A Simulation Method for Analyzing and Evaluating Rail System Performance Based on Speed Profile

Hangfei Huang , Keping Li , Yanhui Wang

Journal of Systems Science and Systems Engineering ›› 2018, Vol. 27 ›› Issue (6) : 810 -834.

PDF
Journal of Systems Science and Systems Engineering ›› 2018, Vol. 27 ›› Issue (6) : 810 -834. DOI: 10.1007/s11518-017-5358-0
Article

A Simulation Method for Analyzing and Evaluating Rail System Performance Based on Speed Profile

Author information +
History +
PDF

Abstract

This paper presents and discusses a simulation method for analyzing and evaluating system performance on a rail line from the perspective of speed profile. Dynamic analysis for train motions is introduced, and a discrete time-operation graph is proposed to represent the relation between speed profile and energy consumption. Based on them, an analytical model is formulated to provide a quick insight into the system performance. The discrete-time simulation (DTS) method is then implemented to study the system in detail. Compared to the existing simulations, two innovations are included in the DTS: (1) the analytical lookup tables that can simplify the dynamic computation and, (2) the speed profile adjustment process that forecasts and avoids future conflicts based on practical constraints. The numerical results show that the DTS speed profile has advantages over existing methods. Finally, the DTS method is used to analyze and evaluate the system performance of the current timetable on Beijing Yizhuang Metro Line. The results suggest that the current timetable is not robust enough, and thus possible improvements are discussed at both scheduling and operating stages. The proposed method is verified to be effective and reliable for practical uses.

Keywords

Simulation / speed profile / discrete graph / dynamic analysis / system evaluation

Cite this article

Download citation ▾
Hangfei Huang, Keping Li, Yanhui Wang. A Simulation Method for Analyzing and Evaluating Rail System Performance Based on Speed Profile. Journal of Systems Science and Systems Engineering, 2018, 27(6): 810-834 DOI:10.1007/s11518-017-5358-0

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Abbott D., Marinov M.. An event based simulation model to evaluate the design of a rail interchange yard, which provides service to high speed and conventional railways. Simulation Modelling Practice and Theory, 2015, 52: 15-39.

[2]

Albrecht A.R., Howlett P.G., Pudney P., Vu X.. Energy-efficient train control: From local convexity to global optimization and uniqueness. Automatica, 2013, 49(10): 3072-3078.

[3]

Borndrfer R., Schlechte T.. Models for railway track allocation. 2005 Workshop on Algorithmic Methods & Models for Optimization of Railways, 2007

[4]

Burdett R.L., Kozan E.A.. A disjunctive graph model and framework for constructing new train schedules. European Journal of Operational Research, 2010, 200(1): 85-98.

[5]

Calin C., Bianca S.. Performance evaluation of discrete event systems involving Henstock-Kurzweil integral. Journal of Systems Science and Systems Engineering, 2009, 18(2): 243-256.

[6]

Chang C.S., Sim S.S.. Optimising train movements through coast control using genetic algorithms. IEE Proceedings-Electric Power Applications, 2008, 144(1): 65-73.

[7]

Chen C., He D.. Intelligent simulation for alternatives comparison and application to air traffic management. Journal of Systems Science and Systems Engineering, 2005, 14(1): 37-51.

[8]

Corman F., D’Ariano A., Pacciarelli D., Pranzo M.. Bi-objective conflict detection and resolution in railway traffic management. Transportation Research Part C: Emerging Technologies, 2012, 20(1): 79-94.

[9]

Corman F., D’Ariano A., Pacciarelli D., Pranzo M.. Evaluation of green wave policy in real-time railway traffic management. Transportation Research Part C: Emerging Technologies, 2009, 17(6): 607-616.

[10]

Corman F., Meng L.. A review of online dynamic models and algorithms for railway traffic control. ICIRT, 2013 128-133.

[11]

D’ariano A., Pacciarelli D., Pranzo M.. A branch and bound algorithm for scheduling trains in a railway network. European Journal of Operational Research, 2007, 183(2): 643-657.

[12]

D’ariano A., Pacciarelli D., Pranzo M.. Assessment of flexible timetables in real-time traffic management of a railway bottleneck. Transportation Research Part C: Emerging Technologies, 2008, 16(2): 232-245.

