01476737

Component

Automated detection of incidents is still an integral component of advanced traffic management systems (ATMS) and its role for the effective management of freeway operations should not be ignored. This paper presents a novel incident detection algorithm with adaptive thresholding capabilities based on the Hilbert-Huang transform (HHT). The HHT is a powerful tool for processing nonstationary data, and it employs the concept of instantaneous frequency; hence it is suitable for local analysis of traffic measurements. In particular, this paper demonstrates how the empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA) components of HHT could be used to detect traffic incidents on freeways. Evaluation of the proposed algorithm was conducted using real-world traffic data with the California and low-pass filter (Minnesota) as baseline. The test results indicate that the HHT-based incident detection algorithm remarkably outperforms the benchmark algorithms with the highest detection rate (95.8%) and the lowest false alarm rate (0.001). This demonstrates the potential for practical application of the proposed algorithm in reality.

This paper was sponsored by TRB committee AHB20 Freeway Operations.

01470560

13-3424

English

21p

2013

Transportation Research Board 92nd Annual Meeting

Digital/other

Figures; References; Tables

Advanced traffic management systems; Algorithms; Freeway operations; Incident detection; Incident management; Traffic data

Empirical Mode Decomposition; Hilbert-Huang transform

California; Minnesota

Highways; Operations and Traffic Management; I73: Traffic Control

Transportation Research Board Annual Meeting 2013 Paper #13-3424

TRIS, TRB, ATRI

Feb 5 2013 12:41PM