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Traffic Engineering

Project Description

Traffic accidents are a primary concern due to the many fatalities and economic losses that occur every year in the United States. Highways are potential sites of fatal traffic accidents in the US. This raises the need for a model that can predict the severity of different accidents on the highways to better help first responders, and to better engineer future highways. In this project, we model the problem of severity prediction of traffic accidents based on data that was collected over a nine-year period from one of the highways in the state of Kansas. The data contains various information such as the severity of accidents, weather conditions, lighting conditions, etc.

Keywords

Machine Learning, Class Imbalance, Predictive Analytics, Feature Selection, Data Augmentation, Traffic Engineering, Deep Learning

Current Team Members

  • Majed Alsadhan - Ph.D. candidate, Computer Science, Kansas State University
  • Deepti Lamba - Ph.D. candidate, Computer Science, Kansas State University

Faculty Members

  • William H. Hsu - Professor, Computer Science, Kansas State University
  • Eric J. Fitzsimmons - Assistant Professor, Civil Engineering, Kansas State University
  • Greg Newmark - Assistant Professor, College of Architecture, Planning and Design, Kansas State University

Former Team Members

  • Lei (Ray) Luo - Ph.D. student, Computer Science, Kansas State University
  • Laurie Greenwold - Ph.D. student, Computer Science, Kansas State University
  • Luis Enrique Bobadilla - M.S. student, Computer Science, Kansas State University
  • Yihong Theis - M.S. 2019 Computer Science, Kansas State University
  • Sai Sandeep Dasari - M.S. 2018 Computer Science, Kansas State University
  • Mary Grace Blair - B.S. 2018 Computer Science, Kansas State University

KDD Lab Publications

  • Lamba, D., Alsadhan, M., Hsu, W., Fitzsimmons, E., & Newmark, G. (2019). Coping with Class Imbalance in Classification of Traffic Crash Severity Based on Sensor and Road Data: A Feature Selection and Data Augmentation Approach. In Nagamalai, D. et al., eds. Proceedings of the 6th International Conference on Data Mining and Database (DMDB 2019), pp. 125-137, Vancouver, BC, Canada, May 25-26, 2019.

    Last updated by rotclanny on Jun 18, 2023