TRB Workshop 174: What Are the Decision Support Subsystem Requirements for the Next Generation Traffic Management Systems and Centers (TMCs)?

Transcription

1 TRB Workshop 174: What Are the Decision Support Subsystem Requirements for the Next Generation Traffic Management Systems and Centers (TMCs)? Purpose of the workshop: The next generation of traffic management systems and centers (TMCs) are expected to process large amounts of data, make automated decisions in real-time, and share information with other systems and service providers. These innovations will enhance the safety and efficiency of freeways and their associated roadways. This workshop will explore the innovative technologies and successful practices needed for subsystems to promote advanced functions and improve the performance of legacy systems and TMCs. This workshop will also coordinate research to help agencies integrate functional subsystems into their operations and actualize next generation of traffic management practices. Purpose of this document: The following pages provide a cursory outline and example list of some current practices, gaps in practice, research and technology transfer needs, and available guidance and resources. Although hardly exhaustive, participants can nonetheless use these prompts and examples to guide their brainstorming and prioritizing of discussion topics in Session 2: What Research is Needed to Advance Using Real-time Decision Support Subsystems for Traffic Management Systems and Centers (TMCs)? Potential Topics for Discussion: Below is a suggested list of topic areas for Session 2 discussion: 1. DSS Requirements Unique for Traffic Signal Systems 2. DSS Requirements Unique for TMCs or Different Systems 3. DSS Requirements for Non-Traffic Functions/Services Systems Support 4. Methods, Design & Architecture Options Considerations for DSS 5. Storing and Using Archived and Real- Time Data 6. Analytical Capabilities and Requirements for DSS 7. Requirements for Adding Prediction Capabilities to DSS 8. Computing Requirements and Platform Considerations for DSS 9. Software and Integration Considerations for DSS 10. Monitoring and Tracking Performance of DSS 11. DSS Capabilities that Allow Agencies to Modify DSS 12. What Technologies Can DSS use (e.g., AI, Data Fusion, Machine Learning) 13. What Resources Are Needed to Use, Monitor or Manage DSS 14. Social Media and Crowdsourcing 15. Other (What is missing?) 1

2 1) DSS Requirements Unique for Traffic Signal Systems Description:, Traffic signal systems are a natural platform for next generation DSS functionalities. Traffic signal systems are deployed to regulate highway bound traffic These signal systems affect throughput of signalized arterials Integrated transit and/or freight signal priority Adaptive signal timing across regional corridors Data sharing and well-defined performance metrics among agencies Macro-level (regional, mega-regional) DSS wide capability System of systems, AI, & machine learning Interoperable data sharing partnerships among TNCs, MPOs, Regional Transit, Municipalities, State DOTs & affiliated TMCs JPO s ITS Benefits website FDOT s May 2017 Manual on Performance of Traffic Signal Systems: Assessment of Operations and Maintenance Current projects include: ICM in Dallas & San Diego 2) DSS Requirements Unique for TMCs or Different Systems Description: Decision support subsystems are used widely throughout different sectors, such as in health care and public safety. Due to an influx in data and capabilities, these DSSs have the ability to interface with TMCs. It is essential in adapting these systems for transportation, specifically for TMCs, that all requirements are identified to ensure all needs of a TMC are met. VDOT active traffic management (ATM) system with rush-hour direction dynamic lane pricing on I-66 near Virginia/D.C. Lane management Integration of historical data from regional agencies, such as MPOs and DOT s Partnerships with TNC decision support systems Data standardization Formal data sharing agreements 2

3 3) DSS Requirements for Non-Traffic Functions/Services Systems Support Description: With growing amounts of available data and evolving TMC roles, there are specific requirements needed for decision support systems in non-traffic functions. These requirements should be fully explored in order to ensure DSSs fulfill the needs of a next gen TMC. National ITS Reference Architecture Environmental and roadway weather information systems monitoring Robust asset management subsystems Integration of remote sensing technologies 4) Methods, Design & Architecture Options Considerations for DSS Description: Expanding the pool of TMC resources raises questions about architecture. A DSS requires additional data flow to a TMC. Methods, design, and architecture need to be fully understood to ensure that additions do not negatively impact or strain the system. National ITS Reference Architecture Exploring real-time data partnerships with TNCs Artificial intelligence and machine learning to situationally prioritize data flow and processing 3

