Robust train speed trajectory optimization: A stochastic constrained shortest path approach

Li WANG, Lixing YANG, Ziyou GAO, Yeran HUANG

PDF(602 KB)
PDF(602 KB)
Front. Eng ›› 2017, Vol. 4 ›› Issue (4) : 408-417. DOI: 10.15302/J-FEM-2017042
RESEARCH ARTICLE
RESEARCH ARTICLE

Robust train speed trajectory optimization: A stochastic constrained shortest path approach

Author information +
History +

Abstract

Train speed trajectory optimization is a significant issue in railway traffic systems, and it plays a key role in determining energy consumption and travel time of trains. Due to the complexity of real-world operational environments, a variety of factors can lead to the uncertainty in energy-consumption. To appropriately characterize the uncertainties and generate a robust speed trajectory, this study specifically proposes distance-speed networks over the inter-station and treats the uncertainty with respect to energy consumption as discrete sample-based random variables with correlation. The problem of interest is formulated as a stochastic constrained shortest path problem with travel time threshold constraints in which the expected total energy consumption is treated as the evaluation index. To generate an approximate optimal solution, a Lagrangian relaxation algorithm combined with dynamic programming algorithm is proposed to solve the optimal solutions. Numerical examples are implemented and analyzed to demonstrate the performance of proposed approaches.

Keywords

train speed trajectory optimization / railway operation / stochastic programming

Cite this article

Download citation ▾
Li WANG, Lixing YANG, Ziyou GAO, Yeran HUANG. Robust train speed trajectory optimization: A stochastic constrained shortest path approach. Front. Eng, 2017, 4(4): 408‒417 https://doi.org/10.15302/J-FEM-2017042

References

[1]
Asnis I A, Dmitruk A V, Osmolovskii N P (1985). Solution of the problem of the energetically optimal control of the motion of a train by the maximum principle. U.S.S.R. Computational Mathematics and Mathematical Physics, 25(6): 37–44
CrossRef Google scholar
[2]
Calderaro V, Galdi V, Graber G, Piccolo A, Cogliano D (2014). An algorithm to optimize speed profiles of the metro vehicles for minimizing energy consumption. In: International Symposium on Power Electronics, Electrical Drives, Automation and Motion
[3]
Cao F, Fan L Q, Ke B R, Tang T (2016). Optimisation of recommended speed profile for train operation based on ant colony algorithm. International Journal of Simulation and Process Modelling, 11(3/4): 229
CrossRef Google scholar
[4]
Dominguez M, Cardador A F, Cucala A P, Pecharroman R R (2012). Energy savings in metropolitan railway substations through regenerative energy recovery and optimal design of ATO speed profiles. IEEE Transactions on Automation Science and Engineering, 9(3): 496–504
CrossRef Google scholar
[5]
Ghoseiri K, Szidarovszky F, Asgharpour M J (2004). A multi-objective train scheduling model and solution. Transportation Research Part B: Methodological, 38(10): 927–952
CrossRef Google scholar
[6]
Howlett P (2000). The optimal control of a train. Annals of Operations Research, 98(1–4): 65–87
CrossRef Google scholar
[7]
Ke B R, Chen M C, Lin C L (2009). Block-layout design using MAXCMIN ant system for saving energy on mass rapid transit systems. IEEE Transactions on Intelligent Transportation Systems, 10(2): 226–235
CrossRef Google scholar
[8]
Ke B R, Lin C L, Yang C C (2012). Optimisation of train energy-efficient operation for mass rapid transit systems. IET Intelligent Transport Systems, 6(1): 58–66
CrossRef Google scholar
[9]
Khmelnitsky E (2000). On an optimal control problem of train operation. IEEE Transactions on Automatic Control, 45(7): 1257–1266
CrossRef Google scholar
[10]
Kim K, Chien S I (2011). Optimal train operation for minimum energy consumption considering track alignment, speed limit, and schedule adherence. Journal of Transportation Engineering, 137(9): 665–674
CrossRef Google scholar
[11]
Ko H, Koseki T, Miyatake M (2004). Application of dynamic programming to optimization of running profile of a train. Computers in railways IX, 74: 10
[12]
Liu S Q, Cao F, Xun J, Wang Y (2015). Energy-efficient operation of single train based on the control strategy of ATO. IEEE 18th International Conference on Intelligent Transportation Systems, 2580–2586
[13]
Liu R F, Golovitcher I M (2003). Energy-efficient operation of rail vehicles. Transportation Research Part A: Policy and Practice, 37(10): 917–932
CrossRef Google scholar
[14]
Miyatake M, Ko H (2010). Optimization of train speed profile for minimum energy consumption. IEEJ Transactions on Electrical and Electronic Engineering, 5(3): 263–269
CrossRef Google scholar
[15]
Miyatake M, Matsuda K (2009). Energy saving speed and charge discharge control of a railway vehicle with on-board energy storage by means of an optimization model. IEEJ Transactions on Electrical and Electronic Engineering, 4(6): 771–778
CrossRef Google scholar
[16]
Tang H C, Dick C T, Feng X Y (2015). A coordinated train control algorithm to improve regenerative energy receptivity in metro transit systems. In Transportation Research Record Board 94th Annual Meeting, No. 15–1318
CrossRef Google scholar
[17]
Wang L, Yang L, Gao Z (2016). The constrained shortest path problem with stochastic correlated link travel times. European Journal of Operational Research, 255(1): 43–57
CrossRef Google scholar
[18]
Wang Y H, Schutter B D, Boom T J J V D, Ning B (2013). Optimal trajectory planning for trains-A pseudospectral method and a mixed integer linear programming approach. IEEE Transportation Research Part C, 29: 97–114
CrossRef Google scholar

RIGHTS & PERMISSIONS

2017 The Author(s) 2017. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)
AI Summary AI Mindmap
PDF(602 KB)

Accesses

Citations

Detail

Sections
Recommended

/