Simulation-Based Method of Capacity Utilization Evaluation to Account for Uncertainty in Recovery Time

Abstract

A typical objective of a railway agency is to design an efficient and reliable service plan. Although enhancing the level of capacity utilization may increase efficiency, the stability of the service plan may also decrease due to stochastic railway disruptions. Past studies used recovery time to evaluate the stability of a service plan; however, the variation of recovery time was not fully examined. Therefore, we developed a Monte Carlo simulation framework to address this issue and proposed four capacity-based indices for the evaluation of service efficiency and stability. These indices are capacity utilization efficiency, mean recovery time, dispersion of recovery time, and probability of unacceptable recovery time. The latter two new indices explored the variation of recovery time and probability of unacceptable long recovery time, which is undesired for railway operation. We conducted a real-world case study on the Taiwan Railways Administration before and after the service plan revision. Consequently, the northbound service plan needed a deliberate review and revision before implementation, whereas the southbound plan successfully increased capacity usage while not necessarily worsening stability. This result demonstrated that our study introduced uncertainty analysis into the evaluation framework and provided flexible information on the service plan. These developments can provide improved support in the decision-making of railway agencies to strike a balance between service level and asset usage.

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References

Carey

Kwiecifiski

Properties of Expected Costs and Performance Measures in Stochastic Models of Scheduled Transport. European Journal of Operational Research, Vol. 83, No. 1, 1995, pp. 182–199.

Carey

Optimizing Scheduled Times, Allowing for Behavioural Response. Transportation Research Part B: Methodological, Vol. 32, No. 5, 1998, pp. 329–342.

Koelemeijer

G. S.

Goverde

R. M. P.

van Egmond

R. J.

PETER: A Performance Evaluator for Railway Timetables. WIT Transactions on the Built Environment, Vol. 50, 2000, pp. 405–414.

Hansen

I. A.

Station Capacity and Stability of Train Operations. WIT Transactions on the Built Environment, Vol. 50, 2000, pp. 809–816.

Huisman

Boucherie

R. J.

van Dijk

N. M.

A Solvable Queueing Network Model for Railway Networks and Its Validation and Applications for the Netherlands. European Journal of Operational Research, Vol. 142, No. 1, 2002, pp. 30–51.

Goverde

R. M. P.

Railway Timetable Stability Analysis Using Max-Plus System Theory. Transportation Research Part B: Methodological, Vol. 41, No. 2, 2007, pp. 179–201.

Yuan

Hansen

I. A.

Optimizing Capacity Utilization of Stations by Estimating Knock-On Train Delays. Transportation Research Part B: Methodological, Vol. 41, No. 2, 2007, pp. 202–217.

Salido

M. A.

Barber

Ingolotti

Robustness in Railway Transportation Scheduling. Proc., 7th World Congress on Intelligent Control and Automation, Chongqing, China, 2008, pp. 2880–2885.

Cicerone

D’Angelo

Di Stefano

Frigioni

Navarra

Recoverable Robust Timetabling for Single Delay: Complexity and Polynomial Algorithms for Special Cases. Journal of Combinatorial Optimization, Vol. 18, No. 3, 2009, pp. 229–257.

10.

Goverde

R. M. P.

Heidergott

Merlet

Railway Timetable Stability Analysis Using Stochastic Max-Plus Linear Systems. Proc., 3rd Railway Operations Modelling and Analysis Engineering and Optimization Approaches, Zurich, 2009.

11.

Andersson

E. V.

Peterson

Törnquist–Krasemann

Quantifying Railway Timetable Robustness in Critical Points. Journal of Rail Transport Planning & Management, Vol. 3, No. 3, 2014, pp. 95–110.

12.

Cats

Yap

van Oort

Exposing the Role of Exposure: Public Transport Network Risk Analysis. Transportation Research Part A: Policy and Practice, Vol. 88, 2016, pp. 1–14.

13.

Zilko

A. A.

Kurowicka

Goverde

R. M. P.

Modeling Railway Disruption Lengths with Copula Bayesian Networks. Transportation Research Part C: Emerging Technologies, Vol. 68, 2016, pp. 350–368.

14.

Hooghiemstra

J. S.

Teunisse

M. J. G.

The Use of Simulation in the Planning of the Dutch Railway services. Proc., 1998 Winter Simulation Conference, Washington, D.C., 1998, pp. 1139–1146.

15.

Carey

Carville

Testing Schedule Performance and Reliability for Train Stations. Journal of the Operational Research Society, Vol. 51, No. 6, 2000, pp. 666–682.

16.

Middelkoop

Bouwman

Testing the Stability of the Rail Network. WIT Transactions on the Built Environment, Vol. 61, 2002, pp. 995–1002.

17.

Engelhardt-Funke

Kolonko

Analysing Stability and Investments in Railway Networks Using Advanced Evolutionary Algorithms. International Transactions in Operational Research, Vol. 11, No. 4, 2004, pp. 381–394.

18.

Vromans

J. C. M.

Dekker

Kroon

L. G.

Reliability and Heterogeneity of Railway Services. European Journal of Operational Research, Vol. 172, No. 2, 2006, pp. 647–665.

19.

Berbey

Galán

San Segundo

Sanz-Bobi

Lyapunov Based Stability Analysis for Metro Lines. Proc., 14th International Conference on Urban Transport and the Environment in the 21st Century, Ashurst Lodge, 2008, pp. 111–119.

20.

Berbey

Caballero

Galán

Sanz-Bobi

A New Stability Index for Metro Lines. Proc., 19th International Conference on Electronics Communications and Computers. Piscataway, 2009, pp. 41–45.

21.

Demitz

Hübschen

Albrecht

Timetable Stability - Using Simulation to Ensure Quality in a Regular Interval Timetable. In Ward

I. A.

(ed.), Timetable Planning and Information Quality, WIT Press, Southampton, UK, 2010, pp. 11–24.

22.

Liu

J. R.

Development and Application of a Simulation Model on Railway Knock-on Delay. PhD dissertation. Institute of Traffic and Transportation, National Chiao Tung University, 2011.

23.

Burkhard

Seybold

Buker

Graffagnino

Labermeier

OnTime - Network-Wide Analysis of Timetable Stability. Proc., 5th International Seminar on Railway Operations Modelling and Analysis - RailCopenhagen 2013. Copenhagen, Denmark, 2013.

24.

Lai

Y. C.

C. S.

An Integrated Framework for Assessing Service Efficiency and Stability of Rail Transit Systems. Transportation Research Part C: Emerging Technologies, Vol. 79, 2017, pp. 18–41.

25.

Lai

Y. C.

Chen

K. T.

Evaluating Service Risk in Railway Capacity Utilization Using Expected Recovery Time. Journal of Transportation Engineering, Part A: Systems, Vol. 143, No. 6, 2017, 04017016.

26.

Ward

J. H.

Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, Vol. 58, No. 301, 1963, pp. 236–244.

27.

Ross

S. M.

Introduction to Probability Models, 10th ed. Academic Press, Cambridge, MA, 2009, pp. 312–319.

28.

Coleman

Modelling Extremes. International Statistical Workshop, Seoul University, Seoul, Korea, 2002.

29.

Lin

X. G.

Statistical Modelling of Severe Wind Gust. International Congress on Modelling and Simulation, Townsville, Vol. 2, 2003, pp. 620–625.

30.

Kellezi

Gilli

Extreme Value Theory for Tail-Related Risk Measures. International Center for Financial Asset Management and Engineering Research Paper Series, Switzerland, 2000.