Conference proceeding
Predictive Modeling with Vehicle Sensor Data and IoT for Injury Prevention
2018 4TH IEEE INTERNATIONAL CONFERENCE ON COLLABORATION AND INTERNET COMPUTING (CIC 2018), pp 293-298
01 Jan 2018
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Automobile accidents remain one of the leading causes of death in the United States. Sensor-based driver assistance systems have made driving safer by lending drivers an extra pair of eyes and an information triage center. The Internet of Things technologies enable information exchange between drivers, vehicles, and roads leading towards intelligent transportation systems. Efforts to further injury prevention in the past decade have been focused on heterogenous information sourcing and predictive analytics on driver intent. The federal naturalistic driving database Strategic Highway Research Program 2 (SHRP 2) is unprecedented in that it provides a wealth of data resulted from real-time sensor capture of inprogress driving trips by a large cohort. We discuss our novel approach to study injury risk factors using temporal heterogenous network mining and address the challenge of algorithmic efficiency associated with large datasets by leveraging distributed computing modules.
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Details
- Title
- Predictive Modeling with Vehicle Sensor Data and IoT for Injury Prevention
- Creators
- Christopher C. Yang - Drexel UniversityOu Stella Liang - Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USASantiago Ontanon - Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USAWeimao Ke - Drexel UniversityHelen Loeb - Children's Hospital of PhiladelphiaCharlie Klauer - Virginia Tech, Transportat Inst, Ctr Vulnerable Rd User Safety, Blacksburg, VA USAIEEE
- Publication Details
- 2018 4TH IEEE INTERNATIONAL CONFERENCE ON COLLABORATION AND INTERNET COMPUTING (CIC 2018), pp 293-298
- Conference
- 2018 4TH IEEE INTERNATIONAL CONFERENCE ON COLLABORATION AND INTERNET COMPUTING (CIC 2018), 4th
- Publisher
- IEEE
- Number of pages
- 6
- Grant note
- NSF-1741306; IIS-1650531; DIBBs-1443019; SCH-1521943 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science; Computer Science
- Web of Science ID
- WOS:000519942300035
- Scopus ID
- 2-s2.0-85059780522
- Other Identifier
- 991019167785504721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic