POP-TRAFFIC: A Novel Fuzzy Neural Approach to Road Traffic Analysis and Prediction

Chai Hiok Quek · Michel Pasquier

199 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Traffic Prediction and Management Techniques Traffic control and management Fuzzy Logic and Control Systems

Abstract

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.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 199 citations exist upstream.

References

2004Travel-Time Prediction With Support Vector Regression 2002Urban traffic flow prediction using a fuzzy-neural approach 2000Fuzzy-Wavelet RBFNN Model for Freeway Incident Detection 1975Organisation behaviour

Cited by

2014Short-term traffic forecasting: Where we are and where we’re going 2017Deep learning for short-term traffic flow prediction 2011Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm 2017An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic 2016Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method 2012Dynamic near-term traffic flow prediction: system-oriented approach based on past experiences 2014Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction 2019Traffic flow prediction based on combination of support vector machine and data denoising schemes

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.