Predesign cost estimation of urban railway projects with parametric modeling

Abstract

This paper presents a parametric modeling method for predesign cost estimation of urban railway systems. Data of 13 light rail and metro projects located in Turkey were compiled for quantification of the impacts of parameters on the project costs. Parametric models have been developed using regression analysis and neural networks techniques. Ten linear regression models were used for determination of the parameters significantly impacting cost of urban railway projects. Two neural networks were considered as an alternative to regression models, particularly for the identification of the non‐linear relations. Predictive behaviour and performance of the models were compared to determine a model that presents adequate relations and has a reasonable accuracy. The proposed method provides a powerful approach for determination of a satisfactory parametric cost model during early project stages by incorporating a coordinated use of regression analysis and neural network techniques.

Santrauka

Pateiktas miesto gelezinkelru sistemu priesprojektiniu islaidu skaičiavimo metodas, pagristas parametriniu modeliavimu. Parametru itaka projekto išlaidoms nustatyta išnagrinejus 13 nedidelru gelezinkelru ir metro projektu Turkijoje. Pa‐rametriniai modeliai sudaryti taikane regresine analize bei dirbtinio intelekto tinklus. Parametru itakos miestu geležinkeliu projektavimo išlaidoms reikšmingumui nustatyti sudaryta 10 tiesines regresijos modeliu. Kaip alternatyva regresijos mo‐deliams sudaryti 2 neuroniniai tinklai, ypač nagrinejant netiesines priklausomybes. Pasiulytais modeliais gauti rezultatai palyginti tarpusavyje, siekiant nustatyti adekvačias priklausomybes ir uztikrinti reikiama tiksluma. Pasiūlytas metodas leidžia sukurti parametrini projekto islaidu modeli, ankstyvojoje priesprojektineje stadijoje. Tai pasiekta suderintai panau‐dojus regresine analize ir dirbtinio intelekto tinklus.

Keywords:

• predesign cost estimation
• urban railway projects
• regression analysis
• neural networks

Reikšminiai žodžiai::

• priešprojektiniu išlaidu skaičiavimas
• miesto geležinkeliu projektai
• regresine analize
• neuroniniai tinklai