1. IntelliDriveSM Connectivity − Mobility
ITS America Webconference
USDOT Overview for the Private Sector Transportation Data Community
Ben McKeever, Gene McHale, Bob Rupert
FHWA
13 September 2010
IntelliDrive is a concept that leverages technology to promote connectivity among vehicles, roadway infrastructure, and wireless devices.
Connectivity will create a data-rich environment, providing substantial opportunities to make surface transportation safer, smarter, and more environmentally friendly.
IntelliDrive is a service mark of the U.S. Department of Transportation.

2. What is IntelliDriveSM?
IntelliDriveSM is a suite of technologies and applications that use wireless communications to provide connectivity:
Among vehicles of all types
Between vehicles and roadway infrastructure
Among vehicles, infrastructure and wireless consumer devices
Vehicles
Wireless
Devices
Infrastructure
Drivers
The IntelliDrive system will deliver networked connectivity that will truly transform travel. IntelliDrive applications provide connectivity:
Among vehicles to enable crash prevention
Between vehicles and the Infrastructure to enable safety, mobility and environmental benefits (for example, by maximizing the efficiency of traffic signal phase and timing)
Among vehicles, infrastructure, and wireless devices to provide continuous real-time connectivity to all system users

3. Networked Environment
Transit Signal Priority
Reduce Speed 35 MPH
Weather Application
Fleet Management/ Dynamic Route Guidance
Illustration of the networked environment.
Truck Data
Transit Data

4. Why IntelliDriveSM? Safety Problem 40,000 highway deaths/year
6 million crashes/year
Leading cause of death for ages 4 to 34
Safety Benefits
Dramatically reduced fatalities and injuries through greater situational awareness:
Driver Advisories
Driver Warnings
Vehicle Control
Why do we need IntelliDrive? Because implementation of the IntelliDrive vision represents perhaps the best opportunity to tackle some of the biggest challenges in the surface transportation industry:
Safety Challenges
Mobility Challenges
Environmental Challenges
Safety—Every year, approximately 40,000 people die on U.S. roadways in approximately 6 million crashes. Children and young people are particularly vulnerable. Crashes are the leading cause of death for ages 3 through 34.
IntelliDrive provides the tools to make transformational improvements in safety—to significantly reduce the number of lives lost each year through IntelliDrive crash prevention applications.

5. Why IntelliDriveSM? Mobility Problem 4.2 billion hours of travel delay
$78 billion annual drain on US Economy
Mobility Benefits
Information-rich environment benefits users and operators of all travel modes:
Travelers have real-time information on rerouting or “modal shift” options
System operators have real-time data to enable better system operations for optimal performance
Planners can use data to improve major investment plans
Mobility—U.S. highway users waste 4.2 billion hours a year stuck in traffic – nearly one full work week (or vacation week) for every traveler. The overall drain on US Economy is $78 billion.
IntelliDrive mobility applications will enable system users, system operators, and system managers to:
make more informed travel choices
enable better system operations for optimal performance
improve major investment plans

6. Why IntelliDriveSM? Environmental Problem
2.9 billion gallons of wasted fuel each year
22% CO2 emissions from vehicles
Environmental Benefits
↓Emissions ↓Greenhouse Gases ↓ Particulates:
Reduced stopping/starting at traffic signals which consumes 3-5 times more fuel than constant driving
Nav systems with real time information can reduce fuel consumption by 10.5% over systems without real time traffic data
Environment—The total amount of wasted fuel was 2.9 billion gallons – three weeks' worth of gas for every traveler.
IntelliDrive environmental applications will enable system users and system operators to make choices that reduce the environmental impacts of surface transportation travel.

