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[1] Ben Akiva, M. & Lerman, S., Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press: Cambridge, MA, 1985.
[2] Eriksson, L., Garvill, J. & Nordlund, A.M., Acceptability of single and combined transport policy measures: The importance of environmental and policy specific beliefs. Transportation Research Part A, 42(8), pp. 1117–1128, 2008. tra.2008.03.006. [Crossref]
[3] Habibian, M. & Kermanshah, M., Coping with congestion: Understanding the role of simultaneous transportation demand management policies on commuters. Transport Policy, 30, pp. 229–237, 2013. [Crossref]
[4] Meyer, M.D., Demand management as an element of transportation policy: using carrots and sticks to influence travel behaviour. Transportation Research Part A, 33(7–8), pp. 575–599, 1999. [Crossref]
[5] Chien, S. & Schonfeld, P., Joint optimization of a rail transit line and its feeder bus system. Journal of Advanced Transportation, 32(3), pp. 253–284, 1998. DOI: 10.1002/ atr.5670320302.
[6] Kuan, S.N., Ong, H.L. & Ng, K.M., Solving the feeder bus network design problem by genetic algorithms and ant colony optimization. Advances in Engineering Software 37(6), pp. 351–359, 2006. [Crossref]
[7] Shrivastav, P. & Dhingra, S.L., Development of feeder routes for suburban railway stations using heuristic approach. Journal of Transportation Engineering, 127(4), pp. 334–341, 2001. [Crossref]
[8] D’Acierno, L., Gallo, M., Montella, B. & Placido, A., The definition of a model framework for managing rail systems in the case of breakdowns. Proceedings of the 16th IEEE Conference on Intelligent Transportation Systems (ITSC), The Hague, The Netherlands, pp. 1059–1064, 2013. [Crossref]
[9] Abenoza, R.F., Cats, O. & Susilo, Y.O., Travel satisfaction with public transport: Determinants, user classes, regional disparities and their evolution. Transportation Research Part A, 95, pp. 64–84, 2017. [Crossref]
[10] dell’Olio, L., Ibeas, A. & Cecin, P., The quality of service desired by public transport users. Transport Policy, 18(1), pp. 217–227, 2011. [Crossref]
[11] de Ona, J. & de Ona, R., Quality of service in public transport based on customer satisfaction surveys: A review and assessment of methodological approaches. Transportation Science, 49(3), pp. 605–622, 2014. [Crossref]
[12] Consilvio, A., Di Febbraro, A. & Sacco, N., Stochastic scheduling approach for predictive risk-based railway maintenance. Proceedings of 2016 IEEE International Conference on Intelligent Rail Transportation (ICIRT), Birmingham, UK, August 2016. [Crossref]
[13] Pereira, P., Ribeiro, R.P. & Gama, J., Failure prediction: An application in the railway industry. Lecture Notes in Computer Science, 8777, pp. 264–275, 2014. [Crossref]
[14] Corman, F. & Meng, L., A review of online dynamic models and algorithms for railway traffic management. IEEE Transactions on Intelligent Transportation Systems, 16(3), pp. 1274–1284, 2015. [Crossref]
[15] Gao, Y., Kroon, L., Schmidt, M. & Yang, L., Rescheduling a metro line in an overcrowded situation after disruptions. Transportation Research Part B, 93, pp. 425–449, 2016. [Crossref]
[16] Zhan, S., Zhao, J. & Peng, Q., Real-time train rescheduling on high-speed railway under partial segment blockages. Journal of the China Railway Society, 38(10), pp. 1–13, 2016. [Crossref]
[17] Cacchiani, V., Huisman, D., Kidd, M., Kroon, L., Toth, P., Veelenturf, L. & Wagenaar, J., An overview of recovery models and algorithms for real-time railway rescheduling. Transportation Research Part B, 63, pp. 15–37, 2014. [Crossref]
[18] Dollevoet, T., Huisman, D., Schmidt, M. & Schöbel, A., Delay management with rerouting of passengers. Transportation Science, 46(1), pp. 74–89, 2012. DOI: 10.1287/ trsc.1110.0375.
