A Primer to Prepare for the Connected Airport and the Internet of Things (2018)

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70 • 3D printing: the action or process of making a physical object from a three-dimensional (3D) digital model, typically by laying down many thin layers of a material in succession. • Actuator: a device that complements a sensor in a sensing system. An actuator converts an electrical signal into action, often by converting the signal to nonelectrical energy, such as motion. A simple example of an actuator is an electric motor that converts electric energy into mechanical energy. • Airport collaborative decision-making (ACDM) tool: a tool that combines information from the airport, airlines, air traffic control, and other sources to support more efficient sur- face movement and other operational needs. • Airport Service Quality rating: a global benchmarking program of the Airports Council International to measure passenger satisfaction while traveling through an airport. • Analytics: the systematic analysis of confusing and conflicting data in search of insight that may inform better decisions. • Application program interface (API): a set of software commands, functions, and protocols that programmers can use to develop software that can run on a certain operating system or website. APIs make it easier for programmers to develop software and ensure that users expe- rience the same user interface when using various software built on the same API. • Artificial intelligence: the theory and development of computer systems able to perform tasks that normally require human intelligence. The field of artificial intelligence has produced a number of cognitive technologies such as computer vision, natural-language processing, and speech recognition. • Automated passport control: a program for border control that uses self-service kiosks to allow travelers to verify information through an automated process. • Automated teller machine (ATM): an electronic banking device that enables customers of financial institutions to perform financial transactions, such as cash withdrawals, deposits, or acquisition of account information, at any time and without the need for direct interaction with bank staff. • Auxiliary power unit (APU): a device on a vehicle, such as a plane, that provides energy for functions other than propulsion. • Batch processing: the execution of a series of computer programs without the need for human intervention. Traditional analytics software generally works on batch-oriented processing in which data are aggregated in batches and then processed. This approach, however, does not deliver the low latency required for near-real-time analysis applications. • Beacon: a class of sensors that can help report the location or presence of an object or person in a certain area. Among the most common beacons are those that operate via the Bluetooth communication protocol. • Big data: a term popularly used to describe large data sets that cannot be handled efficiently by traditional data management systems. The concept of big data also refers to the variety of A P P E N D I X A Glossary

Glossary 71 data sets—structured and unstructured—as well as the velocity or rate at which the data are incoming. • Bluetooth: a standard for the short-range wireless interconnection of mobile phones, com- puters, and other electronic devices. • Bluetooth low-energy beacon: a class of wireless devices that transmit their identifier to nearby portable electronic devices. • Building management system (BMS): a computer-based system that controls and monitors a building’s mechanical and electrical equipment such as ventilation, lighting, power systems, fire systems, and security systems. • Cloud computing: an infrastructure of shared resources (e.g., servers; networks; and software applications and services) that allows users to scale up their data management and processing abilities while keeping costs low. A cloud vendor invests in and maintains the cloud infra- structure; a user pays for only the resources and applications he or she wishes to use. • Cognitive technologies: a set of technologies able to perform tasks that only humans used to be able to perform. Examples of cognitive technologies include computer vision, natural- language processing, and speech recognition. • Communication protocol: a set of rules that provide a common language for devices to com- municate. Different communication protocols are used for device-to-device communication; broadly, they vary in the format in which data packets are transferred. One example is Hyper- text Transfer Protocol (HTTP). • Complex event processing (CEP): an analytics tool that enables processing and analysis of data on a real-time or a near-real-time basis, driving timely decision-making and action. CEP is relevant for IoT in its ability to recognize patterns in massive data sets at low latency rates. A CEP tool identifies patterns by using a variety of techniques such as filtering, aggregation, and correlation to trigger an automated action or to flag the need for human intervention. • Computer vision: a type of cognitive technology that refers to the ability of computers to identify objects, scenes, and activities in images. Computer-vision technology uses sequences of imaging-processing operations and other techniques to decompose the task of analyzing images into manageable pieces. Certain techniques, for example, allow for detecting the edges and textures of objects in an image. Classification models may be used to determine if the fea- tures identified in an image are likely to represent a kind of object already known to the system. • Data rate: the speed at which data are transferred by a network. Sometimes termed band- width, data rates are typically measured in bits transferred per second. Network technologies that are currently available can deliver data rates of up to 1 gigabyte per second. • Descriptive analytics: a type of analytics that provides insights into past business events and performance. In a fundamental sense, descriptive analytics helps answer the question “What has happened?” Descriptive analytics tools augment human intelligence by allowing users to work effectively with much larger or more complex data sets than they would ordinarily be able to without such tools. • Fast and Seamless Travel initiative: a program of the International Air Transport Association to improve operations at airports. • Gateway: a combination of hardware and software components that connects one network to another. • Heating, ventilation, and air conditioning (HVAC): the system of technologies used to pro- vide heating and cooling services in buildings and vehicles. • In-memory processing: the process of storing data in random access memory instead of hard disks; this enables quicker data querying, retrieval, and visualizations. • Information technology (IT): the study or use of systems (especially computers and telecom- munications) for storing, retrieving, and sending information. • International Air Transport Association (IATA): a trade organization of airlines that sup- ports aviation with global standards for airline safety, security, efficiency, and sustainability.

