attention (SSA) network was designed to consume the individual detection results, interpret the spatial context, and modify the collective detection output accordingly. The sequential module improved the accuracy of the stand-alone Vision Transformer as measured by the F1-score, raising the total accuracy for both tasks to 96.71% and 98.07%, respectively. Thirdly, a real-time video-based traffic incident detection algorithm was developed to enhance the utilization of the existing roadside CCTV network. The methodology automatically identified the main road regions in video scenes and investigated static vehicles around those areas. The developed algorithm was evaluated using a dataset of roadside videos. The incidents were detected with 85.71% sensitivity and 11.10% false alarm rate with an average delay of 27.53 seconds. In general, the research proposed in this dissertation maximizes the utility of pre-existing traffic infrastructure and emerging probe traffic data. It additionally demonstrated deep learning algorithms’ capability of modeling complex spatiotemporal traffic data. This research illustrates that advances in the deep learning field continue to have a high applicability potential in the traffic state estimation domain.

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 Developing a Physics informed Deep Learning Paradigm for Traffic State Estimation written by Archie J. Huang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The traffic delay due to congestion cost the U.S. economy $81 billion in 2022, and on average, each worker lost 97 hours each year during commute due to longer wait time. Traffic management and control strategies that serve as a potent solution to the congestion problem require accurate information on prevailing traffic conditions. However, due to the cost of sensor installation and maintenance, associated sensor noise, and outages, the key traffic metrics are often observed partially, making the task of estimating traffic states (TSE) critical. The challenge of TSE lies in the sparsity of observed traffic data and the noise present in the measurements. The central research premise of this dissertation is whether and how the fundamental principles of traffic flow theory could be harnessed to augment machine learning in estimating traffic conditions. This dissertation develops a physics-informed deep learning (PIDL) paradigm for traffic state estimation. The developed PIDL framework equips a deep learning neural network with the strength of the governing physical laws of the traffic flow to better estimate traffic conditions based on partial and limited sensing measurements. First, this research develops a PIDL framework for TSE with the continuity equation Lighthill-Whitham-Richards (LWR) conservation law - a partial differential equation (PDE). The developed PIDL framework is illustrated with multiple fundamental diagrams capturing the relationship between traffic state variables. The framework is expanded to incorporate a more practical, discretized traffic flow model - the cell transmission model (CTM). Case studies are performed to validate the proposed PIDL paradigm by reconstructing the velocity and density fields using both synthetic and realistic traffic datasets, such as the next-generation simulation (NGSIM). The case studies mimic a multitude of application scenarios with pragmatic considerations such as sensor placement, coverage area, data loss, and the penetration rate of connected autonomous vehicles (CAVs). The study results indicate that the proposed PIDL approach brings exceedingly superior performance in state estimation tasks with a lower training data requirement compared to the benchmark deep learning (DL) method. Next, the dissertation continues with an investigation of the empirical evidence which points to the limitation of PIDL architectures with certain types of PDEs. It presents the challenges in training PIDL architecture by contrasting PIDL performances in learning the first-order scalar hyperbolic LWR conservation law and its second-order parabolic counterpart. The outcome indicates that PIDL experiences challenges in incorporating the hyperbolic LWR equation due to the non-smoothness of its solution. On the other hand, the PIDL architecture with the parabolic version of the PDE, augmented with the diffusion term, leads to the successful reassembly of the density field even with the shockwaves present. Thereafter, the implication of PIDL limitations for traffic state estimation and prediction is commented upon, and readers’ attention is directed to potential mitigation strategies. Lastly, a PIDL framework with nonlocal traffic flow physics, capturing the driver reaction to the downstream traffic conditions, is proposed. In summary, this dissertation showcases the vast capability of the developed physicsinformed deep learning paradigm for traffic state estimation in terms of efficiently utilizing meager observation for precise reconstruction of the data field. Moreover, it contemplates the practical ramification of PIDL for TSE with the hyperbolic flow conservation law and explores the remedy with sampling strategies of training instances and adding the diffusion term. Ultimately, it paints the picture of potent PIDL applications in TSE with nonlocal physics and suggests future research directions in PIDL for traffic state predictions.

