OpenstarTs >
EUT-Periodici >
European Transport / Trasporti Europei >
European Transport / Trasporti Europei (2012) 52 >

Please use this identifier to cite or link to this item:

Title: Neural network based vehicle-following model for mixed traffic conditions
Authors: Mathew, Tom V.
Ravishankar, K.V.R.
Keywords: Car-following behaviour
neural network
macroscopic simulation.
Issue Date: Apr-2012
Publisher: EUT Edizioni Università di Trieste
Citation: Mathew, T.V., Ravishankar, K.V.R. (2012) "Neural network based vehicle-following model for mixed traffic conditions", European Transport / Trasporti Europei, 52
Series/Report no.: European Transport / Trasporti Europei
Abstract: Car-following behaviour is well studied and analyzed in the last fifty years for homogeneous traffic. However in the mixed traffic, following behaviour is found to vary based on type of lead and following vehicles. In this study, a neural network based model is proposed to predict the following behaviour for different lead and following vehicle-type combinations. Performance of the model is studied using data collected for six vehicle-type combinations. A multi-layer feed-forward back propagation network is used to predict vehicle-type dependent following behaviour by incorporating the vehicle- type as input into the model. The neural network model is then integrated into a simulation program to study the macroscopic behaviour of the model. Performance of the proposed neural network model is compared with the conventional Gipps‟ model at microscopic and macroscopic level. This study prompts the need for considering vehicle-type dependent following behaviour and ability of neural networks to model this behaviour in mixed traffic conditions.
ISSN: 1825-3997
Appears in Collections:European Transport / Trasporti Europei (2012) 52

Files in This Item:

File Description SizeFormat
ET_2012_52_1 - Mathew_et_al.pdf516.66 kBAdobe PDFView/Open
View Statistics

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.