Abstract
The article introduces an integrated approach for the planning of urban rail transport system with the purpose of maintaining the high traffic density, ensuring the safety of trains, passenger comfortable service and properly use of resources. It explores the possibility of using genetic algorithms for all three types of planning the transportation process such as the planned train schedule, maintenance schedule and working schedule for locomotive crews. The main objective of the study is to improve an automated train planning system with the uniformity criterion by using a variety of resources under limited restrictions. The authors examined the possibility of genetic algorithm for the planning of urban rail transportation system by using various types of crossover, mutations and the influence of genetic algorithm parameters on the results of transportation planning process.
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References
Palte, M.H.A.: Smartfleet, How smart rail vehicles help improve business. In: 5th IET Conference on Railway Condition Monitoring and Non-Destructive Testing (RCM 2011), pp. 1–4. IET, Derby (2011)
Sauder, R.L., Westerman, W.M.: Computer aided train dispatching: decision support through optimization. Interfaces 6(13), 24–37 (1983)
Markevich, A.V., Sidorenko, V.G., Kulagin, M.A.: The influence of the human factor on the safety of train traffic. In: XXVII International Scientific Conference on Problems of Safety Management of Complex Systems, pp. 265–270. Institute of Management Problems V.A. Trapeznikova, Moscow (2019)
Zhao, J., Chan, A.H.C., Burrow, M.P.N.: A genetic-algorithm-based approach for scheduling the renewal of railway track components. Rail Rapid Transit 223, 533–541 (2009)
Ho, T.K., Tsang, C.W., Ip, K.H., Kwan, K.S.: Train service timetabling in railway open markets by particle swarm optimization. Expert Syst. Appl. 39, 861–868 (2012)
Turner, C., Tiwari, A., Starr, A., Blacktop, K.: A review of key planning and scheduling in the rail industry in Europe and UK. J. Rail Rapid Transp. 230(3), 984–998 (2016)
Davydov, B., Chebotarev, V., Kablucova, K.: The model of arrival time distribution forming in the dense vehicle flow. In: Grzegorz, S. (ed.) Integration as Solution for Advanced Smart Urban Transport Systems. AISC, vol. 1083, pp. 69–78. Springer, Cham (2019)
Schöbel, A., Maly, T.: Operational fault states in railways. Eur. Transp. Res. Rev. 4(2), 107–113 (2012). https://doi.org/10.1007/s12544-011-0068-z
Nemtinov, V., Nemtinova, Y., Borisenko, A., Mokrozub, V.: Information support of decision making in urban passenger transport management. Transp. Probl. 12(4), 83–90 (2017)
Oliskevych, M., Kovalyshyn, S., Magats, M., Shevchuk, V., Sukach, O.: The optimization of trucks fleet schedule in view of their interaction and restrictions of the European agreement of work of crews. Transp. Probl. 15(2), 157–170 (2020)
Zherbina, A.I.: Calculation on the computer schedule turnover of trains metro. Transp. MIIT 612, 100–105 (1978)
Sidorenko, V.G., Filipchenko, K.M.: The method of effective service planning with the application of graph theory. Informatization Educ. Sci. 4(28), 123–132 (2015)
Mitchell, M.: An introduction to genetic algorithms. MIT Press, London (1999)
Tormos, P., Lova, A., Barber, F., Ingolotti, L., Abril, M., Salido, M.A.: A genetic algorithm for railway scheduling problems. In: Xhafa, F., Abraham, A. (eds.) Metaheuristics for Scheduling in Industrial and Manufacturing Applications. Studies in Computational Intelligence, vol. 128, pp. 255–276. Springer, Berlin, Heidelberg (2008)
Sheng, S., Dechan, Z., Xiaofei, X.: Genetic algorithm for the transportation problem with discontinuous piecewise linear cost function. Int. J. Comput. Sci. Network Secur. 6(7(A)), 182–190 (2006)
Holland, J.H.: Adaptation in Natural and Artificial Systems. The MIT press, Cambridge, Massachusetts (1992)
Sidorenko, V.G., Aung, C.M., Alekseev, V.M., Rozenberg, E.N., Umanskii, V.I.: Planning electric-rolling-stock maintenance in conditions of limited resources. Russ. Electr. Eng. 88(12), 839–841 (2017)
Safronov, A.I.: Automated construction of the planned schedule for the movement of passenger subway trains. Vestnik 3, 99–104 (2012)
Seslavin, A.I.: Principles of uniformity in problems of control of passenger transport flows. Appl. Inf. 2(20), 91–95 (2009)
Kontsevich, M.L.: Uniform arrangements. Kvant 7, 51–59 (1985)
Goldenberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, New York (1989)
O’Connor, R.V., Elger, P., Clarke, P.M.: Continuous software engineering – a microservices architecture perspective. J. Software Evol. Process 29(11), 866 (2017)
Acknowledgments
The reported study was funded by RFBR, Sirius University of Science and Technology, JSC Russian Railways and Educational Fund “Talent and success”, project number 20-37-51001.
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Baranov, L.A., Sidorenko, V.G., Safronov, A.I., Aung, K.M. (2022). Application of Genetic Algorithms for the Planning of Urban Rail Transportation System. In: Macioszek, E., Sierpiński, G. (eds) Present Approach to Traffic Flow Theory and Research in Civil and Transportation Engineering. TSTP 2021. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-93370-8_2
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DOI: https://doi.org/10.1007/978-3-030-93370-8_2
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