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This synthesis will be of interest to traffic engineers, highway planners, and others concerned with the collection of traffic data for traffic engineering studies, for long-range planning, and for evaluation of traffic law enforcement. Information is presented on current practice in traffic data collection and analysis. Although types of highway traffic data collected over the past 50 years have not changed significantly, the quantities, analysis procedure, and presentations of these data have changed as a result of changing policies, operational concerns, and capabilities resulting from new technologies. This report of the Transportation Research Board describes the technology (both hardware and software) that is being used for traffic data collection, and discusses technological advances that have not yet been applied to the acquisition and presentation of traffic data.
A nice night of October 2007, in Beijing, during the XV World Conference on ITS a number of colleagues met informally for a dinner party that spontaneously became a vivid discussion on the importance of traffic data for all types of p- poses. Researchers can hardly do any progress in modeling, developing, and te- ing theories without suitable data, and what practitioners can do in real life is limited not only by technology but also by the availability of the required data. Quite frequently, the data and not the technologies are what determine how far we can go. Any discussion about traffic data leads in a natural way to a discussion on the variety of traffic data sources, formats, levels of aggregation, accuracies, and so on. Consequently, we moved to talk on the initiative that Kuwahara had undertaken in his traffic laboratory at the University of Tokyo, known as the International Traffic Data Base, and thus smoothly but inexorably we came to agree that it would be convenient to organize a workshop to continue our discussion at a more formal level, share our points of view with other colleagues, listen what they had to say and, if possible, d- seminate the findings in our professional and academic communities.
Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems that includes detailed coverage of the tools needed to implement these methods using big data analytics and other computing techniques. The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce. It explores collecting, archiving, processing, and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies. Users will learn how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning. Includes case studies in each chapter that illustrate the application of concepts covered Presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies Contains contributors from both leading academic and commercial researchers Explains how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications
Road traffic and its impacts affect all aspects of modern life, leisure and industry, with safety, congestion and pollution being of greatest public concern. Transport planning increasingly emphasises travel demand management (TDM) and traffic calming - aided by dynamic, lower cost data from Intelligent Transport Systems (ITS) - to enable real time monitoring, control and traveller information. This second edition of a highly successful work has been fully updated since its first publication in 1996 to reflect developments in technology available to the traffic analyst and in the social, ecological and economic environment. New sections are included on shockwaves, data capture without surveys, traffic incidents, delay estimation, off-line use of on-line data, environmental sensitivity, and controlled crash tests. The authors introduce and demonstrate techniques with which the analyst, engineer or planner can examine traffic problems. The underlying theme is that proper understanding of traffic systems performance and traffic problems can only come from the intelligent processing, refinement, appraisal and evaluation of traffic data. Arranged in five parts, the book offers an integrated approach to tackling road traffic problems: ¢ How to gain information and understanding about traffic ¢ The theories of traffic flow ¢ The principles of good survey planning and management ¢ Specific types of traffic studies ¢ Analytical techniques for transforming raw data into useful information. Understanding Traffic Systems provides cogent insights into the techniques of traffic data collection and analysis, the application of traffic theory and the role of data in analysis and decision making. Its breadth and use of examples from several countries make it a useful reference text for students and researchers, as well as an essential tool for practising traffic engineers and planners.
A nice night of October 2007, in Beijing, during the XV World Conference on ITS a number of colleagues met informally for a dinner party that spontaneously became a vivid discussion on the importance of traffic data for all types of p- poses. Researchers can hardly do any progress in modeling, developing, and te- ing theories without suitable data, and what practitioners can do in real life is limited not only by technology but also by the availability of the required data. Quite frequently, the data and not the technologies are what determine how far we can go. Any discussion about traffic data leads in a natural way to a discussion on the variety of traffic data sources, formats, levels of aggregation, accuracies, and so on. Consequently, we moved to talk on the initiative that Kuwahara had undertaken in his traffic laboratory at the University of Tokyo, known as the International Traffic Data Base, and thus smoothly but inexorably we came to agree that it would be convenient to organize a workshop to continue our discussion at a more formal level, share our points of view with other colleagues, listen what they had to say and, if possible, d- seminate the findings in our professional and academic communities.
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.
This report provides a summary of the Strategic Highway Research Program's Long-Term Pavement Performance (SHRP-LTPP) 5-year effort to better understand traffic's effect on pavement performance. The report also reviews the traffic data collection program over a 4-year period. It provides a connection with the reports and publications issued during the period by providing an extensive reference list.