Rail Degradation Prediction Models for Tram System: Melbourne Case Study

Tram is classified as a light rail mode of transportation. Tram tracks experience high acceleration and deceleration forces of locomotives and wagons within their service life and also share their route with other vehicles. This results in higher rates of degradation in tram tracks compared to the d...

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Bibliographic Details
Published in:Journal of advanced transportation 2018-01, Vol.2018 (2018), p.1-8
Main Authors: Hesami, Reyhaneh, Nazem, Majidreza, Moridpour, Sara, Falamarzi, Amir
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Case studies
Cognition & reasoning
Comparative analysis
Deceleration
Degradation
Dependent variables
Deviation
High acceleration
Independent variables
Infrastructure
Locomotives
Maintenance management
Neural networks
Prediction models
Preventive maintenance
Public transportation
Railway tracks
Regression analysis
Regression models
Service life
Standard deviation
Studies
Variables
Wagons
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Summary:Tram is classified as a light rail mode of transportation. Tram tracks experience high acceleration and deceleration forces of locomotives and wagons within their service life and also share their route with other vehicles. This results in higher rates of degradation in tram tracks compared to the degradation rate in heavy rail tracks. In this research, gauge deviation is employed as a representative of track geometry irregularities for the predication of the tram track degradation. Data sets used in this research were sourced from Melbourne’s tram system. For model development, the data of approximately 250 km of tram tracks are used. Two different models including a regression model and an Artificial Neural Networks (ANN) model have been applied for predicting tram track gauge deviation. According to the results, the performances of the regression models are similar to the ANN models. The determination coefficients of the developed models are above 0.7.
ISSN:0197-6729
2042-3195
DOI:10.1155/2018/6340504