Denoising Traffic Collision Data Using Ensemble Empirical Mode Decomposition and Its Application for Constructing Continuous Risk Profile

Filtering out the noise in traffic collision data is essential in reducing false positive rates (i.e., requiring safety investigation of sites where it is not needed) and can assist government agencies in better allocating limited resources. Previous studies have demonstrated that denoising traffic collision data is possible when there exists a true known high collision concentration location (HCCL) list to calibrate the parameters of a denoising method. However, such a list is often not readily available in practice. To this end, the present study introduces an innovative approach for denoising traffic collision data using the Ensemble Empirical Mode Decomposition (EEMD) method which is widely used for analyzing nonlinear and nonstationary data. The present study explains how to transform the traffic collision data before the data can be decomposed using the EEMD method to obtain set of Intrinsic Mode Functions (IMFs) and residue. The attributes of the IMFs were then carefully examined to denoise the data and to construct Continuous Risk Profiles (CRPs). The findings from comparing the resulting CRP profiles with CRPs in which the noise was filtered out with two different empirically calibrated weighted moving window lengths are also documented, and the results and recommendations for future research are discussed.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ANB20 Safety Data, Analysis and Evaluation. Alternate title: De-noising Traffic Collision Data Using Ensemble Empirical Mode Decomposition and Its Application for Constructing Continuous Risk Profile
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Kim, Nam-Seog
  • Chung, Koohong
  • Ahn, Seongchae
  • Yu, Jeong Whon
  • Choi, Keechoo
• Conference:
  • Transportation Research Board 93rd Annual Meeting
  • Location: Washington DC
  • Date: 2014-1-12 to 2014-1-16
• Date: 2014

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 14p
• Monograph Title: TRB 93rd Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Calibration; Crash data; Crash risk forecasting; High risk locations; Interference; Traffic crashes
• Uncontrolled Terms: Denoising; False positive responses
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; I80: Accident Studies;

Filing Info

• Accession Number: 01519622
• Record Type: Publication
• Report/Paper Numbers: 14-2119
• Files: TRIS, TRB, ATRI
• Created Date: Mar 26 2014 10:07AM