Soft-computing techniques applied to short-term traffic flow forecasting

Authors:
Giovanni Huisken
Affiliations:
Centre for Transport Studies, Department of Civil Engineering, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
Venue:
Systems Analysis Modelling Simulation
Year:
2003

Citing 4
Cited 3

Traffic control process of expressway by fuzzy logic

Fuzzy Sets and Systems - Fuzzy Control
Radial basis function approximations to polynomials

Numerical analysis 1987
The appeal of parallel distributed processing

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation

Neural Network-Based Load Prediction for Highly Dynamic Distributed Online Games

Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
Prediction-based real-time resource provisioning for massively multiplayer online games

Future Generation Computer Systems
An aggregation approach to short-term traffic flow prediction

IEEE Transactions on Intelligent Transportation Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Multi Linear Perceptron (MLP) neural networks, Radial Basis Function (RBF) neural networks, and Fuzzy Logic (FL) were used as soft-computing (or artificial intelligent) modelling techniques to come to short-term forecasts of traffic flow. The field data used for modelling was collected through single loop induction detectors on freeways 405 and 22 in Orange County, California. The data consisted of 30-s time bins of traffic flow [vehicles/3Os], occupancy [seconds/3Os], and reliability indicators (value 0-7) that were transformed into 5-min time bins. The paper concludes that the MLP and the RBF(2) - with 1 output - method outperform the FL and the RBF(1)-with 2 outputs method.