Real time identification of road traffic control measures

Studies in Computational Intelligence

Volumn 144, Issue , 2008, Pages 63-80

  Almejalli, Khaled ^a   Dahal, Keshav ^a   Hossain, M Alamgir ^a  

^a UNIVERSITY OF BRADFORD   (United Kingdom)

Author keywords

Fuzzy logic; Genetic algorithms; Neural networks; Road traffic control

Indexed keywords

EID: 51649099046     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-69390-1_4     Document Type: Article

References (42)

1. Almejalli, K., Dahal, K., Hossain, A.: Intelligent traffic control decision support system. In: Giacobini, M., et al. (eds.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 688-701. Springer, Heidelberg (2007)
2. Almejalli, K., Dahal, K., Hossain, A.: GA-based learning algorithms to identify fuzzy rules for fuzzy neural networks. In: De Janeiro, R. (ed.) The 7th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 289-296. IEEE Computer Society Press, Los Alamitos (2007)
3. Bingham, E.: Reinforcement learning in neurofuzzy traffic signal control. European Journal of Operational Research 131(2), 232-241 (2001)
4. Bogenberger, K., Keller, H.: An evolutionary fuzzy system for coordinated and traffic responsive ramp metering. In: The 34th Annual Hawaii International Conference on System Sciences, p. 3038. IEEE, Los Alamitos (2001)
5. Chen, M., Linkens, D.: Rule-base self-generation and simplification for data-driven fuzzy models. In: The 10th IEEE International Conference on Fuzzy Systems, Melbourne, pp. 424-427 (2001)
6. Chunshien, L., Chun-Yi, L.: Self-organizing neuro-fuzzy system for control of unknown plants. IEEE Transactions on Fuzzy Systems 11(1), 135-150 (2003)
7. Cuena, J., Hernandez, J., Molina, M.: Knowledge-based models for adaptive traffic management systems. Transportation Research Part C 3(5), 311-337 (1995)
8. De Schutter, B., Hoogendoorn, S., Schuurman, H., Stramigioli, S.: A multi-agent case-based traffic control scenario evaluation system. In: Proceedings of Intelligent Transportation Systems, pp. 678-683. IEEE, Los Alamitos (2003)
9. FHWA. FHWA Administrator Testifies That Growing Traffic Congestion Threatens Nation's Economy, Quality of Life (2002) (October 15, 2006), http://www.fhwa.dot.gov/pressroom/fhwa0220.htm
10. Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesely, Reading (1989)
11. Goldberg, D., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. In: The First Workshop on Foundations of Genetic Algorithms, pp. 69-93. Morgan Kaufmann, San Francisco (1991)
12. Hegyi, A., De Schutter, B., Hoogendoorn, S., Babuska, R., van Zuylen, H.: A fuzzy decision support system for traffic control centers. In: Proceedings of Intelligent Transportation Systems, pp. 358-363. IEEE, Los Alamitos (2001)
13. Henry, J., Farges, J., Gallego, J.: Neuro-fuzzy techniques for traffic control. Control Engineering Practice 6(6), 755-761 (1998)
14. Holland, J.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1992)
15. Hoogendoorn, S., Schuurman, H., De Schutter, B.: Real-time traffic management scenario evaluation. In: The 10th IFAC Symposium on Control in Transportation Systems (CTS 2003), Tokyo, pp. 343-348 (2003)
16. Ishibuchi, H., Nozaki, K., Yamamoto, N., Tanaka, H.: Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Transactions on Fuzzy Systems 3(3), 260-270 (1995)
17. Ishibuchi, H., Yamamoto, T.: Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets and Systems 141(1), 59-88 (2004)
18. Karr, C.: Genetic algorithms for fuzzy controllers. AI Expert 6(2), 26-33 (1991)
19. Kohonen, T.: Self-Organization and Associative Memory. Springer, New York (1984)
