Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/4857
Title: Modeling vehicle delays at signalized junctions: Artificial neural networks approach
Authors: Murat, Y Sazi
Baskan, Ozgur
Keywords: Artificial neural networks
Intersections
Signalization
Traffic flows
Vehicle delay model
Issue Date: Jul-2006
Publisher: CSIR
Abstract: Delay of vehicles at signalized junctions is one of the main criteria used for evaluation of control systems’ performances. The vehicle delay is uniform and non-uniform delay types. The uniform part consists of signal timings; the non-uniform part includes vehicle queuing, random arrivals and over-saturation cases of traffic flows. The uniform part of the vehicle delays is basically determined using conventional delay formulas. But for the non-uniform part, artificial neural network (ANN) approach is used and a vehicle delay estimation model [artificial neural network delay estimation of traffic flows (ANNDEsT)] is developed. ANNDEsT model compared with Webster, HCM and Akçelik delay calculation methods and field observations, shows encouraging results especially for the cases of over-saturation or non-uniform conditions.
Description: 558-564
URI: http://hdl.handle.net/123456789/4857
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.65(07) [July 2006]

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