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1 1 Introduction This document details the research conducted for National Cooperative Highway Research Program (NCHRP) project 08-116: Framework for Managing Data from Emerging Transportation Technologies to Support Decision-Making. This report includes findings from a literature review, surveys and interviews with state and local transportation agencies, emerging technology pilot project documentation reviews, an assessment of data management practices by transportation agencies, and a stakeholder workshop. The primary purpose of this report is to document the NCHRP 08-116 research efforts. Another deliverable of the research, under separate cover, provides guidance, tools, and a roadmap for managing data from emerging technologies. The steps outlined in this companion document are designed to walk an agency through the process of developing the knowledge, environment, projects and buy-in to move incrementally and iteratively from a traditional data management approach to establishing data management policies, procedures, technologies, and practices that fully meet the modern data management needs of an agency. 1.1 Research Objectives The objectives of the NCHRP 08-116 research included the following: â¡ Develop a framework for identifying, collecting, aggregating, analyzing, and disseminating data from emerging public and private transportation technologies. â¡ Address within the framework data from connected and automated vehicle deployments as well as other data linked to new mobility initiatives such as smart city programs. â¡ Outline a process for using this framework to help decision-makers incorporate data from emerging technologies into transportation planning and policy. 1.2 Background Over the last 3 decades, the introduction and development of intelligent transportation systems (ITS) technologies have helped to advance transportation safety and mobility and enhance American productivity. ITS encompasses a broad range of wireless and traditional communications-based information and electronic technologies. Some of the most prominent ITS technologies include advanced traffic management systems, electronic toll collection, ramp meters, red light cameras, traffic signal coordination, transit signal priority, and traveler information systems. More recently, further developments in communications and technology have led to the development of more advanced infrastructure and vehicle capabilities as well as mobility service offerings. These developments have led to a variety of emerging transportation technologies including connected vehicles (CVs), automated vehicles (AVs), on-demand and shared mobility services, accessible transportation technologies, and new mobility initiatives such as smart cities and communities. Each of these is described below in more detail.
2 Connected Vehicles â Connected vehicles use vehicle-to-vehicle (V2V) and vehicle-to- infrastructure (V2I) communications to provide connectivity that enables various safety, mobility, and environmental applications. Connected vehicle technologies allow vehicles to send and receive information about their movements in the network, allowing operators to provide more responsive and efficient mobility solutions in real-time and in the long term. Data derived from connected vehicles could provide insights to transportation operators helping to better understand demand and assist in predicting and responding to vehicle movements. Automated Vehicles â Automated transportation offers possibilities for enhancing safety, mobility, accessibility, equity, and the environment. Examples of automated transportation include automated driver assistance applications and low-speed autonomous shuttles. The Society of Automotive Engineers (SAE) has defined six levels of automation, including: â¡ Level 0 (No Automation): Zero autonomy; the driver performs all driving tasks. â¡ Level 1 (Driver Assistance): Vehicle is controlled by the driver, but some driving assist features may be included in the vehicle design. â¡ Level 2 (Partial Automation): Vehicle has combined automated functions, like acceleration and steering, but the driver must remain engaged with the driving task and monitor the environment at all times. â¡ Level 3 (Conditional Automation): Driver is a necessity but is not required to monitor the environment. The driver must be ready to take control of the vehicle at all times with notice. â¡ Level 4 (High Automation): The vehicle is capable of performing all driving functions under certain conditions. The driver may have the option to control the vehicle. â¡ Level 5 (Full Automation): The vehicle is capable of performing all driving functions under all conditions. The driver may have the option to control the vehicle. On-Demand and Shared Mobility Services âThe sharing economy, adoption of smartphone applications, and new transportation services are providing people with more options, helping to overcome barriers to the use of non-driving forms of transportation, and shifting individualsâ travel choices. Shared mobility services include modes shared among users, including public transit, taxis, carsharing, ridesharing, ride-sourcing, micromobility services such as bike-sharing and scooter-sharing, on-demand shuttle services, and commercial delivery vehicles providing flexible goods movement. Accessible Transportation Technologies â Accessible transportation technologies are transformative applications to improve mobility options for all travelers, particularly those with disabilities. With nearly 20 percent of the U.S. population comprising individuals with disabilities, and other demographic trends such as the increasing number of older Americans, these technologies are critical to expand innovative travel options. Accessible transportation research focuses on emerging technologies and creative service models that remove barriers to transportation for people with visual, hearing, cognitive, and mobility disabilities. Smart City Technologies â A smart city uses information and communications technology (ICT) to enhance its livability, workability, and sustainability. Smart cities contain and use a collective intelligent infrastructure that allows sensors, other devices, and existing systems to collect and report real-time data to inform everyday transportation-related operations
3 and performance and trends of a city. Examples of data include traffic, pedestrian, bicyclist, and environmental data and other information available throughout the city. These data are communicated to allow city operators to understand and analyze how the city is operating in a holistic manner and how the operation of facilities, systems, services, and information generated for the public can be enhanced. To demonstrate and build on these emerging technologies, a wide range of institutions, both public and private, have initiated and invested in major pilot programs. In some instances, these efforts are supported by the U.S. Department of Transportation (U.S. DOT) through several federal initiatives including the CV Pilot Deployment Program, the Smart City Challenge, and Federal Highway Administrationâs (FHWA) Advanced Transportation and Congestion Management Technologies Deployment (ATCMTD) program. As these efforts continue to expand, the amount of data surrounding the application of emerging technologies is also expanding, producing âbig dataâ at magnitudes not previously seen. While a few agencies have found some success with individual projects, most are uncertain on how to handle this level of big data at an organizational level. This research focuses on establishing the current state of practice in data management within the big data industry and the transportation industry and demonstrating the gaps between the two. Another product of the research, published under separate cover, provides guidance and a roadmap for transportation agencies to begin to bridge these gaps. 1.3 Organization of Report This NCHRP 08-116 research report is organized as follows: â¡ Chapter 2. What is Data Management? â This chapter provides an overview of data management, both in the traditional and big data senses. A definition of data management is offered, and the traditional data management lifecycle is introduced. A brief overview of various data management knowledge areas is provided. The chapter concludes with several assertions as to why transportation agencies need to be concerned with readying their organizations for big data. â¡ Chapter 3. Big Data Management State of Practice â This chapter synthesizes the findings from a review of the state of practice in the management of big data. This synthesis is provided first by presenting information and recommendations from information technology (IT) and data science experts on managing big data throughout its full lifecycle, followed by a set of foundational principles relevant to the collection, storage, use, and sharing of big data, including that from emerging transportation technologies. â¡ Chapter 4. State of Practice in Data Management for Transportation Agencies â This chapter documents the research efforts into the state of practice of data management in transportation agencies. The chapter includes an overview of the research approach, followed by the findings from each step of the research process. These research results are then distilled into a set of transportation agency challenges and needs that reflect the current state of practice. â¡ Chapter 5. Research Outputs â This chapter lists and briefly describes all other documentation that was created as part of the research effort, including a guidebook, briefing slides, executive summary, and implementation technical memorandum. This chapter also includes recommendations for future research. â¡ Bibliography and Appendices â Included here are detailed findings from agency interviews, project documentation reviews, and survey and interview questions.