The second part of the framework of a traffic prediction model proposed in a previous paper is presented. The model first eliminates the noise caused by random travel conditions by using a wavelet denoising method, the topic of the earlier paper. The model then quantitatively calculates the influence of special factors by using a fuzzy-neural network.
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References
1.
AdeliH., and KarimA.. Fuzzy-Wavelet RBFNN Model for Freeway Incident Detection. Journal of Transportation Engineering, Vol. 126, No. 6, 2000, pp. 464–471.
2.
IshakS. S., and Al-DeekH. M.. Fuzzy ART Neural Network Model for Automated Detection of Freeway Incidents. In Transportation Research Record 1634, TRB, National Research Council, Washington, D.C., 1998, pp. 56–63.
3.
XuH., KwanC. M., HaynesL., and PryorJ. D.. Real-Time Adaptive Online Traffic Incident Detection. Proc., IEEE International Symposium on Intelligent Control Proceedings, Piscataway, N.J., 1996, pp. 200–205.
4.
AdorniG., CagnoniS., GoriM., and MordoniniM.. Access Control System with Neuro-Fuzzy Supervision. Proc., IEEE Conference on Intelligent Transportation Systems, 2001, pp. 472–477.
5.
ChouJ., O’NeillW. A., and ChengH.. Pavement Distress Evaluation Using Fuzzy Logic and Moment Invariants. In Transportation Research Record 1505, TRB, National Research Council, Washington, D.C., 1995, pp. 39–46.
6.
HayashiK., ShimizuY., DoteY., TakayamaA., and HirakoA.. Neuro-Fuzzy Transmission Control for Automobile with Variable Loads. IEEE Transactions on Control Systems Technology, Vol. 3, No. 1, 1995, pp. 49–52.
7.
JouI.-C., ChangC.-J., and ChenH.-K.. Hybrid Neuro-Fuzzy System for Adaptive Vehicle Separation Control. Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, Vol. 21, No. 1, 1999, pp. 15–29.
8.
LiuJ. N. K., and SinK. Y.. Fuzzy Neural Networks for Machine Maintenance in Mass Transit Railway System. IEEE Transactions on Neural Networks, Vol. 8, No. 4, 1997, pp. 932–941.
9.
SayedT., and RazaviA.. Comparison of Neural and Conventional Approaches to Mode Choice Analysis. Journal of Computing in Civil Engineering, Vol. 14, No. 1, 2000, pp. 23–30.
10.
TaylorC., and MeldrumD.. Freeway Traffic Data Prediction via Artificial Neural Networks for Use in a Fuzzy Logic Ramp Metering Algorithm. Proc., Intelligent Vehicles Symposium, IEEE, Piscataway, N.J., 1994, pp. 308–313.
11.
VukadinovicK., TeodorovicD., and PavkovicG.. Application of Neurofuzzy Modeling: The Vehicle Assignment Problem. European Journal of Operational Research, Vol. 114, No. 3, 1999, pp. 474–488.
12.
DoughertyM., KirbyH., and BoyceR.. Using Neural Networks to Recognize, Predict, and Model Traffic. In Artificial Intelligence Applications to Traffic Engineering (BielliM., AmbrosinoG., and BoeroM., eds.), VSP, Zeist, Netherlands, 1994, pp. 235–250.
13.
LedouxC.An Urban Traffic Flow Model Integrating Neural Network. Transportation Research C, Vol. 5, 1997, pp. 287–300.
14.
YinH., WongS. C., XuJ., and WongC. K.. Urban Traffic Flow Prediction Using a Fuzzy-Neural Approach. Transportation Research C, Vol. 10, No. 2, 2002, pp. 85–98.
15.
YuanZ., LiW., and LiuH.. Forecast of Dynamic Traffic Flow. Proc., Conference on Traffic and Transportation Studies, ASCE, Reston, Va., 2000, pp. 507–512.
16.
NguyenD., and WidrowB.. The Truck Backer-Upper: An Example of Self-Learning in Neural Networks. Proc., International Joint Conference on Neural Networks, Washington, D.C., Vol. 2, 1989, IEEE, pp. 357–363.
17.
XiaoH., SunH., RanB., and OhY.. Fuzzy-Neural Network Traffic Prediction Framework with Wavelet Decomposition. In Transportation Research Record: Journal of the Transportation Research Board, No. 1836, TRB, National Research Council, Washington, D.C., 2003, pp. 16–20.