[13]

Dorfman M.J., Medanic J.. Scheduling trains on a railway network using a discrete event model of railway traffic. Transportation Research Part B: Methodological, 2004, 38(1): 81-98.

[14]

Howlett P.G.. The optimal control of a train. annals of operations research, 2000, 98(1): 65-87.

[15]

Howlett P.G., Pudney P.. Energy Efficient Train Control, 1995.

[16]

Huang H., Li K.. Train timetable optimization for both a rail line and a network with graph-based approaches. Engineering Optimization, 2017, 49(12): 2133-2149.

[17]

Kim M., Schonfeld P., Kim E.. Comparison of vertical alignments for rail transit. Journal of Transportation Engineering-ASCE, 2013, 139(2): 230-238.

[18]

Marinov M., Mortimer P., Zunder T., Islam D.. A steady state analysis for yard performances. Journal of Transport Literature, 2011, 5(1): 33-49.

[19]

Marinov M., Viegas J.. A mesoscopic simulation modelling methodology for analyzing and evaluating freight train operations in a rail network. Simulation Modelling Practice and Theory, 2011, 19(1): 516-539.

[20]

Marinov M., Viegas J.. Analysis and evaluation of double ended flat-shunted yard performance employing two yard crews. Journal of Transportation Engineering-ASCE, 2011, 137(5): 319-326.

[21]

Marinov M., Viegas J.. Tactical management of rail freight transportation services: evaluation of yard performance. Transportation Planning and Technology, 2011, 34(4): 363-387.

[22]

Marinov M., Viegas J.. A simulation modelling methodology for evaluating flat-shunted yard operations. Simulation Modelling Practice and Theory, 2009, 17(6): 1106-1129.

[23]

Martinis V., Weidmann U.. Definition of energy-efficient speed profiles within rail traffic by means of supply design models. Research in Transportation Economics, 2015, 54: 41-50.

[24]

Motraghi A., Marinov M.. Analysis of urban freight by rail using event based simulation. Simulation Modelling Practice and Theory, 2012, 25: 73-89.

[25]

Nash, A. & Huerlimann, D. (2004). Railroad simulation using OpenTrack. WIT Transactions on the Built Environment, 74.

[26]

Powell J., Palacin R.. Passenger stability within moving railway vehicles: limits on maximum longitudinal acceleration. Urban Rail Transit, 2015, 1(2): 95-103.

[27]

Quaglietta E., Punzo V.. Supporting the design of railway systems by means of a Sobol variance-based sensitivity analysis. Transportation Research Part C: Emerging Technologies, 2013, 34: 38-54.

[28]

Shakibayifar M., Sheikholeslamib A., Corman F.. A simulation-based optimization approach to reschedule train traffic in uncertain conditions during disruptions. SCI IRAN, Available online, 2017

[29]

Su S., Li X., Tang T., Gao Z.. A subway train timetable optimization approach based on energy-efficient operation strategy. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(2): 883-893.

[30]

Tan W., Chai Y., Liu Y.. A message-driving formalism for modeling and simulation of multi-agent supply chain systems. Journal of Systems Science and Systems Engineering, 2011, 20(4): 385-399.

[31]

Vuchic V.R.. Urban Transit Systems and Technology, 2007.

[32]

Wales J., Marinov M.. Analysis of delays and delay mitigation on a metropolitan rail network using event based simulation. Simulation Modelling Practice and Theory, 2015, 52: 52-77.

[33]

Woroniuk C., Marinov M.. Simulation modelling to analyse the current level of utilisation of sections along a rail route. Journal of Transport Literature, 2013, 7(2): 235-252.

[34]

Xu X., Li K., Yang L.. Discrete event model-based simulation for train movement on a single-line railway. Chinese Physics B, 2014, 23(8): 080205.

[35]

Yang L., Zhang Y., Li S., Gao Y.. A two-stage stochastic optimization model for the transfer activity choice in metro networks. Transportation Research Part B: Methodological, 2016, 83: 271-297.

[36]

Yin J., Yang L., Tang T., Gao Z., Ran B.. Dynamic passenger demand oriented metro train scheduling with energy-efficiency and waiting time minimization: mixed-integer linear programming approaches. Transportation Research Part B: Methodological, 2017, 97: 182-213.

AI Summary AI Mindmap
PDF

124

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/