[1]
S. J. Russell, P. N. Russell, C. Eberhart, Y.H. Shi, Computational Intelligence: Concepts to Implementation [M]., Posts & Telecom Press, pp.2-3.
Google Scholar
[2]
Orvig, Artificial Intelligence: A Modern Approach[M]., Tsinghua Univ. Press. Peking, (2006).
Google Scholar
[3]
J. A. Feldman, Neural networks, artificial intelligence and computational reality[J]., Computers in Industry, vol. 14, no. 1-3, May, 1990, pp.145-148.
DOI: 10.1016/0166-3615(90)90115-6
Google Scholar
[4]
Mark Dougherty, A review of neural network applied to transport[J]. , Transpn. Res. -C, 1995, vol. 3, no. 4, pp.247-260.
Google Scholar
[5]
Nahatsuji T. , Terutoshi K, Development of a self- organizing traffic control system using neural network models., Transportation Research Record, 1991, vol. 1324, p.131~145.
Google Scholar
[6]
M. Papageorgiou, A. Messmer, J. Azema and D. Drewanz, A neural network approach to freeway network traffic control[J]., Control Eng. Practice, 1995, vol. 3, no. 12, pp.1719-1726.
DOI: 10.1016/0967-0661(95)00184-v
Google Scholar
[7]
C.H. Wei, Analysis of artificial neural network models for free ramp metering control[J]., Artificial Intelligence in Engineering, 2001, vol. 15, pp.241-252.
DOI: 10.1016/s0954-1810(01)00019-x
Google Scholar
[8]
G. Lyons, J. Hunt, F. McLeod, A neural model for enhanced operation of midblock signaled pedestrian crossings[J]., European Journal of Operational Research , 2001, vol. 129, pp.346-354.
DOI: 10.1016/s0377-2217(00)00232-0
Google Scholar
[9]
Abdulhai, B., Porwal, H., W. Recker, Short-term traffic flow prediction using neuro-genetic algorithms [J]., ITS Journal, 2002, vol. 7, p.3–41.
DOI: 10.1080/10248070212011
Google Scholar
[10]
Guoqiang Zhang, B. Eddy Patuwo, Michael Y. Hu, Forecasting with artificial neural networks: The State of the art[J]., International Journal of Forecasting, 1998, (14), pp.35-62.
DOI: 10.1016/s0169-2070(97)00044-7
Google Scholar
[11]
S.Y. Yun,S. Namkoong, J.H. Rho, S.W. Shin, J.U. Choi, A performance evaluation of neural network models in traffic volume forecasting[J]. , Mathl. Comput. Modelling, 1998, vol. 27, no. 9-11, pp.293-310.
DOI: 10.1016/s0895-7177(98)00065-x
Google Scholar
[12]
H. R. Kirby, S. M. Watson, M. S. Dougherty, Should we use neural networks or statistical models for short-term motorway traffic forecsting[J]. , International Journal of Forecasting, 1997, vol. 13, pp.43-50.
DOI: 10.1016/s0169-2070(96)00699-1
Google Scholar
[13]
Mark S. Dougherty, Mark R. Cobbett, Short-term inter-urban traffic forecasts using neural networks[J]., International Journal of Forecasting , 1997, vol. 13, pp.21-31.
DOI: 10.1016/s0169-2070(96)00697-8
Google Scholar
[14]
F.X. Qiao, H. Yang, William H.K. Lam, Intelligent simulation and prediction of traffic flow dispersion[J]., Transportation research Part B, 2001, (35), pp.843-863.
DOI: 10.1016/s0191-2615(00)00024-2
Google Scholar
[15]
H. Dia"An object-oriented neural network approach to short-term traffic forecasting[J]. " European Journal of Operational Research, 2001, (131), pp.253-261.
DOI: 10.1016/s0377-2217(00)00125-9
Google Scholar
[16]
S. Huang, Adel W. Sadek, A novel forecasting approach inspired by human memory: The example of short-term traffic volume forecasting[J], Transportation Research Part C, 2009, (17), pp.510-525.
