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

Considerations of Current and Emerging Transportation Management Center Data

Chapter 1. Introduction

Purpose

This graphic contains five boxes: Box 1 represents instant awareness of Controlled Vehicle data. Box 2 represents signal phase and timing (SPaT) and WiFi probes on traffic signal towers and how they collect information to get travel times, queuing, turning, and origin-destination data. Box 3 represents automated decision support systems that allow advanced transportation management systems to synthesize, predict, and act on data received. Box 4 represents control, response and influence choices made available to vehicles. Box 5 represents measuring of data is archived, retrieved, reported upon as return on investment, and performance measures. Copyright 2018 University of Maryland Center for Advanced Transportation Technology Laboratory.
Figure 1. Graphic. How agency traffic management centers integrate private sector data.
Source: University of Maryland, Center for Advanced Transportation Technology Laboratory.

This report documents a Transportation Management Center Pooled Fund Study research project and provides a detailed reference on the concepts, business models, methods, processes, techniques, and other issues related to realtime traffic data collection and dissemination. The audience for this report is transportation management center (TMC) managers and their public sector partners. The project identified, researched, and synthesized an assessment of the following data collection and dissemination factors:

  • Private sector business models.
  • Public-private partnerships and agreements.
  • Private sector data that can supplement traffic data collected by public agencies.
  • Public agency data with a focus on value and models when sharing with the private sector.
  • Emerging data sources such as crowdsourcing and connected vehicles (CVs).

Background

TMCs constantly search for new and innovative data sources that can improve guidance for operations decisions, provide better predictive capabilities, and enhance the safety and mobility of travelers. Figure 1 shows one of many approaches agencies could use in the future to integrate new, private sector data and agency-deployed sensors to enhance operations. The process depicted describes the following activities:

  • Data acquisition, shown in boxes 1 and 2, incorporates the collection of CV data from third parties followed by Wi-Fi re-identification, signal phase and timing (SPaT), and other emerging data products (see chapter 2).
  • Data could then be integrated into an advanced transportation management system (ATMS), decision support system (DSS), etc., which interprets and synthesizes the data to predict future conditions, provides greater insights, etc., as shown in box 3.
  • The resulting system responses could include traffic control, response to changing conditions or incidents, feedback loops, and other actions that influence driver choices on routing and departure times, as represented by box 4.
  • Lastly, a data archive for measuring outcomes, reporting, and process improvement is shown in box 5.

The diagram, while convincing, glosses over many of the critical details. The workflow seems simple enough, but many factors can derail efforts to procure and utilize new data or technologies, all of which can heavily influence operations program success, agency budgets, and the public.

This report looks at some basic aspects of using emerging data from third parties—understanding what is available, how it is collected, the business models used by the companies that sell it, acceptable use of the data, and possible data use cases. Details of these aspects are rarely reported in the public domain, making location of reliable data difficult for agencies. This limits agencies' ability to define successful operations strategies and make sound investment decisions.

Sources of Information and Methodology

The extensive literature review and the gathering of key information for this study came largely from in-person or phone interviews with private sector data providers, systems integrators, and the staff of departments of transportation, universities, and non-profit coalitions. The very nature of this report has the potential to expose key business and agency practices that are both proprietary and sensitive. Most of the private sector and agency interviewees declined the offer of attribution and requested that specific details concerning the exact formats of their data remain confidential.

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