Please use this identifier to cite or link to this item:
|Title:||Computational intelligence-based congestion prediction for a dynamic urban street network|
|Authors:||Srinivasan, D. |
Wai Chan, C.
Traffic flow prediction
|Citation:||Srinivasan, D., Wai Chan, C., Balaji, P.G. (2009-06). Computational intelligence-based congestion prediction for a dynamic urban street network. Neurocomputing 72 (10-12) : 2710-2716. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neucom.2009.01.005|
|Abstract:||This paper develops a hybrid model for single point short term traffic flow forecasting in an urban traffic network. The hybrid model consists of two main modules: a fuzzy input fuzzy output filter (FIFO-filter) and a multi-layer feed-forward artificial neural network architecture optimized using evolution strategies (MLFN-ES). The FIFO-filter performs the data clustering operation and provides a rough forecasted prediction value based on the input data to the MLFN-ES associated with each cluster for modeling the input-output relation to provide accurate short term forecast value. The performance of the proposed model is demonstrated by predicting the traffic flow for an intersection in the central business district (CBD) area of Singapore. The hybrid model proposed in this paper gave a mean absolute percentage error (MAPE) of 8.35% on weekdays and 9.73% on weekends for the test data. A comparison analysis shows improved performance of the proposed hybrid method in short term traffic prediction over popular approaches like ARIMA and artificial neural network based systems. © 2009 Elsevier B.V. All rights reserved.|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jul 17, 2018
WEB OF SCIENCETM
checked on Jun 27, 2018
checked on May 5, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.