[19] Corman F., D’Ariano A., Pacciarelli D. & Pranzo, M., A tabu search algorithm for rerouting trains during rail operations. Transportation Research Part B, 44(1), pp. 175–192, 2010. [Crossref]
[20] Ghaemi N., Goverde R.M.P. & Cats O., Railway disruption timetable: Short-turnings in case of complete blockage. Proceedings of 2016 IEEE International Conference on Intelligent Rail Transportation (ICIRT), Birmingham, UK, August 2016. DOI: 10.1109/ ICIRT.2016.7588734.
[21] Montella, B., Gallo, M. & D’Acierno, L., Multimodal network design problems. Advances in Transport, 5, pp. 405–414, 2000. [Crossref]
[22] D’Acierno L., Placido A., Botte M. & Montella B., A methodological approach for managing rail disruptions with different perspectives. International Journal of Mathematical Models and Methods in Applied Sciences, 10, pp. 80–86, 2016. http://www. naun.org/main/NAUN/ijmmas/2016/a202001-419.pdf
[23] Botte M., Di Salvo C., Placido A., Montella B. & D’Acierno L., A neighbourhood search algorithm for determining optimal intervention strategies in the case of metro system failures. International Journal of Transport Development and Integration, 1(1), pp. 63–73, 2017. [Crossref]
[24] Cadarso, L. & Marìn, A., Improved rapid transit network design model: considering transfer effects. Annals of Operations Research, forthcoming. 015-1999-x. [Crossref]
[25] Lee, Y.J. & Vuchic, V.R., Transit network design with variable demand. Journal of Transportation Engineering, 131(1), pp. 1–10, 2006. 947X(2005)131:1(1). [Crossref]
[26] Marìn, A.G. & Garcìa-Ròdenas, R., Location of infrastructure in urban railway networks. Computers & Operations Research, 36(5), pp. 1461–1477, 2009. cor.2008.02.008. [Crossref]
[27] Cantarella, G.E., A general fixed-point approach to multimode multi-user equilibrium assignment with elastic demand. Transportation Science, 31(2), pp. 107–128, 1997. [Crossref]
[28] D’Acierno, L., Gallo, M. & Montella, B., Ant Colony Optimisation approaches for the transportation assignment problem. WIT Transaction on the Built Environment, 111, pp. 37–48, 2010. [Crossref]
[29] Nguyen, S., Pallottino, S. & Gendreau, M., Implicit enumeration of hyperpaths in a logit model for transit networks. Transportation Science, 32(1), pp. 54–64, 1998. [Crossref]
[30] Nuzzolo, A., Russo, F. & Crisalli, U., A doubly dynamic schedule-based assignment model for transit networks. Transportation Sciences, 35(3), pp. 268–285, 2001. [Crossref]
[31] D’Acierno, L., Gallo, M., Montella, B. & Placido, A., Analysis of the interaction between travel demand and rail capacity constraints. WIT Transactions on the Built Environment, 128, pp. 197–207, 2012. [Crossref]
[32] Kunimatsu, T., Hirai, C. & Tomii, N., Train timetable evaluation from the viewpoint of passengers by microsimulation of train operation and passenger flow. Electrical Engineering in Japan, 181(4), pp. 51–62, 2012. [Crossref]
[33] D’Acierno, L., Botte, M., Placido, A., Caropreso, C. & Montella, B., Methodology for determining dwell times consistent with passenger flows in the case of metro services. Urban Rail Transit, 3(2), pp. 73–89, 2017. [Crossref]
[34] Ercolani, M., Placido, A., D’Acierno, L. & Montella, B., The use of microsimulation models for the planning and management of metro systems. WIT Transactions on the Built Environment, 135, pp. 509–521, 2014. [Crossref]
[35] Placido, A. & D’Acierno, L., A methodology for assessing the feasibility of fleet compositions with dynamic demand. Transportation Research Procedia, 10, pp. 595–604, 2015. [Crossref]
[36] Caropreso, C., Di Salvo, C., Botte, M. & D’Acierno, L., A long term analysis of passenger flows on a regional rail line. International Journal of Transport Development and Integration, 1(3), pp. 329–338, 2017. [Crossref]
