Open Access
MATEC Web Conf.
Volume 259, 2019
2018 6th International Conference on Traffic and Logistic Engineering (ICTLE 2018)
Article Number 02007
Number of page(s) 5
Section Intelligent Transportation and Management
Published online 25 January 2019
  1. Redman, L., Friman, M., Gärling, T., & Hartig, T. (2013). Quality attributes of public transport that attract car users: A research review. Transport Policy, 25, 119–127. [CrossRef] [Google Scholar]
  2. D Knowles, R., & Ferbrache, F. (2016). Evaluation of wider economic impacts of light rail investment on cities. Journal of Transport Geography, 54, 430–439. [CrossRef] [Google Scholar]
  3. Gadziński, J., & Radzimski, A. (2016). The first rapid tram line in Poland: How has it affected travel behaviours, housing choices and satisfaction, and apartment prices? Journal of Transport Geography, 54, 451–463. [CrossRef] [Google Scholar]
  4. Ahac, M., & Lakusic, S. (2017). Track Gauge Degradation Modelling on Small Urban Rail Networks: Zagreb Tram System Case Study. Urban Transport Systems, (January). [Google Scholar]
  5. He, Q., Li, H., Bhattacharjya, D., Parikh, D. P., & Hampapur, A. (2015). Track geometry defect rectification based on track deterioration modelling and derailment risk assessment. Journal of the Operational Research Society, 66(3), 392–404. [CrossRef] [Google Scholar]
  6. Andrade, A. R., & Teixeira, P. F. (2018). Assessing Temporary Speed Restrictions and Associated Unavailability Costs in Railway Infrastructure. International Journal of Civil Engineering, 16(2), 219–228. [CrossRef] [Google Scholar]
  7. Andrews, J. (2012). A modelling approach to railway track asset management. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 227(1), 56–73. [CrossRef] [Google Scholar]
  8. An, R., Sun, Q., Wang, F., Bai, W., Zhu, X., & Liu, R. (2018). Improved Railway Track Geometry Degradation Modeling for Tamping Cycle Prediction, 144(2015), 1–11. [Google Scholar]
  9. Asada, T., Roberts, C., & Koseki, T. (2013). An algorithm for improved performance of railway condition monitoring equipment: Alternating-current point machine case study. Transportation Research Part C: Emerging Technologies, 30, 81–92. [CrossRef] [Google Scholar]
  10. Ebrahimi, A., Tinjum, J. M., & Edil, T. B. (2014). Maintenance model for railway substructure. Geotechnical Engineering, 45(1), 48–55. [Google Scholar]
  11. Cárdenas-Gallo, I., Sarmiento, C. A., Morales, G. A., Bolivar, M. A., & Akhavan-Tabatabaei, R. (2017). An ensemble classifier to predict track geometry degradation. Reliability Engineering and System Safety, 161 (April 2016), 53–60. [CrossRef] [Google Scholar]
  12. PTV. (2017). Public Transport Victoria Annual Report. Melbourne. [Google Scholar]
  13. Grafarend, E. W. (2006). Linear and Nonlinear Models?: Fixed Effects, Random Effects, and Mixed Models. Berlin: De Gruyter. [Google Scholar]
  14. Gordon, S. (2006). The Normal Distribution. University of Sydney. Sydney. [Google Scholar]
  15. Jiang, X., Zhang, L., & Chen, M. X. (2014). Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China. Transportation Research Part C: Emerging Technologies, 44, 110–127. [CrossRef] [Google Scholar]
  16. Li, Q., Shi, Z., Zhang, H., Tan, Y., Ren, S., Dai, P., & Li, W. (2018). A cyber-enabled visual inspection system for rail corrugation. Future Generation Computer Systems, 79, 374–382. [CrossRef] [Google Scholar]
  17. Ben-hur, A., & Weston, J. (2010). Data Mining Techniques for the Life Sciences, 609, 223–239. [CrossRef] [Google Scholar]
  18. Li, X. Z., & Kong, J. M. (2014). Application of GA-SVM method with parameter optimization for landslide development prediction. Natural Hazards and Earth System Sciences, 14(3), 525–533. [CrossRef] [Google Scholar]
  19. Zhang, Z., Qin, Y., Cheng, X., Zhu, L., Kou, L., Li, J., & Sun, F. (2015). Metro Station Safety Status Prediction Based on GA-SVR. In International Conference on Electrical and Information Technologies for Rail Transportation (pp. 57–69). Berlin: Springer. [Google Scholar]
  20. Andrade, A. R., & Teixeira, P. F. (2015). Statistical modelling of railway track geometry degradation using Hierarchical Bayesian models. Reliability Engineering and System Safety, 142, 169–183. [CrossRef] [Google Scholar]
  21. Moridpour, S., Mazloumi, E., & Hesami, R. (2017). Application of Artificial Neural Networks in Predicting the Degradation of Tram Tracks Using Maintenance Data. In Applied Big Data Analytics in Operations Management (pp. 30–54). Delhi, India: IGI Global. [CrossRef] [Google Scholar]
  22. Falamarzi, A., Moridpour, S., Nazem, M., & Hesami, R. (2018). Rail Degradation Prediction Models for Tram System: Melbourne Case Study. Journal of Advanced Transportation, 2018, 8. [CrossRef] [Google Scholar]

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