Traffic signal control based on adaptive neural-fuzzy inference system applied to intersection
Adaptive Neural-Fuzzy Inference System (ANFIS) that integrates the best features of fuzzy systems and neural networks has been widely applied in many areas. It can be applied to synthesize controllers, which are able to tune the fuzzy control system automatically, and models that learn from past dat...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/47696/1/Traffic%20signal%20control%20based%20on%20adaptive%20neural-fuzzy%20inference%20system%20applied%20to%20intersection.pdf |
| _version_ | 1796974838129098752 |
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| author | Che Soh, Azura Abdul Rahman, Ribhan Zafira Lai, Ghuan Rhung Md. Sarkan, Haslina |
| author_facet | Che Soh, Azura Abdul Rahman, Ribhan Zafira Lai, Ghuan Rhung Md. Sarkan, Haslina |
| author_sort | Che Soh, Azura |
| collection | UPM |
| description | Adaptive Neural-Fuzzy Inference System (ANFIS) that integrates the best features of fuzzy systems and neural networks has been widely applied in many areas. It can be applied to synthesize controllers, which are able to tune the fuzzy control system automatically, and models that learn from past data to predict future behavior. The aim of this research is to develop an ANFIS traffic signals controller for multilane intersection in order to ease traffic congestions at traffic intersections. The new concept to generate sample data for ANFIS training is introduced in this research. The sample data is generate based on fuzzy rules and can be analysed using tree diagram. This controller is simulated on multilane traffic intersection model developed using M/M/1 queuing theory and its performance in terms of average waiting time, queue length and delay time are compared with traditional controllers and fuzzy controller. Simulation result shows that the average waiting time, queue length, and delay time of ANFIS traffic signal controller are the lowest as compared to the other three controllers. In conclusion, the efficiency and performance of ANFIS controller are much better than that of fuzzy and traditional controllers in different traffic volumes. |
| first_indexed | 2024-03-06T09:02:51Z |
| format | Conference or Workshop Item |
| id | upm.eprints-47696 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:02:51Z |
| publishDate | 2011 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-476962016-07-14T05:49:22Z http://psasir.upm.edu.my/id/eprint/47696/ Traffic signal control based on adaptive neural-fuzzy inference system applied to intersection Che Soh, Azura Abdul Rahman, Ribhan Zafira Lai, Ghuan Rhung Md. Sarkan, Haslina Adaptive Neural-Fuzzy Inference System (ANFIS) that integrates the best features of fuzzy systems and neural networks has been widely applied in many areas. It can be applied to synthesize controllers, which are able to tune the fuzzy control system automatically, and models that learn from past data to predict future behavior. The aim of this research is to develop an ANFIS traffic signals controller for multilane intersection in order to ease traffic congestions at traffic intersections. The new concept to generate sample data for ANFIS training is introduced in this research. The sample data is generate based on fuzzy rules and can be analysed using tree diagram. This controller is simulated on multilane traffic intersection model developed using M/M/1 queuing theory and its performance in terms of average waiting time, queue length and delay time are compared with traditional controllers and fuzzy controller. Simulation result shows that the average waiting time, queue length, and delay time of ANFIS traffic signal controller are the lowest as compared to the other three controllers. In conclusion, the efficiency and performance of ANFIS controller are much better than that of fuzzy and traditional controllers in different traffic volumes. IEEE 2011 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/47696/1/Traffic%20signal%20control%20based%20on%20adaptive%20neural-fuzzy%20inference%20system%20applied%20to%20intersection.pdf Che Soh, Azura and Abdul Rahman, Ribhan Zafira and Lai, Ghuan Rhung and Md. Sarkan, Haslina (2011) Traffic signal control based on adaptive neural-fuzzy inference system applied to intersection. In: 2011 IEEE Conference on Open Systems (ICOS 2011), 25-28 Sept. 2011, Langkawi, Kedah. (pp. 231-236). 10.1109/ICOS.2011.6079251 |
| spellingShingle | Che Soh, Azura Abdul Rahman, Ribhan Zafira Lai, Ghuan Rhung Md. Sarkan, Haslina Traffic signal control based on adaptive neural-fuzzy inference system applied to intersection |
| title | Traffic signal control based on adaptive neural-fuzzy inference system applied to intersection |
| title_full | Traffic signal control based on adaptive neural-fuzzy inference system applied to intersection |
| title_fullStr | Traffic signal control based on adaptive neural-fuzzy inference system applied to intersection |
| title_full_unstemmed | Traffic signal control based on adaptive neural-fuzzy inference system applied to intersection |
| title_short | Traffic signal control based on adaptive neural-fuzzy inference system applied to intersection |
| title_sort | traffic signal control based on adaptive neural fuzzy inference system applied to intersection |
| url | http://psasir.upm.edu.my/id/eprint/47696/1/Traffic%20signal%20control%20based%20on%20adaptive%20neural-fuzzy%20inference%20system%20applied%20to%20intersection.pdf |
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