-
آرشیو :
بهار 1400
-
نوع مقاله :
پژوهشی
-
کد پذیرش :
1382
-
موضوع :
مهندسی عمران
-
نویسنده/گان :
میلاد نظری، علی عبدی، حسن ذوقی
-
کلید واژه :
تهران، مترو، سرفاصله زمانی، برنامه¬زمانبندی، تئوری صف، زمان انتظار.
-
Title :
Schedule trains based on passenger demand using queue theory
-
Abstract :
The train has become a necessity as a means of traffic, a heavy and important means of public transportation in the world today. The need for traffic management and planning is inevitable. The goal of train scheduling is to minimize train travel time from origin to destination, to satisfy passengers and stakeholders, to reduce delays at stations, and to maximize the capacity of lines, stations, and fleets. In this research, a case study is an urban train between Tehran and Karaj. Using the M / G / C / C queue theory, a schedule for training guidance based on three scenarios is proposed. In fact, using uncertainty in time and capacity, the train is scheduled to move. Another parameter is the passenger waiting time. Passenger satisfaction depends on the short waiting time as well as enough space to board the train. The company's profit also depends on these two parameters, of course, at different times, passenger demand is different, so for various hours, a guide must be set. The recommended goal is between 6-30 minutes in multiple scenarios for different hours.
-
key words :
Tehran, Metro, headway, Time table, queue theory, waiting time
-
مراجع :
[1] Goh, C.J. and A.I. Mees, Optimal control on a graph with application to train scheduling problems. Mathematical and Computer Modelling, 1991. 15(2): p. 49-58.
[2] Miwa, M. and T. Oyama, An optimal track maintenance scheduling model analysis taking the risk of accidents into consideration. International Transactions in Operational Research, 2018. 25(5): p. 1465-1490.
[3] Caprara, A., M. Fischetti, and P. Toth, Modeling and solving the train timetabling problem. Operations research, 2002. 50(5): p. 851-861.
[4] Higgins, A., E. Kozan, and L. Ferreira, Optimal scheduling of trains on a single line track. Transportation research part B: Methodological, 1996. 30(2): p. 147-161.
[5] Lee, Y. and C.-Y. Chen, A heuristic for the train pathing and timetabling problem. Transportation Research Part B: Methodological, 2009. 43(8-9): p. 837-851.
[6] Çelebi, D., B. Bolat, and D. Bayraktar. Light rail passenger demand forecasting by artificial neural networks. in Computers & Industrial Engineering, 2009. CIE 2009. International Conference on. 2009. IEEE.
[7] Dündar, S. and İ. Şahin, Train re-scheduling with genetic algorithms and artificial neural networks for single-track railways. Transportation Research Part C: Emerging Technologies, 2013. 27: p. 1-15.
[8] Adenso-Dıaz, B., M.O. González, and P. González-Torre, On-line timetable re-scheduling in regional train services. Transportation Research Part B: Methodological, 1999. 33(6): p. 387-398.
[9] Marković, N., et al., Analyzing passenger train arrival delays with support vector regression. Transportation Research Part C: Emerging Technologies, 2015. 56: p. 251-262.
[10] Lai, Y.-C., Y.-A. Huang, and H.-Y. Chu, Estimation of rail capacity using regression and neural network. Neural Computing and Applications, 2014. 25(7-8): p. 2067-2077.
[11] MILLS, R. and S. PERKINS, School of Mathematics and Computer Studies, South Australian Institute of Technology. Recent Developments in Mathematical Programming, 1991: p. 345.
[12] Çelebi, D., B. Bolat, and D. Bayraktar. Light rail passenger demand forecasting by artificial neural networks. in 2009 International Conference on Computers & Industrial Engineering. 2009. IEEE.
[13] Brännlund, U., et al., Railway timetabling using Lagrangian relaxation. Transportation science, 1998. 32(4): p. 358-369.
[14] Kroon, L.G. and L.W. Peeters, A variable trip time model for cyclic railway timetabling. Transportation Science, 2003. 37(2): p. 198-212.
[15] Allen, G.J., K.M. Mabrouk, and M.L. Weigel. Utilizing an expert system for conflict resolution in a" train siding selection" simulation model. in Proceedings Winter Simulation Conference. 1996. IEEE.
[16] Scardua, L. and C. Silva de Menezes. Train dispatching using expert systems. in IASTED International Conference on Applied Informatics (15th: 1997: Innsbruck, Austria). Proceedings. 1997.
[17] Chiang, T.-W. and H.-Y. Hau. Cycle detection in repair-based railway scheduling system. in Proceedings of IEEE International Conference on Robotics and Automation. 1996. IEEE.
[18] Gorman, M.F., An application of genetic and tabu searches to the freight railroad operating plan problem. Annals of operations research, 1998. 78: p. 51-69.
[19] Assad, A.A., Models for rail transportation. Transportation Research Part A: General, 1980. 14(3): p. 205-220.
[20] Jovanović, D. and P.T. Harker, Tactical scheduling of rail operations: the SCAN I system. Transportation Science, 1991. 25(1): p. 46-64.
[21] Carey, M. and D. Lockwood, A model, algorithms and strategy for train pathing. Journal of the Operational Research Society, 1995. 46(8): p. 988-1005.
[22] Cai, X., C. Goh, and A.I. Mees, Greedy heuristics for rapid scheduling of trains on a single track. IIE transactions, 1998. 30(5): p. 481-493.
[23] Mees, A.I., Railway scheduling by network optimization. Mathematical and Computer Modelling, 1991. 15(1): p. 33-42.
- صفحات : 39-50
-
دانلود فایل
( 609.71 KB )