Download or read book Mobility Data Driven Urban Traffic Monitoring written by Zhidan Liu and published by Springer Nature. This book was released on 2021-05-18 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the concepts of mobility data and data-driven urban traffic monitoring. A typical framework of mobility data-based urban traffic monitoring is also presented, and it describes the processes of mobility data collection, data processing, traffic modelling, and some practical issues of applying the models for urban traffic monitoring. This book presents three novel mobility data-driven urban traffic monitoring approaches. First, to attack the challenge of mobility data sparsity, the authors propose a compressive sensing-based urban traffic monitoring approach. This solution mines the traffic correlation at the road network scale and exploits the compressive sensing theory to recover traffic conditions of the whole road network from sparse traffic samplings. Second, the authors have compared the traffic estimation performances between linear and nonlinear traffic correlation models and proposed a dynamical non-linear traffic correlation modelling-based urban traffic monitoring approach. To address the challenge of involved huge computation overheads, the approach adapts the traffic modelling and estimations tasks to Apache Spark, a popular parallel computing framework. Third, in addition to mobility data collected by the public transit systems, the authors present a crowdsensing-based urban traffic monitoring approach. The proposal exploits the lightweight mobility data collected from participatory bus riders to recover traffic statuses through careful data processing and analysis. Last but not the least, the book points out some future research directions, which can further improve the accuracy and efficiency of mobility data-driven urban traffic monitoring at large scale. This book targets researchers, computer scientists, and engineers, who are interested in the research areas of intelligent transportation systems (ITS), urban computing, big data analytic, and Internet of Things (IoT). Advanced level students studying these topics benefit from this book as well.

Download or read book Data Driven Approaches for Traffic State and Emission Estimation written by Nikolaos Tsanakas and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Logic Driven Traffic Big Data Analytics written by Shaopeng Zhong and published by Springer Nature. This book was released on 2022-02-01 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book starts from the relationship between urban built environment and travel behavior and focuses on analyzing the origin of traffic phenomena behind the data through multi-source traffic big data, which makes the book unique and different from the previous data-driven traffic big data analysis literature. This book focuses on understanding, estimating, predicting, and optimizing mobility patterns. Readers can find multi-source traffic big data processing methods, related statistical analysis models, and practical case applications from this book. This book bridges the gap between traffic big data, statistical analysis models, and mobility pattern analysis with a systematic investigation of traffic big data’s impact on mobility patterns and urban planning.

Download or read book 2021 6th International Conference on Intelligent Transportation Engineering ICITE 2021 written by Zhenyuan Zhang and published by Springer Nature. This book was released on 2022-05-31 with total page 1238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features high-quality, peer-reviewed papers from the 2021 6th International Conference on Intelligent Transportation Engineering (ICITE 2021), held in Beijing, China, on October 29–31, 2021. Presenting the latest developments and technical solutions in Intelligent Transportation engineering, it covers a variety of topics, such as intelligent transportation, traffic control, road networking, intelligent automobile and vehicle operation & management. The book will be a valuable reference for graduate and postgraduate audiences, researchers and engineers, working in Intelligent Transportation Engineering.

Download or read book Proceedings of the Sixth International Conference of Transportation Research Group of India written by Lelitha Devi and published by Springer Nature. This book was released on 2022-09-28 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises the proceedings of the Sixth International Conference of Transportation Research Group of India (CTRG2021) focusing on emerging opportunities and challenges in the field of transportation of people and freight. The contents of the volume include characterization of conventional and innovative pavement materials, operational effects of road geometry, user impact of multimodal transport projects, spatial analysis of travel patterns, socio-economic impacts of transport projects, analysis of transportation policy and planning for safety and security, technology enabled models of mobility services, etc. This book will be beneficial to researchers, educators, practitioners and policy makers alike.

