00965454

Component

https://doi.org/10.3141/1836-11

http://scholar.google.com...elli&publication_year=2003

The CUSUM (cumulative sum of log-likelihood ratio) algorithm is an optimization-based algorithm that is attractive for many applications because it can minimize detection delay and can explicitly incorporate the characteristics of processes before and after changes. One such application is freeway incident detection, where field-measurable traffic-flow parameters are used to flag incidents in real time in an expedient and reliable manner. In the presented study, the special characteristics of traffic processes associated with incidents are incorporated into the CUSUM algorithm for freeway incident detection. In the algorithm evaluation, the most recently developed neural networks are compared with an enhanced CUSUM algorithm. The neural network algorithms are systematically evaluated first among themselves, and then the best of them is compared with the CUSUM algorithm. The results demonstrate that the CUSUM incident detection algorithm can perform better than the neural network algorithms. The neural network algorithm may show inferior performance because it cannot adjust its decision threshold in real time.

This paper appears in Transportation Research Record No. 1836, Initiatives in Information Technology and Geospatial Science for Transportation.

English

p. 83-92

2003

Transportation Research Record

0309085721

Figures (8) ; References (20) ; Tables (1)

Algorithms; Incident detection; Neural networks; Real time data processing

Cumulative sum of log-likelihood ratio

Highways; Operations and Traffic Management; I73: Traffic Control

TRIS, TRB, ATRI

Nov 7 2003 12:00AM

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• BUILDING AND MAINTAINING A STATEWIDE TRANSPORTATION FRAMEWORK
• COMMON DATA FORMAT ARCHIVING OF LARGE-SCALE INTELLIGENT TRANSPORTATION SYSTEMS DATA FOR EFFICIENT STORAGE, RETRIEVAL, AND PORTABILITY
• DESIGN OF ARCHIVAL TRAFFIC DATABASES: QUANTITATIVE INVESTIGATION INTO APPLICATION OF ADVANCED DATA MODELING CONCEPTS
• EXPLORING IMPUTATION TECHNIQUES FOR MISSING DATA IN TRANSPORTATION MANAGEMENT SYSTEMS
• FUZZY-NEURAL NETWORK TRAFFIC PREDICTION FRAMEWORK WITH WAVELET DECOMPOSITION
• INTEGRATION OF LIGHT DETECTION AND RANGING TECHNOLOGY WITH PHOTOGRAMMETRY IN HIGHWAY LOCATION AND DESIGN
• INTEGRATION OF SPATIAL POINT FEATURES WITH LINEAR REFERENCING METHODS
• LEARNING A CAUSAL MODEL FROM HOUSEHOLD SURVEY DATA BY USING A BAYESIAN BELIEF NETWORK
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• OPTIMIZATION OF DYNAMIC NEURAL NETWORK PERFORMANCE FOR SHORT-TERM TRAFFIC PREDICTION
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• SPATIOTEMPORAL OBJECT DATABASE APPROACH TO DYNAMIC SEGMENTATION
• TOWARD MORE EFFECTIVE TRANSPORTATION APPLICATIONS OF COMPUTATIONAL INTELLIGENCE PARADIGMS
• USE OF LOCAL LINEAR REGRESSION MODEL FOR SHORT-TERM TRAFFIC FORECASTING