7. IntelliDrive Program Structure
Applications
Safety
V2V
V2I
Safety Pilot
Mobility
Real Time Data Capture & Management
Dynamic Mobility Applications
Environment
AERIS
Road Weather Applications
Technology
Harmonization of International Standards & Architecture
Human Factors
Systems Engineering
Certification
Test Environments
Title slide: IntelliDrive Program Structure
I’ve removed some items already
Clean up to look better within this template
Not a fan of the double boxes.
Deployment Scenarios
Financing & Investment Models
Operations & Governance
Institutional Issues
Policy

8. Evolution of IntelliDrive Deployment
Original VII Deployment Model
DSRC based for all applications
Infrastructure intensive using new DSRC technology
Vehicle turnover for embedded DSRC technology
Start with V2I (for all application types) and evolve into V2V (safety)
US DOT’s Current Perspective on IntelliDrive Deployment
Non-safety (mobility, environment)
Leverage existing data sources & communications; include DSRC as it becomes available
Support development of key applications for public agencies using current data sources and evolving probe data from IntelliDrive
Safety  DSRC
Aggressively pursue V2V; leverage vehicle capability for V2I spot safety
Can leveraging of nomadic devices & retrofitting accelerate benefits?
Infrastructure requirement for security is still a TBD

9. IntelliDriveSM Mobility
Mobility and Environmental Applications
Real-time Data Capture and Management
Reduce Speed 35 MPH
Weather Application
Transit Signal Priority
Data Environment
Fleet Management/ Dynamic Route Guidance
The mobility program area includes: Real time Data Capture and Management and Dynamic Mobility Applications.
Data from multiple sources and of multiple types (such as location data, transit data, weather data, vehicle status data and infrastructure data) are captured, cleaned and integrated into a data environment. The data is then used by multiple applications:
enhanced weather applications (e.g., real-time weather advisory information or warning systems)
real-time transit signal priority
real-time traveler information systems
environmental applications, such as eco drive
safety alerts, queue warning systems
Real-time Data Capture and Management addresses the capture, cleaning and integration of data in real time. Dynamic Mobility applications addresses the use of data in real time to develop and deploy enhanced or transformative mobility applications.
The materials in the next several slides were drawn from the vision documents. We will need feedback from you on the approach, especially where things differ from the norm. Are there too many risks? We are looking for feedback on the different ways for data capture and management, distribution, and other concepts that are relatively new for a federally funded research program.
Truck Data
Transit Data

10. Real-Time Data Capture and Management
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
We will first talk about Real-time Data Capture and Management.
We need data to build applications. But there is no point in collecting data for the sake of collecting data if we are not going to use it. We need to identify what data is needed by the applications and make that data available. Secondly, we need to identify what applications we can build from the data.
This makes Real time Data Capture and Management a critical, cross-cutting activity that identifies multi-source data for mobility and environmental applications, and identifies ways to make them available systematically for multiple users, so that it doesn’t have to be done six different times for six different applications, when four of the applications might require the same data. Typically, significant amount of resources are spent by a project to collect data. But because there is no plan in preserving or maintaining the data, the data is usually lost. Even if it is archived, after a few years only a handful people remember why the data was collected in the first place, and fewer still might remember the structure. It is a lot less expensive to do it right once, document it well, preserve it, and share it with others who may want to use the data.
In this program we will try to address this problem.

11. Creating a Data Environment
well-organized collection of data of specific type and quality
captured and stored at regular intervals from one or more sources
systematically shared in support of one or more applications
Data Capture
Data Environment
Information
Raw Data
What is a data environment and how is that any different from what we have done in the past?
[Figure]: Data is pulled into the environment - the orange globe. Data users can extract information from it to build or support applications. The data environment can have multi-source data, which may be observed, simulated or interpolated data, and all the elements needed to develop an application.
In some cases, the observed data might be flagged as being erroneous. In other cases, high quality observed data might be interpolated to help with the assessment of applications if there isn’t sufficient market penetration of certain technologies.
Application

12. How Do We Structure The Data?
Key Issues in Defining A Data Environment
What Data Do We Keep?
What Data Do We Capture?
How Do We Use The Data?
How Do We Structure The Data?

