TY - JOUR
T1 - Hierarchical fuzzy rule-based system optimized with genetic algorithms for short term traffic congestion prediction
AU - Zhang, Xiao
AU - Onieva, Enrique
AU - Perallos, Asier
AU - Osaba, Eneko
AU - Lee, Victor C.S.
PY - 2014/6
Y1 - 2014/6
N2 - Taking practical and effective traffic prediction and control measures to ease highway traffic congestion is a significant issue in the research field of Intelligent Transportation Systems (ITS). This paper develops a Hierarchical Fuzzy Rule-Based System (HFRBS) optimized by Genetic Algorithms (GAs) to develop an accurate and robust traffic congestion prediction system employing a large number of input variables. The proposed system reduces the size of the involved input variables and rule base while maintaining a high degree of accuracy. To achieve this, a hierarchical structure composed of FRBSs is optimized by a Steady-State GA, which combines variable selection and ranking, lateral tuning of the membership functions, and optimization of the rule base. We test the capability of the proposed approach on short term traffic congestion problems, as well as on benchmark datasets, and compare the outcomes with representative algorithms from the literature in inferring fuzzy rules, confirming the effectiveness of the proposed approach. © 2014 Elsevier Ltd.
AB - Taking practical and effective traffic prediction and control measures to ease highway traffic congestion is a significant issue in the research field of Intelligent Transportation Systems (ITS). This paper develops a Hierarchical Fuzzy Rule-Based System (HFRBS) optimized by Genetic Algorithms (GAs) to develop an accurate and robust traffic congestion prediction system employing a large number of input variables. The proposed system reduces the size of the involved input variables and rule base while maintaining a high degree of accuracy. To achieve this, a hierarchical structure composed of FRBSs is optimized by a Steady-State GA, which combines variable selection and ranking, lateral tuning of the membership functions, and optimization of the rule base. We test the capability of the proposed approach on short term traffic congestion problems, as well as on benchmark datasets, and compare the outcomes with representative algorithms from the literature in inferring fuzzy rules, confirming the effectiveness of the proposed approach. © 2014 Elsevier Ltd.
KW - Congestion forecasting
KW - Genetic algorithms
KW - Genetic fuzzy systems
KW - Hierarchical fuzzy rule-based systems
KW - Intelligent transportation systems
KW - Traffic congestion prediction
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84902551279&origin=recordpage
U2 - 10.1016/j.trc.2014.02.013
DO - 10.1016/j.trc.2014.02.013
M3 - RGC 21 - Publication in refereed journal
SN - 0968-090X
VL - 43
SP - 127
EP - 142
JO - Transportation Research Part C: Emerging Technologies
JF - Transportation Research Part C: Emerging Technologies
ER -
