A new methodology for incident detection and characterization on surface streets

✍ Scribed by Jiuh-Biing Sheu; Stephen G. Ritchie

• Publisher: Elsevier Science
• Year: 1998
• Tongue: English
• Weight: 234 KB
• Volume: 6
• Category: Article
• ISSN: 0968-090X
• DOI: 10.1016/s0968-090x(99)00002-9

✦ Synopsis

In this paper, a new methodology is presented for real-time detection and characterization of incidents on surface streets. The proposed automatic incident detection approach is capable of detecting incidents promptly as well as characterizing incidents in terms of time-varying lane-changing fractions and queue lengths in blocked lanes, lanes blocked due to incidents, and incident duration. The architecture of the proposed incident detection approach consists of three sequential procedures: (1) Symptom Identi®cation for identi®cation of incident symptoms, (2) Signal Processing for real-time prediction of incident-related lane trac characteristics and (3) Pattern Recognition for incident recognition. Lane trac counts and occupancy are the only two major types of input data, which can be readily collected from point detectors. The primary techniques utilized in this paper include: (1) a discrete-time, nonlinear, stochastic system with boundary constraints to predict real-time lane-changing fractions and queue lengths and (2) a patternrecognition-based algorithm employing modi®ed sequential probability ratio tests (MSPRT) to detect incidents. O-line tests based on simulated as well as video-based real data were conducted to assess the performance of the proposed algorithm. The test results have indicated the feasibility of achieving real-time incident detection using the proposed methodology.