13. Data Sources and Uses SOURCES USES SOURCES USES TRAVELER VEHICLE
MOBILITY
SAFETY
ENVIR.
TRANSIT
FREIGHT
LIGHT VEHICLE
LOOP
RADAR
OTHER
VEHICLE
INFRASTRUCTURE
LOCATION
DECISIONS
TRAVELER
PERFORMANCE
MEASUREMENT
INFORMATION
VARIABLE
SPEED LIMITS
ECO-
DRIVE
QUEUE
WARNING
Next, we will look at the data sources and data uses. If you cut the orange globe into two, on the left are the sources and on the right are the uses.
Where do these data come from? The program will capture and integrate data from different sources. Infrastructure sources are well documented. A number of data environments integrate data from infrastructures sources.
In addition to looking at the infrastructure as a source, IntelliDrive is also interested in vehicles and travelers as data sources. For vehicles, we are interested in getting data from transit, freight and light vehicles. It is not just the vehicle location and speed that we are interested in, but also other data such as rain sensor status, windshield wiper status, sun sensor status, measured air temperature, exterior light status, etc.
For travelers, we are interested in not just looking at the location of the consumer device but also to systematically acquire the context of the travel, what kind of decisions were made and what the outcome of the trips were. It is this kind of data that the research community has been asking for decades and haven’t been able to get it outside of the infrequently collected surveys.
What are we going to use this data for? Environmental applications, Mobility applications and some of the softer safety applications that was shown in the previous slide such as queue warnings. The hard safety, such as crash avoidance is not part of our program.

14. Data Aggregation and Structure
AREA-WIDE
AGGREGATION
STANDARDS
QUALITY
ACCESS
Next we will look at the data aggregation and data structure issues.
What data do we actually retain? Do we only distribute data that has been aggregated area-wide? Or do we keep all the raw data that we collect? It depends on what we’re trying to do, what applications will make use of the data, who will use the data – researchers, developers looking to improve their algorithms, coming up with new concepts, finding innovative approaches to improve quality and speed of data processing.
On the structure side, we have to look at the Access, IP rights, Standards, Storage, Regulation, Privacy and Quality of data captured. All of those issues will drive how we can build certain environments to meet applications needs. IP
RAW
DATA
PRIVACY
STORAGE
STRUCTURE
REGULATION
STRUCTURE
AGGREGATION

15. Data Environment Evolution
Current State
Potential End State
TRAVELER
TRAVELER
“nearly zero”
“some”
VEHICLE
VEHICLE
“a few”
“nearly all”
Potential Interim States T V I
INFRASTRUCTURE
INFRASTRUCTURE
What kind of data environments do we have now?
We’re not starting from a zero data environment. Our current state is described on the left. We have infrastructure on the freeways and on some major arterials to capture data. A few vehicles act as probes in the system. We also have very infrequent surveys being conducted to find out what people’s context and routes are.
On the right is the potential end state with IntelliDrive. It shows the data we think we will need to develop or support applications. We might deploy infrastructure sensors where needed. At this stage, we don’t know exactly how many. We’ll probably need nearly all vehicles to participate and share information. And we will probably need some travelers to share information with respect to travel context, decisions, and outcomes. What kinds of transformative applications can we imagine that could potentially be supported with such a flow of data into the system?
We can’t leap directly from the current state to the end state. So we need to investigate what states make sense that provide benefits and support the applications of interest. These potential interim states are some of the different data environments we might try build, simulate, test as part of this program.
“some”
“where needed”

16. 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
Capture
GOVERNANCE
HISTORY/CONTEXT
VIRTUAL WAREHOUSING
META-DATA
Data Environment
Information
Raw Data
Next we will look at the elements of data capture and management. The orange globe represents data environment consisting of high quality data.
The value of high-quality data is limited if it is not well documented, if there is no supporting meta data, if data users have to spend time and resources figuring out the content and structure of the data environment. So a key element is the provision of a well documented data environment.
Next we need a mechanism to access the data, to allow data users to collaborate with each other, and to get their questions answered. The intent here is not to build a giant, federal database. We will try to identify the most logical way of supplying data that meets specific needs. One approach is to have individual streams of data maintained by those who capture the data, and then use virtual warehousing techniques to combine data from multiple locations on the fly to serve particular applications or researchers.
Next we need the history or context of data capture. Why was the data collected?
Finally, like all good data environments we need to have a governing structure, the rules of engagement. Who can put data in? Who can use it? What are the rights and responsibilities of the data contributors and the data users? The rules might change from one data environment to another.
Application

17. Projected Outcomes Establish one or more data environments
Broad collaboration supporting data environment utilization
Implementation of data management processes representing best practices
The projected outcome is that in the first phase of the program, we would like to establish one or more data environments so that users can start to utilize the data and collaborate. We need to have the data management processes set up to support multiple users from the beginning.