[37] Cascetta, E., Papola, A., Marzano, V., Simonelli, F. & Vitiello, I., Quasi-dynamic estimation of o–d flows from traffic counts: Formulation, statistical validation and performance analysis on real data. Transportation Research Part B, 55, pp. 171–187, 2013. [Crossref]
[38] Di Mauro, R., Botte, M. & D’Acierno, L., An analytical methodology for extending passenger counts in a metro system. International Journal of Transport Development and Integration, 1(3), pp. 589–600, 2017. [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

Assumptions and Simulation of Passenger Behaviour on Rail Platforms

L. D’Acierno,
M. Botte,
B. Montella
Department of Civil, Architectural and Environmental Engineering, Federico II University of Naples, Italy
International Journal of Transport Development and Integration
|
Volume 2, Issue 2, 2018
|
Pages 123-135
Received: N/A,
Revised: N/A,
Accepted: N/A,
Available online: 01-31-2018
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Abstract:

Current techniques of travel demand management are based on the simulation of users’ reactions to implement strategies. Indeed, the correct modelling of user behaviour may be considered important for managing public transport systems. Especially in high-density contexts, performance of the masstransit system may represent one of the main tools of decision-makers for affecting users’ choices. In this article, we focus on the behaviour of users waiting on rail/metro platforms, analysing boarding priorities when a train arrives based on the traditional First In–First Out (FIFO) approach and comparing it with Random In–First Out (RIFO) behaviour. The approaches are then applied in the case of a real metro line operating under different congestion levels.

Keywords: capacity constraints, FIFO approach, microsimulation approach, passenger behaviour, public transport management, rail passenger systems, RIFO approach, traffic assignment models
References
[1] Ben Akiva, M. & Lerman, S., Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press: Cambridge, MA, 1985.
[2] Eriksson, L., Garvill, J. & Nordlund, A.M., Acceptability of single and combined transport policy measures: The importance of environmental and policy specific beliefs. Transportation Research Part A, 42(8), pp. 1117–1128, 2008. tra.2008.03.006. [Crossref]
[3] Habibian, M. & Kermanshah, M., Coping with congestion: Understanding the role of simultaneous transportation demand management policies on commuters. Transport Policy, 30, pp. 229–237, 2013. [Crossref]
[4] Meyer, M.D., Demand management as an element of transportation policy: using carrots and sticks to influence travel behaviour. Transportation Research Part A, 33(7–8), pp. 575–599, 1999. [Crossref]
[5] Chien, S. & Schonfeld, P., Joint optimization of a rail transit line and its feeder bus system. Journal of Advanced Transportation, 32(3), pp. 253–284, 1998. DOI: 10.1002/ atr.5670320302.
[6] Kuan, S.N., Ong, H.L. & Ng, K.M., Solving the feeder bus network design problem by genetic algorithms and ant colony optimization. Advances in Engineering Software 37(6), pp. 351–359, 2006. [Crossref]
[7] Shrivastav, P. & Dhingra, S.L., Development of feeder routes for suburban railway stations using heuristic approach. Journal of Transportation Engineering, 127(4), pp. 334–341, 2001. [Crossref]
[8] D’Acierno, L., Gallo, M., Montella, B. & Placido, A., The definition of a model framework for managing rail systems in the case of breakdowns. Proceedings of the 16th IEEE Conference on Intelligent Transportation Systems (ITSC), The Hague, The Netherlands, pp. 1059–1064, 2013. [Crossref]
[9] Abenoza, R.F., Cats, O. & Susilo, Y.O., Travel satisfaction with public transport: Determinants, user classes, regional disparities and their evolution. Transportation Research Part A, 95, pp. 64–84, 2017. [Crossref]
