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Optimal control of train movement is an important way to reduce transport cost, enhance service level, and realize sustainable development. In this paper, based on traditional optimal velocity car-following model, an improved simulation model is presented, it is used to optimize the velocity control of train movement in urban railway system. The proposed model is established by introducing a new function of objective optimal velocity into the classical optimal velocity model (See Phys. Rev. E 51, 1035, Bando et al, 1995) to realize the optimal control of train movement in complicated conditions. Numerical simulation takes the Beijing City Metro Yi Zhuang line as an example. Here some reality measurement data is used. Results show that the proposed model can well describe the dynamic characteristics of train movement under the complex limited condition. Simulation results are close to reality measurement data. This demonstrates that the proposed model is valid. Further, by analyzing the space-time graph, the change of train velocity and travel time, the evolution characters of train flow under complex conditions are discussed.
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Keywords:
- car-following model /
- train movement /
- optimal control
[1] Adinolfi A, Lamedica R, Modesto C 1998 IEEE Transactions on Power Delivery 13 1536
[2] Kokotovic P, Singh G 1972 IEEE Transactions on Automatic Control 17 92
[3] Hwang H S 1998 IEEE Transactions on Systems, Man, and Cybernetics A: Systems and Humans 28 791
[4] Duarte M A, Sotomayor P X 1999 Optimal Control Applications and Methods 20 283
[5] Howlett P 1996 Automation 3 519
[6] Liu R Golovitcher I M 2003 Transportation Research Part A: Policy and Practice 37 917
[7] Howlett P G 2000 Annals of Operation Research 98 1257
[8] Li K P, Gao Z Y, Mao B H 2007 Chin. Phys. B 16 359
[9] Chang C S, Sim S S 1997 Proceedings-Electric Power Applications 144 65
[10] Cheng J X, Howlett P G 1993 IEEE Transactions Automatic Control 38 1730
[11] Pipes L A 1953 J. Appl. Phys 24 274
[12] Chandler R E, Herman R, Montroll E W 1958 Operational Research 6 165
[13] Newell G F in 1961 Operational Research 9 209
[14] Bando M, Hasebe K, Nakayama A 1995 Phys. Rev. E 51 1035
[15] Helbing D, Tilch B 1998 Phys. Rev. E 58 133
[16] Treiber M, Hennecke A, Helbing D 2000 Phys. Rev. E 62 1805
[17] Tomer E, Safonov L, Havlin S 2000 Phys. Rev. Lett. 84 382
[18] Tang T Q, Huang H J, Shang H Y 2010 Acta Phys. Sin. 59 6003 (in Chinese)[唐铁桥, 黄海军, 尚华艳2010 59 6003]
[19] Yuan N, Hua C C 2012 Acta Phys. Sin. 61 160509 (in Chinese)[袁娜, 化存才2012 61 160509]
[20] Su S, Li X, Tang T, Gao Z Y 2013 IEEE transaction on intelligent transportation system 14 883
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[1] Adinolfi A, Lamedica R, Modesto C 1998 IEEE Transactions on Power Delivery 13 1536
[2] Kokotovic P, Singh G 1972 IEEE Transactions on Automatic Control 17 92
[3] Hwang H S 1998 IEEE Transactions on Systems, Man, and Cybernetics A: Systems and Humans 28 791
[4] Duarte M A, Sotomayor P X 1999 Optimal Control Applications and Methods 20 283
[5] Howlett P 1996 Automation 3 519
[6] Liu R Golovitcher I M 2003 Transportation Research Part A: Policy and Practice 37 917
[7] Howlett P G 2000 Annals of Operation Research 98 1257
[8] Li K P, Gao Z Y, Mao B H 2007 Chin. Phys. B 16 359
[9] Chang C S, Sim S S 1997 Proceedings-Electric Power Applications 144 65
[10] Cheng J X, Howlett P G 1993 IEEE Transactions Automatic Control 38 1730
[11] Pipes L A 1953 J. Appl. Phys 24 274
[12] Chandler R E, Herman R, Montroll E W 1958 Operational Research 6 165
[13] Newell G F in 1961 Operational Research 9 209
[14] Bando M, Hasebe K, Nakayama A 1995 Phys. Rev. E 51 1035
[15] Helbing D, Tilch B 1998 Phys. Rev. E 58 133
[16] Treiber M, Hennecke A, Helbing D 2000 Phys. Rev. E 62 1805
[17] Tomer E, Safonov L, Havlin S 2000 Phys. Rev. Lett. 84 382
[18] Tang T Q, Huang H J, Shang H Y 2010 Acta Phys. Sin. 59 6003 (in Chinese)[唐铁桥, 黄海军, 尚华艳2010 59 6003]
[19] Yuan N, Hua C C 2012 Acta Phys. Sin. 61 160509 (in Chinese)[袁娜, 化存才2012 61 160509]
[20] Su S, Li X, Tang T, Gao Z Y 2013 IEEE transaction on intelligent transportation system 14 883
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