Research Article
BibTex RIS Cite

Year 2022, Volume: 5 Issue: 1, 62 - 70, 30.04.2022

Mehmet Taciddin Akçay , Abdurrahim Akgundogdu

https://doi.org/10.35377/saucis...932969

Abstract

References

  • [1] Chen, Y., Rilett, L.R. A train speed measurement and arrival time prediction system for highway-rail grade crossings,  Transportation Research Record Journal of the Transportation Research Board, 2017, 2608(1) : 96-104.
  • [2] Ogawa, T., Manabe, S., Yoshikawa, G., Imamura, Y., Kageyama, M. Method of Calculating Running Resistance by the Use of the Train Data Collection Device. Quarterly Report of RTRI, 2017, 58. 21-27.
  • [3] Cosgriff, K., N., Berggren, E., G., Kaynia, A., M., Dam, N., N., Mortensen, N. A new method for estimation of critical speed for railway tracks on soft ground, International Journal of Rail Transportation, 2018, 6:4, 203-217,
  • [4] Hensel, S., Marinov, M. (2014). Time Signal Based Warping Algorithms for Low Speed Velocity Estimation of Rail Vehicles. Annual Journal of Electronics, 2014, 8. 177- 180.
  • [5] Xu, G., Li, F., Long, J., Han, D. Train movement simulation by element increment method. Journal of advanced transportation, 2017, 50: 2060–2076.
  • [6] Lv, Z., Xu, J., Zheng, K., Yin, H., Zhao, P., Zhou, X., “LC-RNN: A Deep Learning Model for Traffic Speed Prediction”, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), July, 2018, Stockholm, Sweden, 3470-3476.
  • [7] Dhamaniya, A., Chandra, S. “Speed Prediction Models for Urban Arterials under Mixed Traffic Conditions”, Procedia - Social and Behavioral Sciences 104 ( 2013 ) 342 – 351.
  • [8] Aradi, S., Becsi, T., Gaspar, P. Estimation of running resistance of electric trains based on on-board telematics system. International Journal of Heavy Vehicle Systems, 2015, 22. 277-291.
  • [9] Gmira, M., Gendreau, M., Lodi, A., Potvin, J. “Travel Speed Prediction Based on Learning Methods For Home Delivery”, Interuniversity Research Center On Business Networks, logistics and transport, CIRRELT, 2018, 1-34.
  • [10] Bysveen, M., “Vehicle speed prediction models for consideration of energy demand within road design”, Norwegian University of Science and Technology, Civil and Environmental Engineering, Master’s Thesis, 2017.
  • [11] Mirbaha, B., Saffarzadeh, M., Beheshty, S., A., Aniran, M., Yazdani, M., Shirini, B. (2017). Predicting Average Vehicle Speed in Two Lane Highways Considering Weather Condition and Traffic Characteristics. IOP Conference Series: Materials Science and Engineering, 1-7.
  • [12] Gmira, M., Gendreau, M., Lodi, A., Potvin, J. Travel speed prediction using machine learning techniques, ITS World Congress 2017 Montreal, October,1-10.
  • [13] Naye, E. Real-time arrival prediction models for light rail train systems, Royal Instistute of Technology, Department of Engineering, Master’s Thesis, 2014.
  • [14] Cats, O. Real-Time Predictions for Light Rail Train Systems, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 8-11 Oct., 2014, Qingdao, China, 1-10.
  • [15] Kouroussis, G., Connolly, D.P., Forde, M., Verlinden, O. Train speed calculation using ground vibrations', Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2015, 229 (5), 466-483.
  • [16] David A. Freedman (2009). Statistical Models: Theory and Practice. Cambridge University Press. p. 26. A simple regression equation has on the right hand side an intercept and an explanatory variable with a slope coefficient. A multiple regression e right hand side, each with its own slope coefficient.
  • [17] Rencher, Alvin C.; Christensen, William F. (2012), "Chapter 10, Multivariate regression – Section 10.1, Introduction", Methods of Multivariate Analysis, Wiley Series in Probability and Statistics, 709 (3rd ed.), John Wiley & Sons, p. 19.
  • [18] https://www.analyticsvidhya.com/blog/2020/05/decision-tree-vs-random-forest-algorithm/]
  • [19] Ho, Tin Kam (1995). Random Decision Forests (PDF). Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14–16 August 1995. pp. 278–282.
  • [20] Breiman L (2001). "Random Forests". Machine Learning. 45 (1): 5–32.
  • [21] J. A. Freeman ve D. M. Skapura, “Neural Networks Algorithms”, Applications and Programming Techniques. New York, USA: Addison-Wesley Publishing Company,1991.
  • [22] Garret GJ, Wu C, “Knowledge‐based modeling of material behavior with neural networks”. Journal of Engineering Mechanics, 117(1), 132-153, 1991.
  • [23] Cover, Thomas M.; Hart, Peter E. (1967). "Nearest neighbor pattern classification". IEEE Transactions on Information Theory. 13 (1): 21–27. CiteSeerX 10.1.1.68.2616. doi:10.1109/TIT.1967.1053964.
  • [24] Walters-Williams, J., & Li, Y., Comparative study of distance functions for nearest neighbors. In Advanced Techniques in Computing Sciences and Software Engineering (pp. 79-84). Springer, Dordrecht, 2010.

