Short term traffic flow prediction based on neuro-fuzzy hybrid sytem
作者： Minal Deshpande , Preeti R. Bajaj
DOI: 10.1109/ICTBIG.2016.7892699
关键词:
摘要: As traffic demand is growing rapidly, congestion has become a severe problem everywhere in the world. Accurate prediction of flow therefore essential for transport users to make smarter and effective decisions like route guidance, mode transport, time travel etc. Short term one important research fields intelligent transportation system (ITS) forms crucial base management, guidance control strategy. This work suggests application neuro-fuzzy hybrid which brings together complementary capabilities both neural networks fuzzy logic short application. The objective improve accuracy. Data from highway Chennai, India used analysis. use results satisfactory improvement performance measure.
sci-hub.se 参考文章(11)
Antony Stathopoulos, Loukas Dimitriou, Theodore Tsekeris, Fuzzy Modeling Approach for Combined Forecasting of Urban Traffic Flow Computer-aided Civil and Infrastructure Engineering. ,vol. 23, pp. 521- 535 ,(2008) , 10.1111/J.1467-8667.2008.00558.X
Yi-shan Gong, Yi Zhang, Research of Short-Term Traffic Volume Prediction Based on Kalman Filtering international conference on intelligent networks and intelligent systems. pp. 99- 102 ,(2013) , 10.1109/ICINIS.2013.32
Steven I-Jy Chien, Yuqing Ding, Chienhung Wei, Dynamic Bus Arrival Time Prediction with Artificial Neural Networks Journal of Transportation Engineering-asce. ,vol. 128, pp. 429- 438 ,(2002) , 10.1061/(ASCE)0733-947X(2002)128:5(429)
Eleni I. Vlahogianni, Enhancing Predictions in Signalized Arterials with Information on Short-Term Traffic Flow Dynamics Journal of Intelligent Transportation Systems. ,vol. 13, pp. 73- 84 ,(2009) , 10.1080/15472450902858384
Brian L. Smith, Michael J. Demetsky, TRAFFIC FLOW FORECASTING: COMPARISON OF MODELING APPROACHES Journal of Transportation Engineering-asce. ,vol. 123, pp. 261- 266 ,(1997) , 10.1061/(ASCE)0733-947X(1997)123:4(261)
Daniel Billings, Jiann-Shiou Yang, Application of the ARIMA Models to Urban Roadway Travel Time Prediction - A Case Study systems, man and cybernetics. ,vol. 3, pp. 2529- 2534 ,(2006) , 10.1109/ICSMC.2006.385244
William H. K. Lam, Y. F. Tang, Mei-Lam Tam, Comparison of two non‐parametric models for daily traffic forecasting in Hong Kong Journal of Forecasting. ,vol. 25, pp. 173- 192 ,(2006) , 10.1002/FOR.984
C. Quek, M. Pasquier, B.L.B. Lim, POP-TRAFFIC: a novel fuzzy neural approach to road traffic analysis and prediction IEEE Transactions on Intelligent Transportation Systems. ,vol. 7, pp. 133- 146 ,(2006) , 10.1109/TITS.2006.874712
Xinyu Min, Jianming Hu, Qi Chen, Tongshuai Zhang, Yi Zhang, Short-term traffic flow forecasting of urban network based on dynamic STARIMA model international conference on intelligent transportation systems. pp. 1- 6 ,(2009) , 10.1109/ITSC.2009.5309741
Dehuai Zeng, Jianmin Xu, Jianwei Gu, Liyan Liu, Gang Xu, Short Term Traffic Flow Prediction Based on Online Learning SVR international conference on power electronics and intelligent transportation system. pp. 616- 620 ,(2008) , 10.1109/PEITS.2008.134