18. Dynamic Mobility Applications
Vision
Expedite development, testing, commercialization, and deployment of innovative mobility applications:
maximize system productivity
enhance mobility of individuals within the system
Objectives
Create applications using frequently collected and rapidly disseminated multi-source data from connected travelers, vehicles (automobiles, transit, freight) and infrastructure
Develop and assess applications showing potential to improve nature, accuracy, precision and/or speed of dynamic decision making by system managers and system users
Demonstrate promising applications predicted to significantly improve capability of transportation system to provide safe, reliable, and secure movement of goods and people
Next we will examine Dynamic Mobility Applications.
As mentioned previously, we don’t collect data for nothing. We need to do something useful with it, specifically developing applications that maximize system productivity and enhance mobility of individuals within the system. The focus is primarily on public sector applications but we’re not ruling out private sector applications.
The program will leverage multi-source data to develop and assess the applications that show potential to improve dynamic decision making.
We will identify the potential benefits that can be realized by these applications. The applications will be prioritized with the help of stakeholders based on the potential benefits, deployment costs, institutional and technical risks, and stakeholder acceptance.
Only when we have proven in a smaller test environment or a demonstration that it really works, can we move to a deployment phase.

19. Guiding Principles Leverage multi-source data
Develop and test mode-specific and multi-modal applications
Feature open source application research and development
Encourage competitive application commercialization
Prioritize program resources based on expected impact
Enhance analytical capabilities related to mobility applications
Practice long-term technology stewardship
These are some of the guiding principles taken directly from the vision.

20. Leverage Multi-Source Data
VEHICLES
FREIGHT
LIGHT VEHICLE
Leverage high-quality data integrated from mobile and fixed sources to develop multiple applications (mode-specific and multi-modal)
Requires coordination with Real-Time Data Capture and Management program
TRANSIT
INFRASTRUCTURE
TRAVELERS
DATA
ENVIRONMENT a c
We will fully leverage high-quality, integrated, multi-source data to support multiple applications. The Dynamic Mobility Applications program will have to coordinate closely with the Data Capture and Management program to identify promising mobility applications that have similar data needs.
[Figure]: The figure illustrates how data from mobile and fixed sources are integrated and managed in a data environment under the Data Capture and Management program. Data and information from this environment can be systematically extracted and used to support the set of identified applications. These may target specific modes (for example automated safety checks for freight vehicles) or may be multi-modal in nature (e.g., a traveler information service integrating tolling, transit and parking availability or cost). Cross-modal applications refer specifically to system management functions coordinating control between modes and jurisdictions.
MODE-SPECIFIC
APPLICATIONS
(e.g., for freight vehicles)
CROSS-MODAL
APPLICATIONS
(e.g., for system managers) b
MULTI-MODAL
APPLICATIONS
(e.g., for travelers)

21. Multi-Modal Applications Development and Test
Coordinated development of mode-specific and multi-modal applications:
avoid duplication
cost-effective a b c
DATA
ENVIRONMENT
VEHICLES
INFRASTRUCTURE
TRAVELERS
MANAGERS
FLEET OPERATORS
APPLICATIONS
A coordinated approach is needed to develop mode-specific or multi-modal applications. The open source approach will help us to avoid duplication and is definitely more cost effective. For example, if we have an algorithm that gives us the expected time of arrival of a vehicle at an intersection, it might be valuable for signal optimization, transit signal priority, and eco driving applications. There’s no reason to build that particular application or foundational element twice. Let’s build it once and reuse it multiple times.