[10] dell’Olio, L., Ibeas, A. & Cecin, P., The quality of service desired by public transport users. Transport Policy, 18(1), pp. 217–227, 2011. [Crossref]
[11] de Ona, J. & de Ona, R., Quality of service in public transport based on customer satisfaction surveys: A review and assessment of methodological approaches. Transportation Science, 49(3), pp. 605–622, 2014. [Crossref]
[12] Consilvio, A., Di Febbraro, A. & Sacco, N., Stochastic scheduling approach for predictive risk-based railway maintenance. Proceedings of 2016 IEEE International Conference on Intelligent Rail Transportation (ICIRT), Birmingham, UK, August 2016. [Crossref]
[13] Pereira, P., Ribeiro, R.P. & Gama, J., Failure prediction: An application in the railway industry. Lecture Notes in Computer Science, 8777, pp. 264–275, 2014. [Crossref]
[14] Corman, F. & Meng, L., A review of online dynamic models and algorithms for railway traffic management. IEEE Transactions on Intelligent Transportation Systems, 16(3), pp. 1274–1284, 2015. [Crossref]
[15] Gao, Y., Kroon, L., Schmidt, M. & Yang, L., Rescheduling a metro line in an overcrowded situation after disruptions. Transportation Research Part B, 93, pp. 425–449, 2016. [Crossref]
[16] Zhan, S., Zhao, J. & Peng, Q., Real-time train rescheduling on high-speed railway under partial segment blockages. Journal of the China Railway Society, 38(10), pp. 1–13, 2016. [Crossref]
[17] Cacchiani, V., Huisman, D., Kidd, M., Kroon, L., Toth, P., Veelenturf, L. & Wagenaar, J., An overview of recovery models and algorithms for real-time railway rescheduling. Transportation Research Part B, 63, pp. 15–37, 2014. [Crossref]
[18] Dollevoet, T., Huisman, D., Schmidt, M. & Schöbel, A., Delay management with rerouting of passengers. Transportation Science, 46(1), pp. 74–89, 2012. DOI: 10.1287/ trsc.1110.0375.
[19] Corman F., D’Ariano A., Pacciarelli D. & Pranzo, M., A tabu search algorithm for rerouting trains during rail operations. Transportation Research Part B, 44(1), pp. 175–192, 2010. [Crossref]
[20] Ghaemi N., Goverde R.M.P. & Cats O., Railway disruption timetable: Short-turnings in case of complete blockage. Proceedings of 2016 IEEE International Conference on Intelligent Rail Transportation (ICIRT), Birmingham, UK, August 2016. DOI: 10.1109/ ICIRT.2016.7588734.
[21] Montella, B., Gallo, M. & D’Acierno, L., Multimodal network design problems. Advances in Transport, 5, pp. 405–414, 2000. [Crossref]
[22] D’Acierno L., Placido A., Botte M. & Montella B., A methodological approach for managing rail disruptions with different perspectives. International Journal of Mathematical Models and Methods in Applied Sciences, 10, pp. 80–86, 2016. http://www. naun.org/main/NAUN/ijmmas/2016/a202001-419.pdf
[23] Botte M., Di Salvo C., Placido A., Montella B. & D’Acierno L., A neighbourhood search algorithm for determining optimal intervention strategies in the case of metro system failures. International Journal of Transport Development and Integration, 1(1), pp. 63–73, 2017. [Crossref]
[24] Cadarso, L. & Marìn, A., Improved rapid transit network design model: considering transfer effects. Annals of Operations Research, forthcoming. 015-1999-x. [Crossref]
[25] Lee, Y.J. & Vuchic, V.R., Transit network design with variable demand. Journal of Transportation Engineering, 131(1), pp. 1–10, 2006. 947X(2005)131:1(1). [Crossref]
[26] Marìn, A.G. & Garcìa-Ròdenas, R., Location of infrastructure in urban railway networks. Computers & Operations Research, 36(5), pp. 1461–1477, 2009. cor.2008.02.008. [Crossref]
[27] Cantarella, G.E., A general fixed-point approach to multimode multi-user equilibrium assignment with elastic demand. Transportation Science, 31(2), pp. 107–128, 1997. [Crossref]
[28] D’Acierno, L., Gallo, M. & Montella, B., Ant Colony Optimisation approaches for the transportation assignment problem. WIT Transaction on the Built Environment, 111, pp. 37–48, 2010. [Crossref]