4 5) Storing and Using Archived and Real-Time Data Description: TMCs must store and incorporate archived and real-time data into their operations. There are multiple approaches to storing, disseminating, and using transportation data many of which have been explored through projects over the past 10 years; however, due to the resources required, TMCs have been limited in their ability to adopt the relevant subsystems. The USDOT currently has four projects exploring different storage approaches and ways to disseminate data for usage: 1. Research Data Exchange (RDE), 2. Operational Data Environment (ODE), 3. Secure Data Commons (SDC), and 4. Situation Data Clearinghouse/Situation Data Warehouse (SDC/SDW) Sharing what TMCs are doing with data in a meaningful way that can be repeated or scaled to fit other TMCs The capacity to use data in real-time and not aggregate it to the 5-15 minute bundle of data analysis Standardized ways to collect specific types of data, that would be useful to share with other TMCs (e.g., work zone information, intersection status/classifications, incident information) User feedback within public private data sharing partnerships (e.g., there is no outlet for communicating information back to partners like Waze or INRIX) Standardized ways to analyze data to provide consistent performance measures or tracking within regions Data quality/vetting the quality of data TMCs are using Usage of software and data analysis tools Scales or a matrix of data adoption (e.g., things you can do easily without impacting a TMC too much, but once you hit (x) point you need to upgrade these things) Shared experiences and data analysis techniques USDOT Connected Data Systems Program has GitHub pages for four different approaches to data storage and distribution FHWA 2014 report on the Guidelines for Virtual Transportation Management Center Development 4

5 6) Analytical Capabilities and Requirements for DSS Description: The ability to add analytics to decision support systems allows TMCs to better monitor and manage their networks. While these capabilities bring a wide range of additional value to TMCs, there are also requirements on the TMC, such as platform and policy considerations. 7) Requirements for Adding Prediction Capabilities to DSS Description: The ability to use predictive analytics in decision support systems allows TMCs to greatly streamline various processes and mitigate or resolve incidents faster, ensuring better network capacity. However, integrating these analytics with TMCs requires additional resources, such as personnel considerations, computing and platform needs, and changes in policies. 8) Computing Requirements and Platform Considerations for DSS Description: Additional flows of data into a TMC and the use of DSSs demand more robust platforms and computing power. It is important to continue discussions on these topics to advance DSS adoption. 9) Software and Integration Considerations for DSS Description: Due to the large number of legacy systems, considerations should be discussed for software and integration of DSSs. As more TMCs begin to adopt DSSs, there will be best practices and lessons learned that will make more widespread adoption from legacy systems less resource intensive. 5

6 10) Monitoring and Tracking Performance of DSS Description: In order to ensure decision support systems fully meet the needs of TMCs and are providing the added benefit they are predicted to have, there must be ways to monitor and track their performance. Standardized ways to monitor and track DSS performance will also allow TMCS to compare and contrast updates or various subsystems and allow for shared best practices. 11) DSS Capabilities that Allow Agencies to Modify DSS Description: Decision support systems allow for a variety of support to TMCs and vastly fast track the monitoring and managing of a transportation network. However, these systems are often expensive and do not/ are not able to predict future technologies and additional needs on the system. The ability to allow agencies to modify these DSS systems is critical in order to make investment decisions in a system that will be flexible enough to meet the immediate and future needs of individual TMCs. 12) What Technologies Can DSS use (e.g., AI, Data Fusion, Machine Learning)? Description: Abundant data availability and new technologies are necessary to manage the information coming in at a rate faster than TMCs are currently able. As new technologies are developed for transportation, TMCs and TMC operators should discuss how these technologies can impact performance as well as the impacts of adding these new technologies into existing systems. New technologies being explored in transportation are: artificial intelligence, data fusion techniques, and machine learning. Each of these has the ability to widely evolve the state-of-the-practice for TMCs; however, each comes with its own requirements on the system as a whole that should be researched and debated to determine best practices in implementing these techniques into existing systems. 6

7 13) What Resources Are Needed to Use, Monitor, or Manage DSS? Description: In an age where data and technology are becoming increasingly available and necessary to use in decision support systems, the resources required to monitor and manage these systems are increasingly more abundant and complex. 14) Social Media and Crowdsourcing Description: Although the use of social media and crowdsourcing for decision making by transportation agencies has been around for many years it is still largely experimental in practice. Many agencies have not yet been able to fully realize and utilize the potential of social media and crowdsourcing. However, the ability to gather real-time or near real-time information on incidents or the user experience can be incredibly helpful to a TMC. The use of Waze in TMCs to aid in early detection of incidents The use of Facebook, Twitter, Instagram in TMCs to aid in early detection of incidents and highlight system wide problems, as experienced by users Gamification as a way to influence traveler behavior Arizona transportation agency smartphone app for real-time updates Quality of information coming in Best practices on using and integrating various techniques into legacy systems How trustful is crowdsourced information? How much should it be relied on? Advanced methods in utilizing social media, such as machine learning and AI Chromaroma, the UK based gamification application use case MassDOT, KYTC, and other agencies using Waze The USDOT Secure Data Commons as a place for best practices on using Waze 15) Other (What is missing?) In this section we invite you to propose any topics that may be missing from the above list. 7

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