DOI: 10.1016/j.trc.2009.04.006
Google Scholar
[17]
X.D. Zang, The shor-term traffic volume forecasting for urban interchange based on RBF artificial neural networks[A], International Conference on Mechatronics and Automation[C]. Aug. 2009, pp.2607-2611.
DOI: 10.1109/icma.2009.5246693
Google Scholar
[18]
M. Fallah-Tafti, The application of artificial neural networks to anticipate the average journey time of traffic in the vicinity of merges[J],. Knowledge-based Sysems, 2001, (14), pp.203-211.
DOI: 10.1016/s0950-7051(01)00098-3
Google Scholar
[19]
S. Fogen Kalyoncuoglu, M. Tigdemir, An alternative approach for modeling and simulation of traffic data: artificial neural networks[J],. Simulation Modelling Practice and Theory, 2004, (12), pp.351-362.
DOI: 10.1016/j.simpat.2004.04.002
Google Scholar
[20]
C. Ledoux, An urban traffic flow model integrating neural networks[J],. Transp. Res. -C, 1997, Vol. 5, No. 5, pp.287-300.
Google Scholar
[21]
H. Berk Celikoglu, H. Kerem Cigizoglu, Public transportation trip flow modeling with generalized regression neural networks[J],. Advances in Engineering Software, 2007, (38), pp.71-79.
DOI: 10.1016/j.advengsoft.2006.08.003
Google Scholar
[22]
H. Berk Celikoglu, H. Kerem Cigizoglu, Modelling public transport trips by radial basis function neural networks[J],. Mathematical and Computer Modelling, 2007, (45), pp.480-489.
DOI: 10.1016/j.mcm.2006.07.002
Google Scholar
[23]
D. Srinivasan, X. Jin, R. L. Cheu, Adaptive neural network models for automatic incident detection on freeways[J],. Neurocomputing, 2005, (64), pp.473-496.
DOI: 10.1016/j.neucom.2004.12.001
Google Scholar
[24]
C.H., Wei, Y. Lee, Sequential forecast of incident duration using artificial neural network models[J],. Accident Analysis and Prevention, 2007, (39), pp.944-954.
DOI: 10.1016/j.aap.2006.12.017
Google Scholar
[25]
Ray R. Hashemi, Louis A. Le Blanc, Conway T. Rucks, and Angela Shearry, A neural network for transportation safety modeling[J],. Expert Systems with Applications. 1995, Vol. 9, No. 3, pp.247-256.
DOI: 10.1016/0957-4174(95)00002-q
Google Scholar
[26]
B.M. Baker, M.A. Ayechew, A genetic algorithm for the vehicle routing problem[J],. Computers and Operations Research, 2003, 30 (5), 787–800.
DOI: 10.1016/s0305-0548(02)00051-5
Google Scholar
[27]
B. J., M. Barkaoui, A hybrid genetic algorithm for the capacitated vehicle routing problem[A],. Proceedings of the Genetic and Evolutionary Computation Conference[C], 2003, Chicago, p.646–656.
DOI: 10.1007/3-540-45105-6_80
Google Scholar
[28]
O. Gundogdu, M. Gokdag, F. Yuksel, A traffic noise prediction method based on vehicle composition using genetic algorithms [J],. Applied Acoustics, 2005, (66): 799-809.
DOI: 10.1016/j.apacoust.2004.11.003
Google Scholar
[29]
X.B. Hu, E. D. Paolo, An efficient genetic algorithm with uniform crossover for air traffic control[J],. Computer & Operations Research , 2009, (36): 245-259.
DOI: 10.1016/j.cor.2007.09.005
Google Scholar
[30]
B. Chu, D. Kim, D. Hong, et. al., GA-based fuzzy controller design for tunnel ventilation systems[J],. Automation in Construction, 2008, (17), pp.130-136.
DOI: 10.1016/j.autcon.2007.05.011
Google Scholar
[31]
F.Z. Zhang, X.J. Cao, D.K. Yang. Intelligent scheduling of public traffic vehicles based on a hybrid genetic algorithm [J]. Tsinghua Science and Technology, 2008, Vol. 13, No. 5, pp.625-631.
