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With increased connectivity between vehicles, sensors, systems, shared-use transportation, and mobile devices, unexpected and unparalleled amounts of data are being added to the transportation domain at a rapid rate, and these data are too large, too varied in nature, and will change too quickly to be handled by the traditional database management systems of most transportation agencies. The TRB National Cooperative Highway Research Program's NCHRP Research Report 952: Guidebook for Managing Data from Emerging Technologies for Transportation provides guidance, tools, and a big data management framework, and it lays out a roadmap for transportation agencies on how they can begin to shift - technically, institutionally, and culturally - toward effectively managing data from emerging technologies. Modern, flexible, and scalable "big data" methods to manage these data need to be adopted by transportation agencies if the data are to be used to facilitate better decision-making. As many agencies are already forced to do more with less while meeting higher public expectations, continuing with traditional data management systems and practices will prove costly for agencies unable to shift.
Effective transportation asset management (TAM) depends on having good data about the assets under management, their descriptions, current condition and history, functional performance, and the activities conducted to develop, maintain, improve, and rehabilitate them during the course of their service lives. The TRB National Cooperative Highway Research Program's NCHRP Research Report 956: Guidebook for Data and Information Systems for Transportation Asset Management presents a structured approach for assessing an organization's current data and information management practices in support of transportation asset management and strategies for improving these practices.
With increased connectivity between vehicles, sensors, systems, shared-use transportation, and mobile devices, unexpected and unparalleled amounts of data are being added to the transportation domain at a rapid rate, and these data are too large, too varied in nature, and will change too quickly to be handled by the traditional database management systems of most transportation agencies. The TRB National Cooperative Highway Research Program's NCHRP Research Report 952: Guidebook for Managing Data from Emerging Technologies for Transportation provides guidance, tools, and a big data management framework, and it lays out a roadmap for transportation agencies on how they can begin to shift - technically, institutionally, and culturally - toward effectively managing data from emerging technologies. Modern, flexible, and scalable "big data" methods to manage these data need to be adopted by transportation agencies if the data are to be used to facilitate better decision-making. As many agencies are already forced to do more with less while meeting higher public expectations, continuing with traditional data management systems and practices will prove costly for agencies unable to shift.
Effective transportation asset management (TAM) depends on having good data about the assets under management, their descriptions, current condition and history, functional performance, and the activities conducted to develop, maintain, improve, and rehabilitate them during the course of their service lives. The TRB National Cooperative Highway Research Program's NCHRP Research Report 956: Guidebook for Data and Information Systems for Transportation Asset Management presents a structured approach for assessing an organization's current data and information management practices in support of transportation asset management and strategies for improving these practices.
The Guide to Transportation Management Center (TMC) Data Capture for Performance and Mobility Measures is a two-volume document consisting of this summary Guidebook and a Reference Manual. These documents provide technical guidance and recommended practices regarding concepts, methods, techniques, and procedures for collecting, analyzing, and archiving TMC operations data to develop measures of roadway and TMC performance, as well as documenting the benefits of TMC activities for a variety of stakeholders. This guide is designed to be used by TMC technical and management staff involved in developing, implementing, and/or refining a TMC performance monitoring program.
The Guide to Transportation Management Center (TMC) Data Capture for Performance and Mobility Measures is a two-volume document consisting of this summary Guidebook and a Reference Manual. These documents provide technical guidance and recommended practices regarding concepts, methods, techniques, and procedures for collecting, analyzing, and archiving TMC operations data to develop measures of roadway and TMC performance, as well as documenting the benefits of TMC activities for a variety of stakeholders. This guide is designed to be used by TMC technical and management staff involved in developing, implementing, and/or refining a TMC performance monitoring program. Effective performance monitoring efforts can assist the user in a variety of tasks including traffic performance monitoring, asset management, evaluation of TMC activities and strategies, and planning and decision-making. They can also provide persuasive data in support of continued or enhanced TMC programs; conversely, a lack of available data regarding the value of TMC programs can make agencies more vulnerable to budget reductions when resources are constrained and the remaining budgets are being allocated. The Guide to TMC Data Capture for Performance and Mobility Measures consists of two parts: The summary Guidebook and the more detailed Reference Manual. This Guidebook provides an overview of TMC performance monitoring guidelines, measures, and issues, with a focus on the “what” and the “why”(i.e., what are the primary metrics that TMCs should consider for their performance and mobility monitoring programs, and why should they be used?). The Reference Manual includes details on the “how” (i.e., how does a TMC implement a monitoring program using a given performance metric?). The Reference Manual also expands on the discussion in the Guidebook and provides a convenient synopsis of each performance measure (or group of related performance measures), including an overview of the measure's usefulness, required data sources, primary calculation steps or equations, useful variations of the measure, issues or implementation considerations associated with the use of that measure, and example applications from TMCs around the country.
Mobility Patterns, Big Data and Transport Analytics provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing and controlling mobility patterns - a key aspect of transportation modeling. The book features prominent international experts who provide overviews on new analytical frameworks, applications and concepts in mobility analysis and transportation systems. Users will find a detailed, mobility 'structural' analysis and a look at the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications and transportation systems analysis that are related to complex processes and phenomena. This book bridges the gap between big data, data science, and transportation systems analysis with a study of big data's impact on mobility and an introduction to the tools necessary to apply new techniques. The book covers in detail, mobility 'structural' analysis (and its dynamics), the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications, and transportation systems analysis related to complex processes and phenomena. The book bridges the gap between big data, data science, and Transportation Systems Analysis with a study of big data's impact on mobility, and an introduction to the tools necessary to apply new techniques. - Guides readers through the paradigm-shifting opportunities and challenges of handling Big Data in transportation modeling and analytics - Covers current analytical innovations focused on capturing, predicting, visualizing, and controlling mobility patterns, while discussing future trends - Delivers an introduction to transportation-related information advances, providing a benchmark reference by world-leading experts in the field - Captures and manages mobility patterns, covering multiple purposes and alternative transport modes, in a multi-disciplinary approach - Companion website features videos showing the analyses performed, as well as test codes and data-sets, allowing readers to recreate the presented analyses and apply the highlighted techniques to their own data
TRB's National Cooperative Highway Research Program (NCHRP) has released a pre-publication version of NCHRP Research Report 920: Management and Use of Data for Transportation Performance Management: Guide for Practitioners that provides practical guidance to transportation agencies to help improve their use of data for performance management. Recent federal legislation has established requirements for agencies to set performance targets and report on safety, pavement and bridge conditions, transit asset state of good repair, system performance, freight, and mobile source emissions. These requirements have resulted in increased visibility and attention to Transportation Performance Management (TPM) and increased awareness of the importance of data within that process. Transportation agencies are recognizing that the value of performance management goes far beyond meeting federal requirements; NCHRP Report 920 will assist agencies to make visible progress in meeting their objectives. The guidance is organized around six data life-cycle stages and includes a discussion of what is involved in implementing each step and some of the critical choices to be made; a synthesis of key points in the form of "Dos and Don'ts" checklists that can be used to assess agency capabilities and identify opportunities for improvement; and illustrative examples. While this guide draws on many examples related to the federally defined TPM areas (safety, pavement, bridge, and system performance), it does not provide official guidance for MAP-21/FAST Act target setting or reporting. It provides a framework for assessing current data management practices and a source of ideas for practice improvement. Its purpose is to promote practices that will enable agencies to go beyond meeting reporting requirements, to get valuable insights from data that can be used to boost agency results.