title: Denoising traffic collision data using ensemble empirical mode decomposition (EEMD) and its application for constructing continuous risk profile (CRP).
abstract: 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 describes 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.
authors.affiliation: Department of Electrical Engineering and Computer Science, University of California at Berkeley, United States. Electronic address: namseog@eecs.berkeley.edu.
authors.forename: Nam-Seog
authors.initials: NS
authors.lastname: Kim
authors.affiliation: School of Civil, Environmental and Architectural Engineering, Korea University, Anam-Dong, Seongbuk-Gu, Seoul 136-713, Republic of Korea; California Department of Transportation Operations, Highway Operations, 111 Grand Ave, Oakland, CA 94623, United States. Electronic address: koohong_chung@dot.ca.gov.
authors.forename: Koohong
authors.initials: K
authors.lastname: Chung
authors.affiliation: Department of Transportation Engineering, Ajou University, San 5, Woncheon-dong, Yeongtong-gu, Suwon 443-749, Republic of Korea. Electronic address: citadel@ajou.ac.kr.
authors.forename: Seongchae
authors.initials: S
authors.lastname: Ahn
authors.affiliation: Department of Transportation Engineering, Ajou University, San 5, Woncheon-dong, Yeongtong-gu, Suwon 443-749, Republic of Korea. Electronic address: jeongwhon@ajou.ac.kr.
authors.forename: Jeong Whon
authors.initials: JW
authors.lastname: Yu
authors.affiliation: Department of Transportation Engineering, Ajou University, San 5, Woncheon-dong, Yeongtong-gu, Suwon 443-749, Republic of Korea. Electronic address: keechoo@ajou.ac.kr.
authors.forename: Keechoo
authors.initials: K
authors.lastname: Choi
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000465:Algorithms; D001330:Electronic Data Processing; D006801:Humans; D012306:Risk; D013223:Statistics as Topic
keywords: Continuous Risk Profile; Empirical Mode Decomposition; Intrinsic Mode Function; Traffic collision