1	Data Capture and Management (DCM) Program Overview Dale Thompson, ITS JPO Gene McHale, FHWA Mobility Workshop 2012 National Harbor, MD May 24, 2012
2	Dale Thompson ITS-JPO
3	Overview Introduction Key Concepts Data Capture Program Roadmap Current Projects and Products Research Data Exchange (RDE) Test Data Sets RDE demonstration Critical Issues for DCM RDE Next Steps Stakeholder Q&A
4	"Vision" Vision Active acquisition and systematic provision of integrated, multi-source data to enhance current operational practices and transform future surface transportation systems management Objectives Enable systematic data capture from connected vehicles (automobiles, transit, trucks), mobile devices, and infrastructure Develop data environments that enable integration of data from multiple sources for use in transportation management and performance measurement Reduce costs of data management and eliminate technical and institutional barriers to the capture, management, and sharing of data Determine required infrastructure for transformative applications implementation, along with associated costs and benefits Program Partners ITS JPO, FTA, FHWA R&D, FHWA Office of Operations BTS, FMCSA
5
6
7	Data Environment Evolution
8	Data Environment Evolution
9	Elements of Data Capture and Management
10	Elements of Data Capture and Management Meta data: Provision of well-documented data environment
11	Elements of Data Capture and Management Meta data: Provision of well-documented data environment Virtual warehousing: Supports access to data environment and forum for collaboration
12	Elements of Data Capture and Management Meta data: Provision of well-documented data environment Virtual warehousing: Supports access to data environment and forum for collaboration History/context: Objectives of data assembly
13	Elements of Data Capture and Management Meta data: Provision of well-documented data environment Virtual warehousing: Supports access to data environment and forum for collaboration History/context: Objectives of data assembly Governance: Rules under which data environment can be accessed and procedures for resolving disputes
14	Elements of Data Capture and Management Meta data: Provision of well-documented data environment Virtual warehousing: Supports access to data environment and forum for collaboration History/context: Objectives of data assembly Governance: Rules under which data environment can be accessed and procedures for resolving disputes
15	Elements of Data Capture and Management Meta data: Provision of well-documented data environment Virtual warehousing: Supports access to data environment and forum for collaboration History/context: Objectives of data assembly Governance: Rules under which data environment can be accessed and procedures for resolving disputes
16	Each Data Environment Supports Multiple Apps Overlapping data needs and synergy between application concepts
17	Federated Data Environments
18
19	Innovations Scan Project Assessed industry best practices in data capture and management methods and technologies that are applicable to the DCM Program Identified four "most promising" emerging concepts and technologies USDOT Lead: Mohammed Yousuf (FHWA R&D) Contractor: SAIC/Delcan/University of Virginia (PI: Dick Mudge) Study Period: 9/22/10 - 11/30/11
20	Data Capture Challenges
21	Dynamic Interrogative Data Capture (DIDC) Each device (in a vehicle, on the infrastructure, or on a person) can set and reset message priorities to different data elements Each device can intelligently and dynamically decide on data aggregation levels and transmission frequencies, based on its own state (local conditions) as well as the state of the network (global conditions) Each device can query other devices in its vicinity, depending on its data needs, and request certain data aggregation levels
22	Crowdsourcing
23	Cloud Computing "Model for enabling ubiquitous, convenient, on‐demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction" (Source: National Institute of Standards and Technology (NIST))
24	Data Federation Form of data virtualization where data from multiple, heterogeneous, autonomous data sources are made accessible to data consumers as if it is contained in one single relational database, by using on-demand data integration
25	Gene McHale FHWA Operations R&D
26	Prototype Data Environment: Utilization and Insights Five registered research projects using PDE data U. of Washington: Adaptive Vehicle Routing on Arterial Networks U. of Virginia: Traffic Signal Control and Performance Measures PATH: Advanced Traffic Signal Algorithms Virginia Tech: Traffic Responsive Signal Control U. of Washington: Tracking Transit Vehicles in King County 2,721 total visits, 1132 unique visitors, 60 countries, 138 registered users Most popular features are home page and data download page Steady monthly utilization, number of unique visitors continues to rise Insights from the Prototype Data Environment Revealed needs for a complex system of multiple data environments Research Data Exchange (RDE) Concept of Operations enabled internal collaboration, engaged stakeholders Focal point for discussions on Open Data concepts
27	Evolution from Independent Data Sets to Research Data Exchange The Research Data Exchange (RDE) is the connected system of data environments we envision to support application research and development The RDE will not be a single, centralized repository but rather a system of systems linking multiple data management systems some of which will be maintained and controlled outside of the USDOT, through a common web-based Data Portal Some data will be archived at USDOT within the RDE, other data will be archived outside of USDOT and federated with the RDE
28	Data Capture and Management: The Road to Deployment The Research Data Exchange supports research related to applications enabled by new forms of data The RDE does not itself represent a prototype operational data environment, however, research supported by the RDE Identifies and characterizes the minimum data set and data characteristics required to realize each application Reveals implications for related standards, IPR, data ownership, and privacy issues Provides lessons learned in terms of balancing data federation and centralization for operational deployments Well-formed and described minimum data sets and characteristics can be used to guide the integration of applications into legacy data systems In Phase 3 our goal is to demonstrate how new forms of data from wirelessly connected vehicles and data can be incorporated into deployed systems supporting new applications
29	RDE Data: Current and Near-Term Contents
30	Potential Research Supported by Near-Term RDE Data Sets Primary goal: support development of mobility applications The DCM program will investigate the following topics supported by near-term RDE data sets: What are the key differences between current probe data and BSM connected vehicle probe data? How can probe data be used in conjunction with other forms of data to enable new transformative applications? Can multi-modal data be fused and utilized for traveler information and systems management?
31	Potential Research Topics Using RDE Content Quantifying variability of intersection peak hour demands Estimating route travel time reliability on arterials Analyzing travel time reliability Freight, transit, and integrated multi-modal system perspectives Quantifying variability of freeway traffic demands Estimating accidents and/or weather impacts on a system Verifying accuracy of congestion predictions and accuracy of travel recommendations based on those predictions Determining effectiveness of driver information using CMS messages in changing traffic conditions Predicting bus delays as a function of prevailing traffic conditions Analyzing travel times (delays) and type of incident and incident response for better real-time incident management and travel advisory.
32	RDE Demonstration RDE Website: www.its-rde.com
33	Critical Issues for DCM What role can standards play in the RDE? How can we manage data privacy? How can we deal with intellectual property rights and data ownership issues? How do we manage the RDE? What are the most valuable data? What level of quality is required for the data in the RDE? Will other researchers use the RDE? What is the value of capturing and making data available? What are the most effective ways to provide BSM data to support research?
34	Next Steps of RDE Prototype Research Data Exchange is live Please visit the RDE at: www.its-rde.com Stakeholder Engagement Meeting – Summer 2012 RDE Update 1 – Fall 2012 Integration with Mobility Applications – TBD
35	Questions?