22. Open Source Research and Development
Research issue is too complex or big for isolated researchers to solve
Promotes highest level of collaboration
Preserves intellectual capital
Serves to engage partners from academia and industry who may not be directly involved in funded applications development and testing
Currently seeking to refine how best to structure open source agreements
maximize collaboration
without reducing innovation or endangering commercialization
USDOT is applying the open source concept to a big initiative for the first time. There have been other ITS programs where this has been examined: QuickZone, NGSIM, TRANSIMS Open Source.
The push for open source here is a realization that no one program has enough resources to solve the problem completely. The open source research and development concept is good for taking on those issues things that are really hard to solve, where collective thinking is needed to solve the problem.
Secondly, the intellectual capital is preserved and available. Typically when research is conducted, the rest of the community benefits from the research only when a publication comes out. Once the project ends, the value starts to decline over time due to lost or poor documentation. But with the open source approach there is the potential to manage and grow intellectual capital.
Finally, the open source concept brings together stakeholders in a way that traditional, proprietary development does not. The open source concept allows multiple researchers and developers to work together in solving complex problems.

23. Encourage competitive application commercialization
APPLICATION DEPLOYMENT LIFE-CYCLE
RESEARCH
DEVELOPMENT
COMMERCIALIZATION
SUPPORT
PRIVATE
SECTOR
FOCUS
PUBLIC
COMPETITIVE
APPLICATION
COOPERATIVE
AND
OPEN
DATA
OPEN SOURCE
TECHNOLOGY
TRANSFER
Research and development efforts cannot become isolated exercises unrelated to user needs or without a clear path to eventual commercialization and broad deployment.
Where is the Dynamic Mobility Applications program putting its money down? It is primarily in the dark green area, where the focus is mostly on R & D using the open source concept.
The focus is not on commercial application development, although we recognize that’s what needs to be done eventually. We have to be able to move the research that we’ve done, and get it into the field. The federal government will not be commercializing or supporting applications; that’s what the private sector does best.
USDOT will structure the program through the open data concept, open source development and tech transfer to clearly and fairly spur competitive application commercialization. It is not a one-way feed. The program will make use of the work done by the private sector to enhance existing applications.

24. Prioritize resources based on expected impact
Develop prototype mobility application that focuses on performance measures:
exploits new or integrated data sources
enhances traditional measures or creates new measures to capture full impact of mobility applications
Prioritization of development and test of candidate applications:
applications must improve system productivity or user mobility
well-defined, quantitative performance measures (multi-modal or mode independent)
applications must have broad stakeholder interest and support
Resources need to be prioritized based on expected impact. But if stakeholders are not interested in an application and no one will use it, there is no point in making significant investments on that application.

25. Enhance analytical capabilities
Develop analytic tools and processes to accurately predict impacts:
assess long-term performance
use real-time prediction to support improved decision making by travelers, system managers and other transportation system stakeholders (e.g., fleet operators)
Employ tools to refine and identify promising applications prior to committing resources for field testing or full demonstration
The program will look at new and enhanced modeling and simulation capabilities. To help ease the burden and cost of testing and evaluation, applications will most likely be tested in a simulation environment before resources are committed to full demonstrations.

26. Practice long-term technology stewardship
Finally, the program will put in place mechanisms for long term stewardship.
For example, if early data environments aren’t fully fledged or have limited amount of data due to low market penetration, only a few applications may be supported. But as the market penetration increases, and with additional data flowing in, the range of applications that are supported by the data environment might increase.
We will be looking at the combination of applications that can be supported at each stage as the program evolves from the current state to the end state.

27. Projected Outcomes
Multiple applications developed leveraging multi-source data
Research spurs commercialization
Applications enable transformational change
If the program works the way we have envisioned it to, there should be multiple stakeholders leveraging one or more data environments to build new applications.
We will have a clear path to commercialization, free from IP entanglements and patents or at least if there are those entanglements they’re clear to anyone who decides to participate.
The eventual deployment of these applications will enable the transformational change that we been talking about as one of the overriding goals of the program.

28. Keys to Success for IntelliDrive Mobility
Facilitate easy, secure access to data environment and enable collaboration in mobility application development
Accumulate and share intellectual capital while respecting IP rights
Coordination with other IntelliDrive program areas and broader ITS programs
Active interaction with broader group of stakeholders outside the federal research and development efforts
Not a one-time engagement, will require ongoing collaboration to:
refine program goals
refine data needs
structure relevant and feasible data environment development efforts
prioritize applications development and testing
These are the keys to success for the overall mobility program.

