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[1] O'Dell, S. & Wilson, N., Optimal real-time control strategies for rail transit operations during disruptions. Computer-Aided Transit Scheduling, Vol. 471, ed. N. Wilson, Springer-Verlag, Berlin, Heidelberg, pp. 299–323, 1999. [Crossref]
[2] Eberlein, X.-J., Wilson, N. & Bernstein, D., Modeling real-time control strategies in public transit operations. Computer-Aided Transit Scheduling, Vol. 471, ed. N. Wilson, Springer-Verlag, Berlin, Heidelberg, pp. 325–346, 1999. [Crossref]
[3] Mesa, J.A., Ortega, F.A. & Pozo, M.A., Effective allocation of fleet frequencies by reducing intermediate stops and short turning in transit systems. Lecture Notes in Computer Science, 5868(8), pp. 293–309, 2009. [Crossref]
[4] Cortés, C.E., Jara-Díaz, S. & Tirachini, A., Integrating short turning and deadheading in the optimization of transit services. Transportation Research Part A, 45, pp. 419–434, 2011. [Crossref]
[5] Wilson, N.H.M., Macchi, R.A., Fellows, R.E. & Decko, A.A., Improving service on the MBTA green line through better operations control. Transportation Research Record, 1361, pp. 296–304, 1992. DOI: http://onlinepubs.trb.org/Onlinepubs/trr/1992/1361/1361-042.pdf.
[6] Soeldner, D., A comparison of control options on the MBTA green line. Master’s thesis, Civil Engineering, MIT, 1993.
[7] Cordeau, J.-F., Toth, P. & Vigo, D., A survey of optimization models for train routing and scheduling. Transportation Science, 32(4), pp. 380–404, 1998. [Crossref]
[8] Törnquist, J., Railway traffic disturbance management – an experimental analysis of disturbance complexity, management objectives and limitations in planning horizon. Transportation Research Part A: Policy and Practice, 41(3), pp. 249–266, 2007. [Crossref]
[9] Mesa, J.A., Ortega, F.A. & Pozo, M.A., A geometric model for an effective rescheduling after reducing service in public transportation systems. Computers & Operations Research, 40(3), pp. 737–746, 2013. [Crossref]
[10] Mesa, J.A., Ortega, F.A., Pozo, M.A. & Puerto, J., Rescheduling railway timetables in presence of passenger transfers between lines within a transportation network. Advances in Intelligent Systems and Computing, 262, pp. 347–360, 2014. Computer-based Modelling and Optimization in Transportation, ed. J.F. de Sousa & R. Rossi, Springer International Publishing Switzerland. [Crossref]
[11] Daly, A., Estimating logit models. Transportation Research Part B: Methodological, 21(4), pp. 251–267, 1987. [Crossref]
[12] D'Acierno, L., Botte, M. & Montella, B., Assumptions and simulation of passenger behaviour on rail platforms. International Journal of Transport Development and Integration, 2(2), pp. 123–135, 2018. –135. [Crossref]
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Acadlore takes over the publication of IJTDI from 2025 Vol. 9, No. 4. The preceding volumes were published under a CC BY 4.0 license by the previous owner, and displayed here as agreed between Acadlore and the previous owner. ✯ : This issue/volume is not published by Acadlore.

Open Access
Research article

Railway Traffic Disturbance Management by Means of Control Strategies Applied to Operations in the Transit System

Francisco A. Ortega1,
Juan A. Mesa2,
Miguel A. Pozo2,
Justo Puerto3
1
Higher Technical School of Architecture, Universidad de Sevilla, Spain
2
Higher Technical School of Engineering, Universidad de Sevilla, Spain
3
Faculty of Mathematics, Universidad de Sevilla, Spain
International Journal of Transport Development and Integration
|
Volume 2, Issue 4, 2018
|
Pages 362-372
Received: N/A,
Revised: N/A,
Accepted: N/A,
Available online: 11-15-2018
View Full Article|Download PDF

Abstract:

Railway systems in metropolitan areas support a high density of daily traffic that is exposed to different types of disturbances in the service. An interesting topic in the literature is to obtain action protocols in the presence of contingencies which can affect the system operation, avoiding the propagation of perturbation and minimizing its negative consequences.

Assume that, with a small margin of time (e.g. one day), the decision-maker of the transportation network is knowing that a part of the train fleet will become inoperative temporarily along a specific transit line and none additional vehicle will be able to restore the affected services. The decision to be taken in consequence will require to reschedule the existing services by possibly reducing the number of expeditions (line runs). This will affect travellers who regularly use the transit system to get around.

Consider that the decision-maker aims to lose the least number of passengers as a consequence of having introduced changes into the transit line. A strategy that could be applied in this context is to remove those line runs which are historically less used by travellers without affecting the remaining services. Another alternative strategy might be to reschedule the timetables of the available units, taking into account the pattern of arrivals of users to the boarding stations and the user behavior during waiting times (announced in situ).

The aim of this work consists of assessing the strategy of train rescheduling along the current transportation line when the supply must be reduced in order to reinforce the service of another line, exploited by the same public operator, which has suffered an incidence or emergency.