Download or read book Extended Selected Papers of the 14th International Conference on Information Intelligence Systems and Applications written by Nikolaos Bourbakis and published by Springer Nature. This book was released on with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Multi sensor Data Fusion Approach for Real time Lane based Traffic Estimation written by Zhuoyang Zhou and published by . This book was released on 2015 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern intelligent transportation systems (ITS) make driving more efficient, easier, and safer. Knowledge of real-time traffic conditions is a critical input for operating ITS. Real-time freeway traffic state estimation approaches have been used to quantify traffic conditions given limited amount of data collected by traffic sensors. Currently, almost all real-time estimation methods have been developed for estimating laterally aggregated traffic conditions in a roadway segment using link-based models which assume homogeneous conditions across multiple lanes. However, with new advances and applications of ITS, knowledge of lane-based traffic conditions is becoming important, where the traffic condition differences among lanes are recognized. In addition, most of the current real-time freeway traffic estimators consider only data from loop detectors. This dissertation develops a bi-level data fusion approach using heterogeneous multi-sensor measurements to estimate real-time lane-based freeway traffic conditions, which integrates a link-level model-based estimator and a lane-level data-driven estimator. Macroscopic traffic flow models describe the evolution of aggregated traffic characteristics over time and space, which are required by model-based traffic estimation approaches. Since current first-order Lagrangian macroscopic traffic flow model has some unrealistic implicit assumptions (e.g., infinite acceleration), a second-order Lagrangian macroscopic traffic flow model has been developed by incorporating drivers anticipation and reaction delay. A multi-sensor extended Kalman filter (MEKF) algorithm has been developed to combine heterogeneous measurements from multiple sources. A MEKF-based traffic estimator, explicitly using the developed second-order traffic flow model and measurements from loop detectors as well as GPS trajectories for given fractions of vehicles, has been proposed which gives real-time link-level traffic estimates in the bi-level estimation system. The lane-level estimation in the bi-level data fusion system uses the link-level estimates as priors and adopts a data-driven approach to obtain lane-based estimates, where now heterogeneous multi-sensor measurements are combined using parallel spatial-temporal filters. Experimental analysis shows that the second-order model can more realistically reproduce real world traffic flow patterns (e.g., stop-and-go waves). The MEKF-based link-level estimator exhibits more accurate results than the estimator that uses only a single data source. Evaluation of the lane-level estimator demonstrates that the proposed new bi-level multi-sensor data fusion system can provide very good estimates of real-time lane-based traffic conditions.

Download or read book Proceedings of the 5th International Conference on Big Data and Internet of Things written by Mohamed Lazaar and published by Springer Nature. This book was released on 2022-07-03 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of papers in the research area of big data, cloud computing, cybersecurity, machine learning, deep learning, e-learning, Internet of Things, reinforcement learning, information system, social media and natural language processing. This book includes papers presented at the 5th International Conference on Big Data Cloud and Internet of Things, BDIoT 2021 during March 17–18, 2021, at ENSIAS, Mohammed V University in Rabat, Morocco.

Download or read book Traffic Signal Systems written by and published by . This book was released on 2017 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Learning Deep Architectures for AI written by Yoshua Bengio and published by Now Publishers Inc. This book was released on 2009 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

Download or read book Graph Data Mining written by Qi Xuan and published by Springer Nature. This book was released on 2021-07-15 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph data is powerful, thanks to its ability to model arbitrary relationship between objects and is encountered in a range of real-world applications in fields such as bioinformatics, traffic network, scientific collaboration, world wide web and social networks. Graph data mining is used to discover useful information and knowledge from graph data. The complications of nodes, links and the semi-structure form present challenges in terms of the computation tasks, e.g., node classification, link prediction, and graph classification. In this context, various advanced techniques, including graph embedding and graph neural networks, have recently been proposed to improve the performance of graph data mining. This book provides a state-of-the-art review of graph data mining methods. It addresses a current hot topic – the security of graph data mining – and proposes a series of detection methods to identify adversarial samples in graph data. In addition, it introduces readers to graph augmentation and subgraph networks to further enhance the models, i.e., improve their accuracy and robustness. Lastly, the book describes the applications of these advanced techniques in various scenarios, such as traffic networks, social and technical networks, and blockchains.

