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DriveSense’14 NSF Workshop on Large-Scale Traffic and Driving Activity Data

This workshop explored topics such as multi-modal transportation, correlation between different mobility sources, energy constraints, security services, and the filtering and access of traffic and driving data. It also discussed goals of data collection/sensing, privacy concerns, and challenges in studying emergent behavior.

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DriveSense’14 NSF Workshop on Large-Scale Traffic and Driving Activity Data

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  1. DriveSense’14NSF 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. Group III – Topics Discussed • Goals of data collection/sensing • Safety, Efficiency, Sustainability, Livability • User level • Traffic system level • Privacy • Allowing user control • How to collect • Levels of aggregation

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

  5. Group III – Privacy • 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

  6. Group III – How to Collect • 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)

  7. Group III – Challenges • 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)

  8. Things to Think About • What does the community lack? What type of resources we should have • Within a5year/10year timeframe, whatistheidealstateofdata sensing and collection?Howdoesyourideaorthisidealstateinfiveyearsdifferfromtoday? Whatdoyouseeassignificantbarrierstoachievingthisidealstate? • What aspectsofDriveSense research aregoodcandidatesforattention? Whataretheemergingareas? Inyouropinion, whatismissingandneedstobeprovidedtoinspirehighqualityresearch? Whatnotableadvancesinresearch inotherfields(Computer Science, ElectricalandComputerEngineering, Civil Engineering, etc.) mightapply? • 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?

  9. Things to Think About • 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, andotherbarriersarepreventingDriveSense to advance? Ascomputerscience, computerengineering, civil engineering, etc., grewwhatwerethebarrierstotheirgrowth? HowcanexperiencesgainedinotherfieldsbeappliedtoDriveSense? • 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)

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