Please use this identifier to cite or link to this item:
Title: Soft computing and regression modelling approaches for link-capacity functions
Authors: Koşun, Çağlar
Tayfur, Gökmen
Çelik, Hüseyin Murat
Keywords: Artificial neural networks
Flow rate
Link capacities
Regression analysis
Travel time
Traffic control
Issue Date: 2016
Publisher: Czech Technical University in Prague
Source: Koşun, Ç., Tayfur, G., and Çelik, H. M. (2016). Soft computing and regression modelling approaches for link-capacity functions. Neural Network World, 26(2), 129-140. doi:10.14311/NNW.2016.26.007
Abstract: Link-capacity functions are the relationships between the fundamental traffic variables like travel time and the flow rate. These relationships are important inputs to the capacity-restrained traffic assignment models. This study investigates the prediction of travel time as a function of several variables V/C (flow rate/capacity), retail activity, parking, number of bus stops and link type. For this purpose, the necessary data collected in Izmir, Turkey are employed by Artificial Neural Networks (ANNs) and Regression-based models of multiple linear regression (MLR) and multiple non-linear regression (MNLR). In ANNs modelling, 70% of the whole dataset is randomly selected for the training, whereas the rest is utilized in testing the model. Similarly, the same training dataset is employed in obtaining the optimal values of the coefficients of the regression-based models. Although all of the variables are used in the input vector of the models to predict the travel time, the most significant independent variables are found to be V/C and retail activity. By considering these two significant input variables, ANNs predicted the travel time with the correlation coefficient R = 0:87 while this value was almost 0.60 for the regression-based models.
ISSN: 1210-0552
Appears in Collections:City and Regional Planning / Şehir ve Bölge Planlama
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File Description SizeFormat 
5943.pdfMakale497.75 kBAdobe PDFThumbnail
Show full item record

CORE Recommender


checked on Feb 16, 2024


checked on Jan 20, 2024

Page view(s)

checked on Feb 19, 2024


checked on Feb 19, 2024

Google ScholarTM



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