Keywords: Disruption management, Railways, Timetable rescheduling
References
[1] O'Dell, S. & Wilson, N., Optimal real-time control strategies for rail transit operations during disruptions. Computer-Aided Transit Scheduling, Vol. 471, ed. N. Wilson, Springer-Verlag, Berlin, Heidelberg, pp. 299–323, 1999. [Crossref]
[2] Eberlein, X.-J., Wilson, N. & Bernstein, D., Modeling real-time control strategies in public transit operations. Computer-Aided Transit Scheduling, Vol. 471, ed. N. Wilson, Springer-Verlag, Berlin, Heidelberg, pp. 325–346, 1999. [Crossref]
[3] Mesa, J.A., Ortega, F.A. & Pozo, M.A., Effective allocation of fleet frequencies by reducing intermediate stops and short turning in transit systems. Lecture Notes in Computer Science, 5868(8), pp. 293–309, 2009. [Crossref]
[4] Cortés, C.E., Jara-Díaz, S. & Tirachini, A., Integrating short turning and deadheading in the optimization of transit services. Transportation Research Part A, 45, pp. 419–434, 2011. [Crossref]
[5] Wilson, N.H.M., Macchi, R.A., Fellows, R.E. & Decko, A.A., Improving service on the MBTA green line through better operations control. Transportation Research Record, 1361, pp. 296–304, 1992. DOI: http://onlinepubs.trb.org/Onlinepubs/trr/1992/1361/1361-042.pdf.
[6] Soeldner, D., A comparison of control options on the MBTA green line. Master’s thesis, Civil Engineering, MIT, 1993.
[7] Cordeau, J.-F., Toth, P. & Vigo, D., A survey of optimization models for train routing and scheduling. Transportation Science, 32(4), pp. 380–404, 1998. [Crossref]
[8] Törnquist, J., Railway traffic disturbance management – an experimental analysis of disturbance complexity, management objectives and limitations in planning horizon. Transportation Research Part A: Policy and Practice, 41(3), pp. 249–266, 2007. [Crossref]
[9] Mesa, J.A., Ortega, F.A. & Pozo, M.A., A geometric model for an effective rescheduling after reducing service in public transportation systems. Computers & Operations Research, 40(3), pp. 737–746, 2013. [Crossref]
[10] Mesa, J.A., Ortega, F.A., Pozo, M.A. & Puerto, J., Rescheduling railway timetables in presence of passenger transfers between lines within a transportation network. Advances in Intelligent Systems and Computing, 262, pp. 347–360, 2014. Computer-based Modelling and Optimization in Transportation, ed. J.F. de Sousa & R. Rossi, Springer International Publishing Switzerland. [Crossref]
[11] Daly, A., Estimating logit models. Transportation Research Part B: Methodological, 21(4), pp. 251–267, 1987. [Crossref]
[12] D'Acierno, L., Botte, M. & Montella, B., Assumptions and simulation of passenger behaviour on rail platforms. International Journal of Transport Development and Integration, 2(2), pp. 123–135, 2018. –135. [Crossref]
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Ortega, F. A., Mesa, J. A., Pozo, M. A., & Puerto, J. (2018). Railway Traffic Disturbance Management by Means of Control Strategies Applied to Operations in the Transit System. Int. J. Transp. Dev. Integr., 2(4), 362-372. https://doi.org/10.2495/TDI-V2-N4-362-372
F. A. Ortega, J. A. Mesa, M. A. Pozo, and J. Puerto, "Railway Traffic Disturbance Management by Means of Control Strategies Applied to Operations in the Transit System," Int. J. Transp. Dev. Integr., vol. 2, no. 4, pp. 362-372, 2018. https://doi.org/10.2495/TDI-V2-N4-362-372
@research-article{Ortega2018RailwayTD,
title={Railway Traffic Disturbance Management by Means of Control Strategies Applied to Operations in the Transit System},
author={Francisco A. Ortega and Juan A. Mesa and Miguel A. Pozo and Justo Puerto},
journal={International Journal of Transport Development and Integration},
year={2018},
page={362-372},
doi={https://doi.org/10.2495/TDI-V2-N4-362-372}
}
Francisco A. Ortega, et al. "Railway Traffic Disturbance Management by Means of Control Strategies Applied to Operations in the Transit System." International Journal of Transport Development and Integration, v 2, pp 362-372. doi: https://doi.org/10.2495/TDI-V2-N4-362-372
Francisco A. Ortega, Juan A. Mesa, Miguel A. Pozo and Justo Puerto. "Railway Traffic Disturbance Management by Means of Control Strategies Applied to Operations in the Transit System." International Journal of Transport Development and Integration, 2, (2018): 362-372. doi: https://doi.org/10.2495/TDI-V2-N4-362-372
ORTEGA F A， MESA J A， POZO M A， et al. Railway Traffic Disturbance Management by Means of Control Strategies Applied to Operations in the Transit System[J]. International Journal of Transport Development and Integration， 2018， 2(4)： 362-372. https://doi.org/10.2495/TDI-V2-N4-362-372