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Page 118
Suggested Citation:"Abbreviations." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Page 118
Page 119
Suggested Citation:"Abbreviations." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Page 119
Page 120
Suggested Citation:"Abbreviations." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Page 120

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118 AAMVA American Association of Motor Vehicle Administrators Arizona DOT Arizona Department of Transportation AFC Automated Fare Collection AHMCT Advanced Highway Maintenance and Construction Technology Research Center ALPR Automatic License and Plate Reader/Recognition APCO Association of Public-Safety Communications Officials App Application APTRA Arizona Professional Towing and Recovery Association ATMS Advanced Traffic Management Systems AVL Automatic Vehicle Location AWS Amazon Web Services AZDPS Arizona Department of Public Safety BDE BigDataEurope CAD Computer-Aided Dispatch CATT Lab Center for Advanced Transportation Technology Laboratory CCP Connected Citizens Program CCTV Closed Circuit Television Colorado DOT Colorado Department of Transportation CDR Call Detail Records CHP California Highway Patrol CMS Changeable Message Sign CMSA Capability Maturity Self-Assessment CPU Central Processing Unit C.R.A.S.H. Crash Reduction Analyzing Statistical History CSV Comma-Separated Value DaaS Data-as-a-Service DAISy Data Analytics Intelligence System DCM Data Capture and Management DOT Department of Transportation DSRC Dedicated Short-Range Communications ECC Emergency Communications Center EDC Every Day Counts EDR Event Data Recorder EMS Emergency Medical Service(s) ESS Environmental Sensor Station ETL Extract-Transform-Load FARS Fatality Analysis Reporting System Abbreviations

Abbreviations 119 FDE Fundamental Data Elements FDEM Florida Department of Emergency Management Florida DOT Florida Department of Transportation FHP Florida Highway Patrol FOIA Freedom of Information Act FTP File Transfer Protocol GPS Global Positioning System GPU Graphics Processing Unit GTFS General Transit Feed Specification HDFS Hadoop Distributed File System HIPAA Health Insurance Portability and Accountability Act ICIJ International Consortium of Investigative Journalists ICT Incident Clearance Time IoT Internet of Things IR Incident Response IRCO Incident Response and Clearance Ontology ITF International Transport Forum ITS Intelligent Transportation Systems JOPS Joint Operations Policy Statement JSON JavaScript Object Notation KPI Key Performance Indicator KPM Key Performance Measure LODE Live Owl Documentation Environment MADIS Meteorological Assimilation Data Ingest System MAG Maricopa Association of Governments MCMIS Motor Carrier Management Information System MIRE Model Inventory of Roadway Elements MMUCC Model Minimum Uniform Crash Criteria MPO Metropolitan Planning Organization MTA Metropolitan Transit Authority MVDS Microwave Vehicle Detection System NASEMSO National Association of State Emergency Medical Services Officials NCEP National Centers for Environmental Prediction NCO National Central Operations NDS Naturalistic Driving Study NEMSIS National Emergency Medical Services Information System NFIRS National Fire Incident Reporting System NITTEC Niagara International Transportation Technology Coalition NOAA National Oceanic and Atmospheric Administration NPMRDS National Performance Management Research Data Set NWS National Weather Service NYCTA New York City Transit Authority O/D Origin-Destination ODI Open Data Institute Oregon DOT Oregon Department of Transportation OLAP Online Analytical Processing OLTP Online Transactional Processing OSP Oregon State Police PDR Public Data Release PII Personally Identifiable Information PSAP Public Safety Answering Point

120 Leveraging Big Data to Improve Traffic Incident Management RCT Roadway Clearance Time RDBMS Relational Database Management System RFID Radio Frequency Identification RITIS Regional Integrated Transportation Information System ROC Rio de Janeiro Operations Center RWIS Road Weather Information Systems RWMP Road Weather Management Program SaaS Software-as-a-Service SHRP Strategic Highway Research Program SHSP Strategic Highway Safety Plan SOP Standard Operating Procedures SQL Simple Query Language SSP Safety Service Patrol TAC Traffic Assistance Center TfL Transport for London THP Tennessee Highway Patrol TIM Traffic Incident Management TIM-BC Traffic Incident Management Benefit-Cost TIMELI Traffic Incident Management Enabled by Large-data Innovations TIM PM Traffic Incident Management Performance Measurement TMC Traffic Management Center TOC Traffic Operations Center TRCC Traffic Records Coordinating Committee TPEG Transport Protocol Experts Group UAV Unmanned Aerial Vehicle UBI Usage Based Insurance Utah DOT Utah Department of Transportation V2C Vehicle-to-Cloud V2I Vehicle-to-Infrastructure V2V Vehicle-to-Vehicle VASTIM Virginia Statewide Traffic Incident Management Committee VEACON Vehicular Accident Ontology VTTI Virginia Tech Transportation Institute W3C World Wide Web Consortium WITS Washington Incident Tracking System WMV Windows Media Video WxDE Weather Data Environment XML Extensible Mark-up Language

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"Big data" is not new, but applications in the field of transportation are more recent, having occurred within the past few years, and include applications in the areas of planning, parking, trucking, public transportation, operations, ITS, and other more niche areas. A significant gap exists between the current state of the practice in big data analytics (such as image recognition and graph analytics) and the state of DOT applications of data for traffic incident management (TIM) (such as the manual use of Waze data for incident detection).

The term big data represents a fundamental change in what data is collected and how it is collected, analyzed, and used to uncover trends and relationships. The ability to merge multiple, diverse, and comprehensive datasets and then mine the data to uncover or derive useful information on heretofore unknown or unanticipated trends and relationships could provide significant opportunities to advance the state of the practice in TIM policies, strategies, practices, and resource management.

NCHRP (National Cooperative Highway Research Program) Report 904: Leveraging Big Data to Improve Traffic Incident Management illuminates big data concepts, applications, and analyses; describes current and emerging sources of data that could improve TIM; describes potential opportunities for TIM agencies to leverage big data; identifies potential challenges associated with the use of big data; and develops guidelines to help advance the state of the practice for TIM agencies.

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