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This volume constitutes selected and revised papers of the 5th International Conference on Advanced Informatics for Computing Research, ICAICR 2021, held in Gurugram, India, in December 2021. The 17 revised full papers and 6 short papers presented were carefully reviewed and selected from 306 submissions. The papers targeted state-of-the-art as well as emerging topics pertaining to advanced informatics for computing research and its implementation for engineering applications.
"For more than 50 years, the Transportation Research Record has been internationally recognized as one of the preeminent peer-reviewed journals for transportation research papers from authors in the United States and from around the world. One of the most cited transportation journals, the TRR offers unparalleled depth and breadth in the coverage of transportation topics from both academic and practitioner perspectives. All modes of passenger and freight transportation are addressed in papers covering a wide array of disciplines, including policy, planning, administration, economics and financing, operations, construction, design, maintenance, safety, and more."--Publisher's website
Intelligent Transportation Systems: Functional Design for Economical and Efficient Traffic Management provides practical guidance on the efficient use of resources in the design of ITS. The author explains how functional design alternatives can meet project objectives and requirements with optimal cost effectiveness and clarifies how transportation planning and traffic diversion principles relate to functional ITS device selections and equipment locations. Methodologies for translating objectives to functional device types, determining device deployment densities and determining the best placement of CCTV cameras and message signs are provided, as are models for evaluating the benefits of design alternatives based on traffic conditions. Readers will learn how to reduce recurrent congestion, improve incident clearance time in non-recurrent congestion, provide real-time incident information to motorists, and leverage transportation management center data for lane control through important new active transportation and demand management (ATDM) methods. Finally, the author examines exciting developments in connected vehicle technologies, exploring their potential to greatly improve safety, mobility and energy efficiency. This resource will greatly benefit all ITS designers and managers and is of pivotal importance for operating agencies performing evaluations to justify operational funding and system expansions.
The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
Advances in hardware technology have lead to an ability to collect data with the use of a variety of sensor technologies. In particular sensor notes have become cheaper and more efficient, and have even been integrated into day-to-day devices of use, such as mobile phones. This has lead to a much larger scale of applicability and mining of sensor data sets. The human-centric aspect of sensor data has created tremendous opportunities in integrating social aspects of sensor data collection into the mining process. Managing and Mining Sensor Data is a contributed volume by prominent leaders in this field, targeting advanced-level students in computer science as a secondary text book or reference. Practitioners and researchers working in this field will also find this book useful.
At the rate that government and nongovernmental organizations are clearing existing landmines, it will take 450-500 years to rid the world of them. Concerned about the slow pace of demining, the Office of Science and Technology asked RAND to assess potential innovative technologies being explored and to project what funding would be required to foster the development of the more promising ones. The authors of this report suggest that the federal government undertake a research and development effort to develop a multisensor mine detection system over the next five to eight years.
Topics included : Robust route guidance model based on advanced traveler information systems, using decision support system and graphical user interface to choose appropriate lane configurations at toll facilities, AASHTO SiteManager, predictive traffic information, designing advanced traffic surveillance systems, modeling urban link travel time with inductive loop detector data, Markov Chain and Monte Carlo multiple imputation for intelligent transportation systems data, ISO 19133 tracking and navigation standard, efficient map matching of large global positioning system data sets, lane-based network for flow analysis and inventory management, using the Ant Algorithm to derive Pareto fonts, using fuzzy quality function deployment model (Airport cargo terminals), travel time predition, vehicle detection and classification, Q-learning for flexible learning, etc.