Calculation of Driving Parameters for GOA4 Signaling System using Machine Learning Methods

Year 2022, Volume: 5 Issue: 1, 62 - 70, 30.04.2022

Mehmet Taciddin Akçay , Abdurrahim Akgundogdu

https://doi.org/10.35377/saucis...932969

Abstract

Among the electromechanical components of the rail system, the rail system vehicle is one of the most important units that carrying the passenger load. In terms of the efficiency of the signalization system, it is very critical to create the optimum vehicle driving profile. While many parameters of the vehicle come into play while designing the driving profile, determining the acceleration and braking accelerations directly affects this characteristic. With the developing technology in rail transportation systems, the use of programmable devices and software instead of human factors is becoming more widespread day by day. Among the software used, artificial intelligence and machine learning applications constitute a large share in the general distribution. Especially if driverless (GOA4) signaling systems are preferred, these software become more important. In this study, the estimation of Vehicle Acceleration and Braking Acceleration with travel time has been carried out by using Machine Learning Methods. The ideal results obtained were given comparatively and interpreted on the graphics.

Keywords

Acceleration braking driverless machine learning vehicle.

References

  • [1] Chen, Y., Rilett, L.R. A train speed measurement and arrival time prediction system for highway-rail grade crossings,  Transportation Research Record Journal of the Transportation Research Board, 2017, 2608(1) : 96-104.
  • [2] Ogawa, T., Manabe, S., Yoshikawa, G., Imamura, Y., Kageyama, M. Method of Calculating Running Resistance by the Use of the Train Data Collection Device. Quarterly Report of RTRI, 2017, 58. 21-27.
  • [3] Cosgriff, K., N., Berggren, E., G., Kaynia, A., M., Dam, N., N., Mortensen, N. A new method for estimation of critical speed for railway tracks on soft ground, International Journal of Rail Transportation, 2018, 6:4, 203-217,
  • [4] Hensel, S., Marinov, M. (2014). Time Signal Based Warping Algorithms for Low Speed Velocity Estimation of Rail Vehicles. Annual Journal of Electronics, 2014, 8. 177- 180.
  • [5] Xu, G., Li, F., Long, J., Han, D. Train movement simulation by element increment method. Journal of advanced transportation, 2017, 50: 2060–2076.
  • [6] Lv, Z., Xu, J., Zheng, K., Yin, H., Zhao, P., Zhou, X., “LC-RNN: A Deep Learning Model for Traffic Speed Prediction”, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), July, 2018, Stockholm, Sweden, 3470-3476.
  • [7] Dhamaniya, A., Chandra, S. “Speed Prediction Models for Urban Arterials under Mixed Traffic Conditions”, Procedia - Social and Behavioral Sciences 104 ( 2013 ) 342 – 351.
  • [8] Aradi, S., Becsi, T., Gaspar, P. Estimation of running resistance of electric trains based on on-board telematics system. International Journal of Heavy Vehicle Systems, 2015, 22. 277-291.
  • [9] Gmira, M., Gendreau, M., Lodi, A., Potvin, J. “Travel Speed Prediction Based on Learning Methods For Home Delivery”, Interuniversity Research Center On Business Networks, logistics and transport, CIRRELT, 2018, 1-34.
