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[Figure]:  What are the key issues in defining a data environment?  It sounds easy until you have to do it.  Then it turns out to be harder.
There is a lot of data out there.  What data do we capture?  The easy answer is that we can capture all of it and then figure out what to do with it.  But in some cases it is not the best idea to capture every single data element because of communications requirements. 
The other issue is why are we capturing the data?  How do we use the data?  One size does not fit all.  There isn’t just one data environment that addresses all applications.  We might have one data environment that supports tactical movements at an intersection and another that supports national level freight movement.  So, data environments can support a specific application or multiple applications, and there can be multiple data environments.
The 3rd issue is what data do we keep?  We may not be able to keep all of the data because of privacy issues, IP rights issues. We have to make decisions about what data to keep and what data to leave out.
Finally, how do we structure the data?  What are the rules?  Do we need guidelines? And how do we deal with structuring data that have IP rights or privacy issues associated with them?
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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.
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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.
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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. 
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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. 
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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. 
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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. 
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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. 
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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. 
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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. 
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Numbers of total visits etc.. as of 5/21/12
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Data:
•Archived, simulated and real-time vehicle probe data from ongoing research activity in the V2V/V2I Test Bed (Michigan)
•Real-time vehicle probe data feed from ongoing field testing at the FHWA Saxton Transportation Operations Laboratory Test Bed (Virginia)
•Seattle Real-time Transit feed
•Integrated multimodal data from vehicles and roadside sensors in four test data sets (Seattle, Portland, Pasadena, San Diego). Data includes vehicle (light vehicle, truck and transit), weather, freeway/arterial sensor and traffic signal data. 
•Vehicle data from the 2011 World Congress Demonstration
•The Road Weather Management program Integrated Mobile Observations (IMO) Test  data from wirelessly-connected snowplows and maintenance trucks
•Traveler, vehicle and infrastructure sensor data from other demonstrations and DMA research
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Our program will investigate these research topics
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These are idea that researchers can offer (outside of the program)
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Invitation for participants to play with RDE
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These are some critical questions that we need help answering. 
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Invite participants to visit and use the RDE; and participate in stakeholder meetings to provide feedback
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