72 A Primer to Prepare for the Connected Airport and the Internet of Things • Internet Protocol (IP): an open network protocol that provides unique addresses to vari- ous devices connected to the Internet. The two versions of IP are IP version 4 (IPv4) and IP version 6 (IPv6). • Internet transit price: the price charged by an Internet service provider (ISP) to transfer data on a network. Since no single ISP can cover the worldwide network, ISPs rely on each other to transfer data using network interconnections through gateways. • IP version 4 (IPv4): an older version of the Internet Protocol; IPv6 is a most recent version. IPv4 offers an addressing space of about 6 billion addresses, out of which 4 billion addresses have been used already. IPv4 allows a group of co-located sensors to be identified geographi- cally but not individually, thus restricting the value that can be generated through the scope of data collected from individual devices that are co-located. • IP version 6 (IPv6): a recent version of the Internet Protocol that succeeds IPv4. IPv6 has superior scalability and identifiability features compared with IPv4: the IPv6 address space supports approximately 3.4×1038 unique addresses compared with 6 billion addresses under IPv4. • Latency: the time delay in the transfer of data from one point in a network to another. Low-latency networks allow for near-real-time data communications. • Local area network (LAN): a network that extends to a geographic range of at least 100 m, such as within a house or office. Devices can connect to wired or wireless LAN technologies. Examples of wired LAN technologies include Ethernet and fiber optics. Wi-Fi is an example of a wireless LAN technology. • Low-power wide area network (LoRaWAN): a type of wireless telecommunication WAN designed to allow long-range communications at a low bit rate among connected objects, such as sensors operated on a battery. • Machine learning: the ability of computer systems to improve their performance by expo- sure to data, without the need to follow explicitly programmed instructions. At its core, machine learning is the process of automatically discovering patterns in data. Once discov- ered, the pattern can be used to make predictions. For instance, presented with a database of information about credit-card transactions—such as date, time, merchant, merchant loca- tion, price, and whether the transaction was legitimate or fraudulent—a machine-learning system recognizes patterns that are predictive of fraud. The more transaction data the system processes, the better its predictions are expected to be. • Machine-to-human (M2H) interface: a set of technologies that enable machines to interact with human beings. Some common examples of M2H interfaces include wearables, home automation devices, and autonomous vehicles. Based on the data collected and algorithmic calculations, machines have the potential to convey suggestive actions to individuals who then exercise their discretion to take or not to take the recommended action. • Machine-to-machine (M2M) interface: a set of technologies that enable machines to com- municate with other machines and drive action. In common vernacular, M2M is often used interchangeably with IoT. For the team’s purposes, though, IoT is a broader concept that includes M2M and M2H interfaces, as well as support systems that facilitate the management of information in a way that creates value. • Metadata: the data that describe other data. For example, metadata for a document would typically include the author’s name, size of the document, last created or modified date, and so forth. • Narrow-band IoT (NBIoT): a type of low-power WAN designed to enable a range of devices and services to connect using cellular telecommunications bands. • National Institute of Standards and Technology: a measurement standards laboratory and a nonregulatory agency of the U.S. Department of Commerce. • Network: an infrastructure of hardware components and software protocols that allows devices to share data with each other. Networks can be wired (e.g., Ethernet) or wireless (e.g., Wi-Fi).