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 Large scale Traffic Flow Prediction Using Deep Learning in the Context of Smart Mobility written by Arief Koesdwiady and published by . This book was released on 2018 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designing and developing a new generation of cities around the world (termed as smart cities) is fast becoming one of the ultimate solutions to overcome cities' problems such as population growth, pollution, energy crisis, and pressure demand on existing transportation infrastructure. One of the major aspects of a smart city is smart mobility. Smart mobility aims at improving transportation systems in several aspects: city logistics, info-mobility, and people-mobility. The emergence of the Internet of Car (IoC) phenomenon alongside with the development of Intelligent Transportation Systems (ITSs) opens some opportunities in improving the traffic management systems and assisting the travelers and authorities in their decision-making process. However, this has given rise to the generation of huge amount of data originated from human-device and device-device interaction. This is an opportunity and a challenge, and smart mobility will not meet its full potential unless valuable insights are extracted from these big data. Although the smart city environment and IoC allow for the generation and exchange of large amounts of data, there have not been yet well de ned and mature approaches for mining this wealth of information to benefit the drivers and traffic authorities. The main reason is most likely related to fundamental challenges in dealing with big data of various types and uncertain frequency coming from diverse sources. Mainly, the issues of types of data and uncertainty analysis in the predictions are indicated as the most challenging areas of study that have not been tackled yet. Important issues such as the nature of the data, i.e., stationary or non-stationary, and the prediction tasks, i.e., short-term or long-term, should also be taken into consideration. Based on this observation, a data-driven traffic flow prediction framework within the context of big data environment is proposed in this thesis. The main goal of this framework is to enhance the quality of traffic flow predictions, which can be used to assist travelers and traffic authorities in the decision-making process (whether for travel or management purposes). The proposed framework is focused around four main aspects that tackle major data-driven traffic flow prediction problems: the fusion of hard data for traffic flow prediction; the fusion of soft data for traffic flow prediction; prediction of non-stationary traffic flow; and prediction of multi-step traffic flow. All these aspects are investigated and formulated as computational based tools/algorithms/approaches adequately tailored to the nature of the data at hand. The first tool tackles the inherent big data problems and deals with the uncertainty in the prediction. It relies on the ability of deep learning approaches in handling huge amounts of data generated by a large-scale and complex transportation system with limited prior knowledge. Furthermore, motivated by the close correlation between road traffic and weather conditions, a novel deep-learning-based approach that predicts traffic flow by fusing the traffic history and weather data is proposed. The second tool fuses the streams of data (hard data) and event-based data (soft data) using Dempster Shafer Evidence Theory (DSET). One of the main features of the DSET is its ability to capture uncertainties in probabilities. Subsequently, an extension of DSET, namely Dempsters conditional rules for updating belief, is used to fuse traffic prediction beliefs coming from streams of data and event-based data sources. The third tool consists of a method to detect non-stationarities in the traffic flow and an algorithm to perform online adaptations of the traffic prediction model. The proposed detection approach is developed by monitoring the evolution of the spectral contents of the traffic flow. Furthermore, the approach is specfi cally developed to work in conjunction with state-of-the-art machine learning methods such as Deep Neural Network (DNN). By combining the power of frequency domain features and the known generalization capability and scalability of DNN in handling real-world data, it is expected that high prediction performances can be achieved. The last tool is developed to improve multi-step traffic flow prediction in the recursive and multi-output settings. In the recursive setting, an algorithm that augments the information about the current time-step is proposed. This algorithm is called Conditional Data as Demonstrator (C-DaD) and is an extension of an algorithm called Data as Demonstrator (DaD). Furthermore, in the multi-output setting, a novel approach of generating new history-future pairs of data that are aggregated with the original training data using Conditional Generative Adversarial Network (C-GAN) is developed. To demonstrate the capabilities of the proposed approaches, a series of experiments using artificial and real-world data are conducted. Each of the proposed approaches is compared with the state-of-the-art or currently existing approaches.

Download or read book Vehicle Traffic Estimation Using Deep Learning written by Meetkumar Patel and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: For commuters, vehicular traffic is an important planning concern. People have access to the weather forecast and the current traffic situation, but there is no application available to estimate traffic congestion and flow in the near future. Similarly, traffic management authorities also seek information about future traffic for traffic management purposes. Thus, we design and develop a machine learning approach which can predict vehicular traffic density and flowrate up to two days in the future based on the weather, calendar and special events data. First, Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) networks are utilized to predict the number of new vehicles and the total number of vehicles in images captured by a Nova Scotia Webcams (NS Webcams) video camera. The best models provide a Mean Absolute Percentage Error (MAPE) of 20.38% for the number of new vehicles and 18.56% for the total number of vehicles. These values are used to estimate traffic flowrate and density for hourly records over a three-month period. The hourly traffic data is combined with observed and forecasted weather data, retrieved from the DarkSky.net website and special event data provided by the Port of Halifax to create a time series data. A Multiple Task Learning (MTL) - LSTM model is trained and tested using these data and a K-fold cross-validation approach. The Mean Absolute Error (MAE) and MAPE are used to evaluate the model performance. The MTL-LSTM model achieves a MAPE of 19.35% and 27.50% for flowrate and density using observed weather data, respectively. In the case of forecasted weather data, the MAPE for flowrate and density increases to 20.51% and 31.10%, respectively.