Abstract
Accurate prediction of traffic flow is an important step needed in urban traffic management systems. While several neurofuzzy approaches have been used in literature for this particular problem, most of them need manual intervention in the formulation of the fuzzy rule base and also in determining the architecture of the neurofuzzy system. This paper evaluates two recent neurofuzzy algorithms that are capable of automatically determining the rule base and architecture in a purely data driven approach. An open source traffic data has been evaluate and compare the performance of these neurofuzzy systems.
Highlights
In transportation and logistics travel time information plays an important role
The results show that both these systems are capable of accurately predicting the traffic flow, while the McIT2FIS provides higher accuracy that may be attributed to its meta-cognitive learning capability
The performance of the Self-Adaptive Fuzzy Inference Network (SaFIN) and McIT2FIS algorithms are evaluated for the traffic flow prediction problem using the dataset provided in [19]
Summary
In transportation and logistics travel time information plays an important role. Nowadays it is applied in applied in various Intelligent Transport Systems (ITS), such as in-vehicle route guidance (RGS) and advanced traffic management systems (ATMS). A range of different traffic factors affect the travel time. A large traffic data is required to accurately predict the travel time. A good Understanding of the traffic factors affecting travel time is necessary for improving prediction accuracy in related travel time studies. Received: February 27, 2016 Published: May 7, 2016 §Correspondence author c 2016 Academic Publications, Ltd. url: www.acadpubl.eu
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