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|Title:||Modeling vehicle delays at signalized junctions: Artificial neural networks approach|
|Authors:||Murat, Y Sazi|
|Keywords:||Artificial neural networks;Intersections;Signalization;Traffic flows;Vehicle delay model|
|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.|
|ISSN:||0975-1084 (Online); 0022-4456 (Print)|
|Appears in Collections:||JSIR Vol.65(07) [July 2006]|
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