Glossary 73 • Network protocol: a set of rules that define how computers identify each other on a network. One example of a network protocol is the Internet Protocol, which offers unique addresses to machines connected to the Internet. • Nitrogen dioxide: one of a group of gases called nitrogen oxides that primarily come from the burning of fuel and are of concern for their potential effects on human health and air quality. • Parallel processing: the concurrent processing of data on clusters of computers in which each computer offers local aggregation and storage. • Personal area network (PAN): a network that extends to a small geographic range of at least 10 m, such as inside a room. Devices can connect to wireless PAN technologies such as Blue- tooth and ZigBee, as well as wired PAN technologies such as Universal Serial Bus (USB). • Predictive analytics: the computational tools that aim to answer questions related to “What might be happening or could happen, given historical trends?” Predictive analytics exploits the large quantity and the increasing variety of data to build useful models that correlate sometimes seemingly unrelated variables. Predictive models are expected to produce more accurate results through machine learning, a process that refers to computer systems’ abil- ity to improve their performance by exposure to data without the need to follow explicitly programmed instructions. • Prescriptive analytics: the computational tools that endeavor to answer questions related to “What should one do to achieve a desired outcome?” based on data related to what has hap- pened and what could happen. Prescriptive analytics includes optimization techniques that are based on large data sets, business rules (information on constraints), and mathematical models. Prescriptive algorithms can continuously include new data and improve prescriptive accuracy in decision optimizations. • Radio frequency identification (RFID) tag: a tag that uses radio frequency signals to transmit data about the tag to RFID readers. • Real-time processing: the processing of data instantaneously upon receiving the data and/or instruction. There is often the question of “What data can be considered truly real?” Ideally, data are valid the second they are generated; however, because of practical issues related to latency, the meaning of real time varies from application to application. • Relational database: a type of database that organizes data by establishing relationships based on a unique identifier. Structured data stored in relational databases can be queried using structured query language (SQL). • Return on investment (ROI): A performance measure used to evaluate the efficiency of an investment as a ratio of the benefit (or return) of an investment divided by the cost of the investment. • Sensor: a device that is used to sense a physical condition or event. A sensor works by convert- ing a nonelectrical input into an electrical signal that can be sent to an electronic circuit. A sensor does not function by itself—it is a part of a larger system made up of microprocessors, modem chips, power sources, and other related devices. • Smart building: a real estate project that integrates IoT technologies and applications to transform physical spaces, increase building efficiency, and enhance productivity of tenants. • Speech recognition: a type of cognitive technology that focuses on accurately transcribing human speech. The technology has to accommodate challenges such as diverse accents, back- ground noise, homophones (e.g., principle and principal), speed of speaking, and so forth. Speech-recognition systems use some of the same techniques as natural-language processing systems, as well as others such as acoustic models that describe sounds and their probability of occurring in a certain sequence in a given language. • Structured data: the data stored in predefined formats, such as rows and columns in spreadsheets. Structured data are generally stored in relational databases and can be queried using SQL.

74 A Primer to Prepare for the Connected Airport and the Internet of Things • Transportation network company (TNC): an organization that uses digital technology to connect passengers to drivers via mobile applications or a website to arrange taxi-like services. • Unstructured data: data that do not fit into predefined formats. Common sources of unstruc- tured data include images, videos, webpages, emails, blog entries, and Microsoft® Word documents. • Wi-Fi: a technology that allows computers, smartphones, or other devices to connect to the Internet or communicate with one another wirelessly within a particular area. • Wide area network (WAN): a network that spreads to a large area, such as beyond buildings and cities. WAN is an internetwork that is set up by connecting a number of local area net- works through routers. The Internet is an example of a WAN.

Abbreviations and acronyms used without definitions in TRB publications: A4A Airlines for America AAAE American Association of Airport Executives AASHO American Association of State Highway Officials AASHTO American Association of State Highway and Transportation Officials ACI–NA Airports Council International–North America ACRP Airport Cooperative Research Program ADA Americans with Disabilities Act APTA American Public Transportation Association ASCE American Society of Civil Engineers ASME American Society of Mechanical Engineers ASTM American Society for Testing and Materials ATA American Trucking Associations CTAA Community Transportation Association of America CTBSSP Commercial Truck and Bus Safety Synthesis Program DHS Department of Homeland Security DOE Department of Energy EPA Environmental Protection Agency FAA Federal Aviation Administration FAST Fixing America’s Surface Transportation Act (2015) FHWA Federal Highway Administration FMCSA Federal Motor Carrier Safety Administration FRA Federal Railroad Administration FTA Federal Transit Administration HMCRP Hazardous Materials Cooperative Research Program IEEE Institute of Electrical and Electronics Engineers ISTEA Intermodal Surface Transportation Efficiency Act of 1991 ITE Institute of Transportation Engineers MAP-21 Moving Ahead for Progress in the 21st Century Act (2012) NASA National Aeronautics and Space Administration NASAO National Association of State Aviation Officials NCFRP National Cooperative Freight Research Program NCHRP National Cooperative Highway Research Program NHTSA National Highway Traffic Safety Administration NTSB National Transportation Safety Board PHMSA Pipeline and Hazardous Materials Safety Administration RITA Research and Innovative Technology Administration SAE Society of Automotive Engineers SAFETEA-LU Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (2005) TCRP Transit Cooperative Research Program TDC Transit Development Corporation TEA-21 Transportation Equity Act for the 21st Century (1998) TRB Transportation Research Board TSA Transportation Security Administration U.S.DOT United States Department of Transportation

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