  • [10] Bysveen, M., “Vehicle speed prediction models for consideration of energy demand within road design”, Norwegian University of Science and Technology, Civil and Environmental Engineering, Master’s Thesis, 2017.
  • [11] Mirbaha, B., Saffarzadeh, M., Beheshty, S., A., Aniran, M., Yazdani, M., Shirini, B. (2017). Predicting Average Vehicle Speed in Two Lane Highways Considering Weather Condition and Traffic Characteristics. IOP Conference Series: Materials Science and Engineering, 1-7.
  • [12] Gmira, M., Gendreau, M., Lodi, A., Potvin, J. Travel speed prediction using machine learning techniques, ITS World Congress 2017 Montreal, October,1-10.
  • [13] Naye, E. Real-time arrival prediction models for light rail train systems, Royal Instistute of Technology, Department of Engineering, Master’s Thesis, 2014.
  • [14] Cats, O. Real-Time Predictions for Light Rail Train Systems, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 8-11 Oct., 2014, Qingdao, China, 1-10.
  • [15] Kouroussis, G., Connolly, D.P., Forde, M., Verlinden, O. Train speed calculation using ground vibrations', Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2015, 229 (5), 466-483.
  • [16] David A. Freedman (2009). Statistical Models: Theory and Practice. Cambridge University Press. p. 26. A simple regression equation has on the right hand side an intercept and an explanatory variable with a slope coefficient. A multiple regression e right hand side, each with its own slope coefficient.
  • [17] Rencher, Alvin C.; Christensen, William F. (2012), "Chapter 10, Multivariate regression – Section 10.1, Introduction", Methods of Multivariate Analysis, Wiley Series in Probability and Statistics, 709 (3rd ed.), John Wiley & Sons, p. 19.
  • [18] https://www.analyticsvidhya.com/blog/2020/05/decision-tree-vs-random-forest-algorithm/]
  • [19] Ho, Tin Kam (1995). Random Decision Forests (PDF). Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14–16 August 1995. pp. 278–282.
  • [20] Breiman L (2001). "Random Forests". Machine Learning. 45 (1): 5–32.
  • [21] J. A. Freeman ve D. M. Skapura, “Neural Networks Algorithms”, Applications and Programming Techniques. New York, USA: Addison-Wesley Publishing Company,1991.
  • [22] Garret GJ, Wu C, “Knowledge‐based modeling of material behavior with neural networks”. Journal of Engineering Mechanics, 117(1), 132-153, 1991.
  • [23] Cover, Thomas M.; Hart, Peter E. (1967). "Nearest neighbor pattern classification". IEEE Transactions on Information Theory. 13 (1): 21–27. CiteSeerX 10.1.1.68.2616. doi:10.1109/TIT.1967.1053964.
  • [24] Walters-Williams, J., & Li, Y., Comparative study of distance functions for nearest neighbors. In Advanced Techniques in Computing Sciences and Software Engineering (pp. 79-84). Springer, Dordrecht, 2010.
There are 24 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Mehmet Taciddin Akçay 0000-0002-1050-4566

Abdurrahim Akgundogdu 0000-0001-8113-0277

Publication Date April 30, 2022
Submission Date May 5, 2021
Acceptance Date March 10, 2022
Published in Issue Year 2022Volume: 5 Issue: 1

Cite

IEEE M. T. Akçay and A. Akgundogdu, “Calculation of Driving Parameters for GOA4 Signaling System using Machine Learning Methods”, SAUCIS, vol. 5, no. 1, pp. 62–70, 2022, doi: 10.35377/saucis...932969.
 Download Cover Image

29070    The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License