DOI: 10.1016/s1007-0214(08)70103-2
Google Scholar
[32]
S. Rahmani, S.M. Mousavi, M.J. Kamali, Modeling of road-traffic noise with the use of genetic algorithm[J],. Applied soft computing, 2010, In press.
DOI: 10.1016/j.asoc.2010.01.022
Google Scholar
[33]
H. Ceylan, Michael G.H. Bell, Traffic signal timing optimization based on genetic algorithm approach, including drivers'routing[J],. Transportation Research Part B, 2004, (38), pp.329-342.
DOI: 10.1016/s0191-2615(03)00015-8
Google Scholar
[34]
J. Kennedy, R. Eberhart, Particle swarm optimization,. Proceedings of 1995 IEEE International Conference on Neural Networks , 1995(4), p.1942–(1948).
Google Scholar
[35]
J. Kennedy, R.A. Eberhart, discrete binary version of the particle swarm algorithm,. Proceedings of 1997 IEEE International Conference on Systems, Man, and Cybernetics, 1997(5), p.4104–4108.
DOI: 10.1109/icsmc.1997.637339
Google Scholar
[36]
J. Kennedy, R. Eberhart, Y. Shi,. Swarm Intelligence. Morgan Kaufmann Publisher, San Francisco, (2001).
Google Scholar
[37]
Y. Marinakis, A hybrid particle swarm optimization algorithm for the vehicle routing problem[J],. Engineering Applications of Artificial Intelligence (2010), In Press, doi: 10. 1016/ j. engappai. 2010. 02. 002.
DOI: 10.1016/j.engappai.2010.02.002
Google Scholar
[38]
Y. Peng, H.Y. Zhu, Research on vehicle routing problem with stochastic demand and PSO-DP algorithm with inver-over operator[J],. System Engineering-Theory& Practice, 2008, Vol. 28, Issue 10, pp.76-81.
DOI: 10.1016/s1874-8651(10)60003-8
Google Scholar
[39]
J. M Hu, J.Y. Song, X. J Kang, M. Ch. Zhang. A study of Particle swarm optimization in urban traffic surveillance system[A],. IMACS Multiconference on CESA, Oct. 4-6, 2006, Beijing China, p.2056-(2061).
DOI: 10.1109/cesa.2006.313652
Google Scholar
[40]
X.R. Liang, Y. K Fan, T. Jiang. Application of PSO algorithm to coordinated ramp control[A],. Proceedings of the Eighth International Conference on Machine Learning and Cybernetics[C], Baoding, July, 2009, pp.1712-1716.
DOI: 10.1109/icmlc.2009.5212350
Google Scholar
[41]
Z. Qin, Z.P. Fan, X.D. Zang, H.W. Gong, Approach of intersection signal control based on PSO[A],. CCDC, 2009, pp.4221-4225.
Google Scholar
[42]
Y. Wei, Qing Shao, Y. Han and B.Q. Fan. Intersection signal control approach based on PSO and simulation[A],. The Second International Conference on Genetic and Evolutionary Computing[C], 2008, pp.277-280.
DOI: 10.1109/wgec.2008.124
Google Scholar
[43]
L.G. Zhang, Y. Y Zhong, Z.L. Li, Y.Z. Chen, PSO-based optimization for isolated intersections signal timing and simulation[A], the Proceedings of the 7th International Conference on Machine Learning and Cybernectics, Kunming, 12-15, July, 2008, pp.993-996.
DOI: 10.1109/icmlc.2008.4620549
Google Scholar
[44]
J. Chen, L.H. Xu, Road-junction traffic signal timing optimization by an adaptive particle swarm algorithm[A],. ICARCV[C], (2006).
DOI: 10.1109/icarcv.2006.345348
Google Scholar
[45]
A. W. Mohemmed, N. Ch. Sahoo, T. K. Geok. Sovling shortest path problem using particle swarm optimization[J],. Applied Soft Computing, 2008, (8), pp.1643-1653.
DOI: 10.1016/j.asoc.2008.01.002
Google Scholar