Download or read book Short Term Traffic Prediction in Large Scale Urban Networks written by Matej Cebecauer and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Enhancing Short Term Traffic Prediction for Large Scale Transport Networks by Spatio Temporal Clustering written by Matej Cebecauer 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 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 Inter urban Short term Traffic Congestion Prediction written by Giovanni Huisken and published by . This book was released on 2006 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Short term Traffic Flow Prediction in Urban Areas Using Neural Networks written by 葉政威 and published by . This book was released on 2017 with total page 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 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 Predicting Short Term Traffic Congestion on Urban Motorway Networks written by Taiwo Olubunmi Adetiloye and published by . This book was released on 2018 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traffic congestion is a widely occurring phenomenon caused by increased use of vehicles on roads resulting in slower speeds, longer delays, and increased vehicular queueing in traffic. Every year, over a thousand hours are spent in traffic congestion leading to great cost and time losses. In this thesis, we propose a multimodal data fusion framework for predicting traffic congestion on urban motorway networks. It comprises of three main approaches. The first approach predicts traffic congestion on urban motorway networks using data mining techniques. Two categories of models are considered namely neural networks, and random forest classifiers. The neural network models include the back propagation neural network and deep belief network. The second approach predicts traffic congestion using social media data. Twitter traffic delay tweets are analyzed using sentiment analysis and cluster classification for traffic flow prediction. Lastly, we propose a data fusion framework as the third approach. It comprises of two main techniques. The homogeneous data fusion technique fuses data of same types (quantitative or numeric) estimated using machine learning algorithms. The heterogeneous data fusion technique fuses the quantitative data obtained from the homogeneous data fusion model and the qualitative or categorical data (i.e. traffic tweet information) from twitter data source using Mamdani fuzzy rule inferencing systems. The proposed work has strong practical applicability and can be used by traffic planners and decision makers in traffic congestion monitoring, prediction and route generation under disruption.

Download or read book Introduction to Modern Time Series Analysis written by Gebhard Kirchgässner and published by Springer Science & Business Media. This book was released on 2008-08-27 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series. It contains the most important approaches to analyze time series which may be stationary or nonstationary.

Download or read book Traffic Control in Large scale Urban Networks written by Liudmila Tumash and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research is done in the context of European Research Council's Advanced Grant project Scale-FreeBack. The aim of Scale-FreeBack project is to develop a holistic scale-free control approach to complex systems, and to set new foundations for a theory dealing with complex physical networks with arbitrary dimension. One particular case is intelligent transportation systems that are capable to prevent the occurrence of congestions in rush hours. The contributions of the present PhD work are mainly related to traffic boundary control design and modelling on large-scale urban networks. We consider traffic from the macroscopic viewpoint describing it in terms of aggregated variables such as flow and density of vehicles, i.e., traffic is seen as a fluid whose motion is described using the concept of kinematic waves. The corresponding dynamic equation corresponds to a first-order hyperbolic partial differential equation. Within this PhD thesis, we propose control design techniques that completely rely on the intrinsic properties of the model. First of all, we solve one-dimensional (1D) boundary control problems, i.e., one road traffic. Thereby, the traffic state is driven to a space- and time-dependent desired trajectory that admits traffic regimes switching, i.e., both states can be partially congested and partially in the free-flow regime. This introduces non-linearities into the state equation, which we can handle and achieve the target by acting only from road's boundaries. Then, we extend the problem to a urban network of arbitrary size. The large-scale traffic dynamics are described by a two-dimensional (2D) conservation law model. The model parameters are defined everywhere in the continuum plane from its values on physical roads that are further interpolated as a function of distance to these roads. The traffic flow direction is determined by network's geometry (location of roads and intersections) and infrastructure parameters (speed limits, number of lanes, etc). This 2D model assumes that there exists a preferred direction of motion. For this case, we elaborate a unique method that considerably simplifies control design for traffic systems evolving in large-scale networks. In particular, we present a coordinate transformation that translates a 2D continuous traffic model into a continuous set of 1D systems equations. This enables an explicit elaboration of strategies for various control tasks to solve on large-scale networks: we design boundary control for 2D density in a mixed traffic regime, apply variable speed limit control to drive traffic to any space-dependent equilibrium, and calculate steady-states. Finally, we also present a new multi-directional two-dimensional continuous traffic model. This model is formally derived by solely using the demand-supply concept at one intersection (classical Cell Transmission Model). Our new model is called the NSWE-model, since it consists of four partial differential equations that describe the evolution of vehicle density with respect to cardinal directions: North, South, West and East. The traffic flow direction is determined by turning ratios at intersections. For this model, we design a boundary control that drives multi-directional congested traffic to a desired equilibrium vehicle density mitigating the congestion level. The effectiveness of our contributions were tested using simulated and real data. In the first case, the results are verified by using the well-known commercial traffic Aimsun, which produces microsimulations of vehicles' trajectories in a modelled network. In the second case, real data are obtained from sensors measuring traffic flow in the city of Grenoble, and collected using the Grenoble Traffic Lab.

Download or read book Traffic Modeling Estimation and Control for Large scale Congested Urban Networks written by Mohsen Ramezani Ghalenoei and published by . This book was released on 2014 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Pervasive Computing written by Judy Kay and published by Springer. This book was released on 2012-06-28 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th International Conference on Pervasive Computing, Pervasive 2012, held in Newcastle, UK, in June 2012. The 28 revised papers presented were carefully reviewed and selected from 138 submissions. The contributions are grouped into the following topical sections: activity capturing; urban mobility and computing; home and energy; HCI; development tools and devices; indoor location and positioning; social computing and games; privacy; public displays and services.

Download or read book Efficient Model Predictive Control for Large scale Urban Traffic Networks written by Shu Lin and published by . This book was released on 2011 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Urban Informatics written by Wenzhong Shi and published by Springer Nature. This book was released on 2021-04-06 with total page 941 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity.

Download or read book Urban Area Traffic Flow Forecasting in Intelligent Transportation Systems written by Ziyue Wang and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Currently, Intelligent Transportation Systems (ITS), is revolutionizing the transportation industry. ITS incorporates advanced Internet of Things (IoT) technologies to implement "Smart City". These technologies produce tremendous amount of real time data from diverse sources that can be used to solve transportation problems. In this thesis, I focus on one such problem, traffic congestion in urban areas. A road segment affected by traffic affects the surrounding road segments. This is obvious. However, over a period of time, other roads not necessarily close in proximity to the congested road segment may also be affected. The congestion is not stationary. It is dynamic and it spreads. I address this issue by first formulating a similarity function using ideas from network theory. Using this similarity function, I then cluster the road points affected by traffic using affinity propagation clustering, a distributed message passing algorithm. Finally, I predict the effect of traffic on this cluster using long-short term memory neural network model. I evaluate and show the feasibility of my proposed clustering and prediction algorithm during peak and non-peak hours on open source traffic data set.

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.