Office of Operations
21st Century Operations Using 21st Century Technologies
Table of Contents

Enhancing Active Transportation and Demand Management (ATDM) with Advanced and Emerging Technologies and Data Sources

Executive Summary

Per the Federal Highway Administration (FHWA) Office of Operations, active transportation and demand management (ATDM) is defined as:

"…the dynamic management, control, and influence of travel demand, traffic demand, and traffic flow on transportation facilities. Through the use of available tools and assets, traffic flow is managed, and traveler behavior is influenced in real time to achieve operational objectives, such as preventing or delaying breakdown conditions, improving safety, reducing emissions, or maximizing system efficiency." (5)

ATDM solutions aim to leverage data sources and technologies to manage capacity and demand on facilities to reduce congestion and delay; respond to incidents and provide traveler information based on real-time data; and balance resources across active traffic management (ATM), active demand management (ADM), and active parking management (APM) for optimal solutions.

This document informs agencies of the technology and data sources available to modify and enhance their ATDM solutions from static/responsive management to truly proactive management. To identify emerging technologies and data sources, a detailed literature review was conducted. In addition, interviews with public agencies and private companies that implement ATDM solutions were conducted to determine what technologies and data sources are in use today. Listed below are the emerging technologies and data sources that are relative to the next-generation ATDM concepts and solutions.

Sample list of emerging technologies:

  • Data technologies:
    • Light detection and ranging (LiDAR).
    • Laser.
    • Automatic vehicle location (AVL).
    • Global positioning system (GPS)/phone-based probe data.
    • Crowdsourced data.
    • Internet of things (IoT).
    • Cloud computing.
    • Big data technologies.
    • Block chain.
    • Data analytics.
    • Commercial transactional data.
  • Vehicle technologies:
    • Connected vehicle.
    • Autonomous vehicle (AV).
  • Sensor technologies:
    • Video analytics sensors.
    • Air quality monitoring sensors.
    • Smart lighting.
    • Gunshot detectors.
    • Bluetooth/WiFi sensors.
  • Decision support system technologies:
    • Artificial intelligence (AI).
    • Machine learning (ML).
    • Deep learning (DL).
    • Cloud computing.
    • Edge computing.
    • Voice drive assistants.
    • Data analytics.

Sample list of emerging data sources:

  • Connected travelers data:
    • Crowd sourced data (e.g. speed, incident, event, and congestion data).
    • Connected citizen applications.
    • Crowd sourced video.
  • Connected vehicle data:
    • Basic safety messages (BSM).
    • Probe data messages.
    • Others.
  • Connected infrastructure data:
    • Roadside dedicated short range communication (DSRC)/BSM collection.
    • High-definition (HD) signal data.
    • ATM and intelligent transportation system (ITS) devices, e.g., signals, signs, cameras, road weather information system (RWIS), etc.
    • IoT.
  • Map technologies:
    • Crowdsourced mapping data.
    • High resolution map data (LiDAR or similar) and other asset management systems.
    • Real-time trajectory data.
  • Other data sources:
    • Real-time turning movement data.
    • Bluetooth re-identification.
    • Mobile sensors.
    • HD maps.

The literature search and interviews also provided information on what ATDM concepts agencies have planned for the future. ATDM implementations go through the same steps:

  • Monitor the system.
  • Assess system performance.
  • Evaluate and recommend dynamic actions.
  • Implement dynamic actions.

By incorporating the emerging technologies and data sources at each step, ATDM implementation will be improved. Use cases provide examples of how ATDM is accomplished now and what can be enhanced using new technologies and data sources.

Planning for ATDM implementation is a complex process and involves advanced planning. This report provides information for planning an ATDM implementation:

  • Organizational Capability - discusses the organizational capability concepts that support successful ATDM operations.
  • Planning for Modified ATDM Operations - discusses specific efforts that an agency undertakes to plan for operations, including scenario planning and use of data.
  • Setting Objectives and Performance Measures - describes the importance of identifying objectives and performance measures for operations.
  • Analysis, Modeling, and Simulation - discusses the types of analyses that are useful in assessing the feasibility and potential impacts of ATDM solutions on specific corridors.
  • Programming and Budgeting - describes strategies for programming and budgeting for ATDM solutions on a regional basis.

For the planning to be successful, design and deployment elements need to be considered. Data management, system platforms, and infrastructure need to be considered. Technology testing and public outreach are also important considerations. Operations and maintenance (O&M) are also an integral component in planning for ATDM enhancements. It is necessary to consider such items as cybersecurity, performance monitoring, costs, and future proofing.

This document concludes with several case studies that exemplify the use of ATDM solutions in conjunction with emerging technologies and data sources.

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