POP-TRAFFIC: a novel fuzzy neural approach to road traffic analysis and prediction

• Published in: IEEE transactions on intelligent transportation systems Vol. 7; no. 2; pp. 133 - 146
• Main Authors: Quek, C., Pasquier, M., Lim, B.B.S.
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.06.2006; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Backpropagation; Computer science; control theory; systems; Control theory. Systems; Data mining; Density and volume prediction; Design engineering; Exact sciences and technology; Feedforward neural networks; Fuzzy; Fuzzy logic; fuzzy neural network; Fuzzy neural networks; Fuzzy set theory; Ground, air and sea transportation, marine construction; Mathematical models; Neural networks; noise tolerance; pseudo outer-product-based fuzzy neural network (POPFNN); Road transportation and traffic; Roads; Robotics; Studies; Telecommunication traffic; traffic analysis; Traffic control; Traffic engineering; Traffic flow; traffic prediction and modeling; truth-value-restriction fuzzy inference; Short term; Inference; Neural network; Knowledge extraction; Modeling; Fuzzy neural nets; Logic circuit; Fuzzy logic; Statistical regression; fuzzy neural network; truth-value-restriction fuzzy inference; Backpropagation algorithm; Traffic flow; traffic prediction and modeling; Regression model; Traffic management; Density and volume prediction; Knowledge discovery; Road traffic; traffic analysis; pseudo outer-product-based fuzzy neural network (POPFNN); noise tolerance
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2006.874712

Although much research has been done over the decades on the formulation of statistical regression models for road traffic relationships, they have been largely unsuitable due to the complexity of traffic characteristics. Traffic engineers have resorted to alternative methods such as neural networks, but despite some promising results, the difficulties in their design and implementation remain unresolved. In addition, the opaqueness of trained networks prevents understanding the underlying models. Fuzzy neural networks, which combine the complementary capabilities of both neural networks and fuzzy logic, thus constitute a more promising technique for modeling traffic flow. This paper describes the application of a specific class of fuzzy neural network known as the pseudo outer-product fuzzy neural network using the truth-value-restriction method (POPFNN-TVR) for short-term traffic flow prediction. The obtained results highlight the capability of POPFNN-TVR in fuzzy knowledge extraction and generalization from input data as well its high degree of prediction capability as compared to traditional feedforward neural networks using backpropagation learning.