1. DriveSense’14 NSF Workshop on Large-Scale Traffic and Driving Activity Data DriveSense’14, Oct 30-31, Norfolk, VA

2. Breakout Group III – Mobility, Connectivity, Emerging Services/Applications Multi-Modal Transportation Correlation between different mobility sources (e.g., pedestrians vs. vehicles) DSRC (Short Range Communication) has been regulated – Impacts? Role of clustering Energy Constraints! Simulation vs. Testbed – What scale is good enough? Security Services! If you had access to traffic traces / driving data, how would you want to filter and access it? New enabled applications – What data is needed? What do we need to collect?

3. Goals of data collection/sensing – Safety, Efficiency, Sustainability, Livability – User level – Traffic system level Privacy – Allowing user control How to collect – Levels of aggregation Group III – Topics Discussed

4. User level – Accident warning – Route guidance – Parking assistance – Modes of transportation Group III – Goals of Data Collection/Sensing Traffic system level – For decision-makers Traffic signal optimization Infrastructure improvements Effects of tolling – For response Roadside assistance Weather, end-of-queue warnings

5. Allow users control over their data – Start/end times – Data sharing at specific locations/times – Data sharing at specific granularities Aggregate sensitive data – Origin-destination – Add noise Group III – Privacy

6. Need some real-time data for safety, roadside assistance type apps Planning purposes need O-D data – Aggregate level Use real-time data for a limited time only – then aggregate as time passes (for historical comparisons) Group III – How to Collect

7. How to study emergent behavior? – What unknown uses are coming? Incentives – Personal benefit vs. community benefit How to open collected data to researchers? – Summary statistics? Customize access to data – How to filter based on location, needed level of granularity, time? – Access levels (law enforcement vs. research) Group III – Challenges

8. What does the community lack? What type of resources we should have Within a 5year/10year time frame, what is the ideal state of data sensing and collection? How does your idea or this ideal state in five years differ from today? What do you see as significant barriers to achieving this ideal state? What aspects of DriveSense research are good candidates for attention? What are the emerging areas? In your opinion, what is missing and needs to be provided to inspire high quality research? What notable advances in research in other fields (Computer Science, Electrical and Computer Engineering, Civil Engineering, etc.) might apply? – How can NSF encourage the transfer of innovations from other fields and the development of new innovations in DriveSense? How to enhance shared knowledge and resource utilization – What type resources NSF should encourage the development? – Repository? Things to Think About

9. What is the ideal relationship between academia, government and industry partners? How can partnerships or collaborations be fostered? – What projects NSF should invest in that foster building relationship? – How can NSF foster relationships between industry and academia to support innovation? What institutional, educational, and other barriers are preventing DriveSense to advance? As computer science, computer engineering, civil engineering, etc., grew what were the barriers to their growth? How can experiences gained in other fields be applied to DriveSense? How to enrich Education? How to continue this momentum? Summer workshops? – How can academia keep related content current at various education levels? – What role can NSF play in assisting academic institutions maintain this momentum? – What type of initiatives NSF should support to invest in research and curriculum development to close the gap between the definition and application of core concepts within and across disciplines? Collaboration between different funding organizations/agencies (NSF/DoT) Things to Think About