29. Getting Involved
Provide feedback on program direction, goals, data environment, mobility applications
Respond to upcoming funded requests for research and development of mobility applications
Seek to leverage IntelliDrive data and applications resources in other non-federal or non-IntelliDrive federally funded research projects
Offer new data sets and applications
Actively commercialize mobility applications developed within the IntelliDrive program
Utilize the IntelliDrive Prototype Data Environment
Submit application concepts using the Candidate Applications Template
Stakeholders are invited and encouraged to provide input on the two programs, respond to upcoming funded requests for data collection, and applications research and development, and for commercializing applications developed within the program.

30. Data Capture Prototype Data Environment
Data (and meta-data) from the Michigan IntelliDrive Test Bed
Documented probe data samples from recent tests (POC/NCAR)
Trajectory Conversion Analysis (TCA) program
Simulated 100% market penetration data for the test bed contributed by the University of Michigan Transportation Research Institute (UMTRI)
Forums for researchers to register projects, flag erroneous data, contribute analyses and data views

31. Data Capture and Mngmt Future Activities
Investigations into state-of-the-practice for technologies, institutional and policy, and standards
Broad Agency Announcement (BAA) for Test Data Sets
Acquisition and hosting of IntelliDrive funded data, such as
Near term performance measures demo
Safety Pilot
Mobility Application Demos
IntelliDrive Test Bed
Development of data environments to support Mobility applications

32. Candidate Mobility Applications Concepts
Goal
Identify, with help of stakeholders, collection of applications for development and testing in Phase 2 of Program
Approach
Solicit ideas for transformative applications that improve decision making by system managers and users
Template available for submission of ideas at:
Initial request closed on 31 July; second call issued (closes 15 October)
46 submittals with 49 application ideas (as of 31 August)
Combine suggested applications of interest to condensed list to avoid duplication
Prioritize condensed list for future development and testing

33. Mobility Applications Template
Summary of Submittals to Date (46 received)
Contributor types:
Academia: 7
State/local government: 14
Private sector: 10
Federal government: 5
Non-government organization: 4
VII Day One: 6
Types of Applications:
Traffic Control: 4
Traveler Information: 11
Active Traffic Management: 11
Transit-related: 10
Freight-related: 7
Rural: 4
Pedestrians/Bicycles: 2
Weather-related: 4
Other / unrelated: 9
Total of 49 ideas included in the 46 submittals.
Summary report available at:

34. Submitting New Concepts
Got a transformative idea that utilizes IntelliDrive data?
Fill out template online:
Or, fill out a template off-line and send it via
Template requires high-level description of idea
Problem being addressed
Transformative benefits
How the idea makes use of IntelliDrive data
Deadline for getting your idea in: 15 October 2010
It is not too late to submit new ideas.

35. Near-Term Performance Measurement Demonstration
Goal:
In CY 2011, potentially initiate one or more field demonstrations
Utilize IntelliDrive data from probe vehicles, mobile devices and infrastructure sensors
Measure and characterize overall transportation system productivity (not just facility or mode) and individual mobility (across modes)
Proposed Elements
Multi-source data capture and integration (vehicles, rail, freight, travelers) to support innovation in performance measurement
Critical mass demonstration in a complex (multi-modal) system
Share data from demonstration through Data Capture program
Summary of Questions posed in RFI:
Nature of procurement – contract with private sector with public partners, or agreement with public agency with private participation.
Time frame – 6 months to develop in CY2011 with 18-month period of performance (i.e., operations, results, analysis, report)
Performance metrics the right ones?
How much can performance measurement/management (decision-support) be demonstrated with today’s data sources?
Single demonstration or multiple?
Open access to data.
Open source applications. 35

36. http://www.intellidrive.org/ For More Information…
Finally, partners can learn more about the USDOT’s IntelliDrive programs and activities through the IntelliDrive website at 36

37. Contact Information
Ben McKeever, FHWA
Gene McHale, FHWA
Bob Rupert, FHWA