[29] Nguyen, S., Pallottino, S. & Gendreau, M., Implicit enumeration of hyperpaths in a logit model for transit networks. Transportation Science, 32(1), pp. 54–64, 1998. [Crossref]
[30] Nuzzolo, A., Russo, F. & Crisalli, U., A doubly dynamic schedule-based assignment model for transit networks. Transportation Sciences, 35(3), pp. 268–285, 2001. [Crossref]
[31] D’Acierno, L., Gallo, M., Montella, B. & Placido, A., Analysis of the interaction between travel demand and rail capacity constraints. WIT Transactions on the Built Environment, 128, pp. 197–207, 2012. [Crossref]
[32] Kunimatsu, T., Hirai, C. & Tomii, N., Train timetable evaluation from the viewpoint of passengers by microsimulation of train operation and passenger flow. Electrical Engineering in Japan, 181(4), pp. 51–62, 2012. [Crossref]
[33] D’Acierno, L., Botte, M., Placido, A., Caropreso, C. & Montella, B., Methodology for determining dwell times consistent with passenger flows in the case of metro services. Urban Rail Transit, 3(2), pp. 73–89, 2017. [Crossref]
[34] Ercolani, M., Placido, A., D’Acierno, L. & Montella, B., The use of microsimulation models for the planning and management of metro systems. WIT Transactions on the Built Environment, 135, pp. 509–521, 2014. [Crossref]
[35] Placido, A. & D’Acierno, L., A methodology for assessing the feasibility of fleet compositions with dynamic demand. Transportation Research Procedia, 10, pp. 595–604, 2015. [Crossref]
[36] Caropreso, C., Di Salvo, C., Botte, M. & D’Acierno, L., A long term analysis of passenger flows on a regional rail line. International Journal of Transport Development and Integration, 1(3), pp. 329–338, 2017. [Crossref]
[37] Cascetta, E., Papola, A., Marzano, V., Simonelli, F. & Vitiello, I., Quasi-dynamic estimation of o–d flows from traffic counts: Formulation, statistical validation and performance analysis on real data. Transportation Research Part B, 55, pp. 171–187, 2013. [Crossref]
[38] Di Mauro, R., Botte, M. & D’Acierno, L., An analytical methodology for extending passenger counts in a metro system. International Journal of Transport Development and Integration, 1(3), pp. 589–600, 2017. [Crossref]
Cite this:
APA Style
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BibTex Style
MLA Style
Chicago Style
GB-T-7714-2015
D’Acierno, L., Botte, M., & Montella, B. (2018). Assumptions and Simulation of Passenger Behaviour on Rail Platforms. Int. J. Transp. Dev. Integr., 2(2), 123-135. https://doi.org/10.2495/TDI-V2-N2-123-135
L. D’Acierno, M. Botte, and B. Montella, "Assumptions and Simulation of Passenger Behaviour on Rail Platforms," Int. J. Transp. Dev. Integr., vol. 2, no. 2, pp. 123-135, 2018. https://doi.org/10.2495/TDI-V2-N2-123-135
@research-article{D’acierno2018AssumptionsAS,
title={Assumptions and Simulation of Passenger Behaviour on Rail Platforms},
author={L. D’Acierno and M. Botte and B. Montella},
journal={International Journal of Transport Development and Integration},
year={2018},
page={123-135},
doi={https://doi.org/10.2495/TDI-V2-N2-123-135}
}
L. D’Acierno, et al. "Assumptions and Simulation of Passenger Behaviour on Rail Platforms." International Journal of Transport Development and Integration, v 2, pp 123-135. doi: https://doi.org/10.2495/TDI-V2-N2-123-135
L. D’Acierno, M. Botte and B. Montella. "Assumptions and Simulation of Passenger Behaviour on Rail Platforms." International Journal of Transport Development and Integration, 2, (2018): 123-135. doi: https://doi.org/10.2495/TDI-V2-N2-123-135
D’ACIERNO L， BOTTE M， MONTELLA B. Assumptions and Simulation of Passenger Behaviour on Rail Platforms[J]. International Journal of Transport Development and Integration， 2018， 2(2)： 123-135. https://doi.org/10.2495/TDI-V2-N2-123-135