20. Krause, B., von Altrock, C., Limper, K., Schafers, W.: A neuro-fuzzy adaptive control strategy for refuse incineration plants. Fuzzy Sets and Systems 63(3), 329-338 (1994)
21. Lee, M., Lee, S., Park, C.: A new neuro-fuzzy identification model of nonlinear dynamic systems. International Journal of Approximate Reasoning 10(1), 29-44 (1994)
22. Lin, T.: Neural Fuzzy Control Systems With Structure and Parameter Learning. World Scientific, Singapore (1994)
23. Lin, C.: A GA-based neural fuzzy system for temperature control. Fuzzy Sets and Systems 143(2), 311-333 (2004)
24. Lin, C., Lee, C.: Neural-network-based fuzzy logic control and decision system. IEEE Transactions on Computers 40(12), 1320-1336 (1991)
25. Messmer, A.: METANET - A Simulation Program for Motorway Networks. Technical University of Crete, Dynamic Systems and Simulation Laboratory (2000)
26. Molina, M., Hern, J., Cuena, E.: A structure of problem-solving methods for realtime decision support in traffic control. International Journal of Human-Computer Studies 49(4), 577-600 (1998)
27. Quek, C., Pasquier, M., Lim, B.: POP-TRAFFIC: A novel fuzzy neural approach to road traffic analysis and prediction. IEEE Transactions on Intelligent Transportation Systems 7(2), 133-146 (2006)
28. Quek, C., Zhou, R.: POPFNN: A pseudo outer-product based fuzzy neural network. Neural Networks 9(9), 1569-1581 (1996)
29. Quek, C., Zhou, R.: The POP learning algorithms: Reducing work in identifying fuzzy rules. Neural Networks 14(10), 1431-1445 (2001)
30. Petrovic-Lazarevic, S., Coghill, K., Abraham, A.: Neuro-fuzzy modelling in support of knowledge management in social regulation of access to cigarettes by minors. Knowledge-Based Systems 17(1), 57-60 (2004)
31. Ritchie, S.: A knowledge-based decision support architecture for advanced traffic management. Transportation Research Part A: General 24(1), 27-37 (1990)
32. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Rumelhart, D.E., McClelland, J.A. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1: Foundations, pp. 318-362. MIT Press, Cambridge (1986)
33. Tay, J., Zhang, X.: Neural fuzzy modeling of anaerobic biological wastewater treatment systems. Journal of Environmental Engineering 125(12), 1149-1159 (1999)
34. Trabia, M., Kaseko, M., Ande, M.: A two-stage fuzzy logic controller for traffic signals. Transportation Research Part C: Emerging Technologies 7(6), 353-367 (1999)
35. Wei, C.: Analysis of artificial neural network models for freeway ramp metering control. Artificial Intelligence in Engineering 15(3), 241-252 (2001)
36. Wei, W., Zhang, Y., Mbede, J., Zhang, Z., Song, J.: Traffic signal control using fuzzy logic and MOGA. In: 2001 IEEE International Conference on Systems, Man and Cybernetics, pp. 1335-1340 (2001)
37. Werbos, P.: Neurocontrol and fuzzy logic: Connections and designs. International Journal of Approximate Reasoning 6(2), 185-219 (1992)
38. Whitley, D.: Using reproductive evaluation to improve genetic search and heuristic discovery. In: The Second International Conference on Genetic Algorithms and Their Application, pp. 108-115. Lawrence Erlbaum Associates, Mahwah (1987)
39. Yager, R.: Modeling and formulating fuzzy knowledge bases using neural networks. Neural Networks 7(8), 1273-1283 (1994)
40. Yager, R.: Implementing fuzzy logic controllers using a neural network framework. Fuzzy Sets and Systems 100(1), 133-144 (1999)
41. Zhang, H., Ritchie, S.: Real-time decision-support system for freeway management and control. Journal of Computing in Civil Engineering 8(1), 35-51 (1994)
42. Zhang, H., Ritchie, S., Jayakrishnan, R.: Coordinated traffic-responsive ramp control via nonlinear state feedback. Transportation Research Part C: Emerging Technologies 9(5), 337-352 (2001)