Skip to main content
Log in

Dynamic Vulnerability Analysis of Public Transport Networks: Mitigation Effects of Real-Time Information

Networks and Spatial Economics Aims and scope Submit manuscript

Abstract

In this paper, a dynamic and stochastic notion of public transport network vulnerability is developed. While previous studies have considered only the network topology, the granular nature of services requires a more refined model for supply and demand interactions in order to evaluate the impacts of disruptions. We extend the measures of betweenness centrality (often used to identify potentially important links) and link importance to a dynamic-stochastic setting from the perspectives of both operators and passengers. We also formalize the value of real-time information (RTI) provision for reducing disruption impacts. The developed measures are applied in a case study for the high-frequency public transport network of Stockholm, Sweden. The importance ranking of the links varies depending on the RTI provision scheme. The results suggest that betweenness centrality (passenger/vehicle flows) may not be a good indicator of link importance. The results of the case study reveal that while service disruptions have negative effects and RTI may have significant positive influence, counter examples also exist due to secondary spillover effects.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  • Angeloudis P, Fisk D (2006) Large subway systems as complex networks. Phys A Stat Mech Appl 3:553–558

    Article  Google Scholar 

  • Ball MO, Golden BL, Vohra RV (1989) Finding the most vital arcs in a network. Oper Res Lett 8:73–76

    Article  Google Scholar 

  • Berche B, von Ferber C, Holovatch T, Holovatch Y (2009) Resilience of public transport networks against attacks. Eur Phys J B 71:125–137

    Google Scholar 

  • Berdica K (2002) An introduction to road vulnerability: what has been done, is done and should be done. Transp Policy 9:117–127

    Article  Google Scholar 

  • Carson Y, Maria A (1997) Simulation optimization: methods and applications. Proceedings of the 1997 Winter Simulation Conference, 118–126

  • Cats O (2013) Multi-agent transit operations and assignment model. Procedia Comput Sci 19:809–814, The 2nd International Workshop on Agent-based Mobility, Traffic and Transportation Models, Methodologies and Applications (ABMTRANS)

    Article  Google Scholar 

  • Cats O, Koutsopoulos HN, Burghout W, Toledo T (2011a) Effect of real-time transit information on dynamic passenger path choice. Transp Res Rec 2217:46–54

    Article  Google Scholar 

  • Cats O, Larijani AN, Burghout W, Koutsopoulos HN (2011b) Impacts of holding control strategies on transit performance: a bus simulation model analysis. Transp Res Rec 2216:51–58

    Article  Google Scholar 

  • Colak S, Lus H, Atligan AR (2010) Vulnerability of networks against critical links failure. Available at: http://arxiv.org/abs/1012.5961v2

  • Criado R, Hernández-Bermejo B, Romance M (2007) Efficiency, vulnerability and cost: an overview with applications to subway networks worldwide. Int J Bifurcation Chaos 17:2289–2301

    Article  Google Scholar 

  • Crucitti P, Latora V, Porta S (2007) Centrality measures in spatial networks of urban streets. Phys Rev E 73:1–5

    Google Scholar 

  • Derrible S, Kennedy C (2010) The complexity and robustness of metro networks. Physica A 389:3678–3691

    Article  Google Scholar 

  • Dowling R, Skabardonis A, Alexiadis V (2004) Traffic analysis toolbox volume III: guidelines for applying traffic microsimulation modeling software. U.S. Department of Transportation, Federal Highway Administration (FHA), Washington DC

    Google Scholar 

  • Freeman LC, Borgatti SP, White DR (1991) Centrality in valued graphs: a measure of betweenness based on network flow. Soc Networks 13:141–154

    Article  Google Scholar 

  • Jenelius E, Mattsson L-G (2012) Road network vulnerability analysis of area-covering disruptions: a grid-based approach with case study. Transp Res A 46:746–760

    Google Scholar 

  • Jenelius E, Mattsson L-G (2014). Road network vulnerability analysis: Conceptualization, implementation and application. Comput Environ Urban Syst, accepted for publication.

  • Jenelius E, Petersen T, Mattsson L-G (2006) Importance and exposure in road network vulnerability analysis. Transp Res A 40:537–560

    Google Scholar 

  • Latora V, Marchiori M (2007) A measure of centrality based on network efficiency. New J Phys 9:188

    Article  Google Scholar 

  • Mahmassani HS, Jayakrishnan R (1991) System performance and user response under real-time information in a congested traffic corridor. Transp Res A 25:293–307

    Article  Google Scholar 

  • Murray-Tuite PM, Mahmassani HS (2004) Methodology for the determination of vulnerable links in a transportation network. Transp Res Rec 1882:88–96

    Article  Google Scholar 

  • Nicholson AJ, Du ZP (1994) Improving network reliability: a framework. In: Proceedings of 17th Australian Road Research Board Conference, 3:1–17

  • Ratliff HD, Sicilia GT, Lubore SH (1975) Finding the n most vital links in flow networks. Manag Sci 21:531–539

    Article  Google Scholar 

  • Scott DM, Novak DC, Aultman-Hall L, Guo F (2006) Network robustness index: a new method for identifying critical links and evaluating the performance of transportation networks. J Transp Geogr 14:215–227

    Article  Google Scholar 

  • SL – AB StorStockholms Lokaltrafik (2009) Annual report 2009. Available at: http://sl.se/Global/Pdf/Rapporter/SLfakta_2009_webb.pdf [in Swedish]

  • Sohn J (2006) Evaluating the significance of highway network links under the flood damage: an accessibility approach. Transp Res A 40:491–506

    Google Scholar 

  • Taylor MAP, Susilawati (2012) Remoteness and accessibility in the vulnerability analysis of regional road networks. Transp Res A 46:761–771

    Google Scholar 

  • Toledo T, Cats O, Burghout W, Koutsopoulos HN (2010) Mesoscopic simulation for transit operations. Transp Res C 18:896–908

    Article  Google Scholar 

  • von Ferber C, Holovatch T, Holovatch Y, Palchykov V (2009) Public transport networks: empirical analysis and modeling. Eur Phys J B 68:261–275

    Article  Google Scholar 

  • von Ferber C, Berche B, Holovatch T, Holovatch Y (2012) A tale of two cities: vulnerabilities of the London and Paris transit networks. J Transp Secur 5(3):199–216

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Lars-Göran Mattsson, participants at the 5th International Symposium on Transportation Network Reliability (INSTR), 18–19 December 2012, Hong Kong, and two anonymous reviewers for their valuable suggestions and comments on the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Oded Cats.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cats, O., Jenelius, E. Dynamic Vulnerability Analysis of Public Transport Networks: Mitigation Effects of Real-Time Information. Netw Spat Econ 14, 435–463 (2014). https://doi.org/10.1007/s11067-014-9237-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11067-014-9237-7

Keywords

Navigation