Abstract
Emergency road networks (ERNs), an important part of local disaster prevention systems, can provide security to residents and their property. Exploring the ERNs structure is of great significance in terms of promoting disaster prevention and establishing road safety in dangerous mountainous areas. This study considered the ERNs of the Kangding section of the Dadu River Basin as the area for a case study. Complex Network Analysis was used to examine the relationship between the four characteristic indicators of mountain roads and the degree of earthquake impacts under the Lushan, Wenchuan, and Kangding Earthquake scenarios. Based on the analysis results, the southwest mountain road network was evaluated; then, computer simulations were used to evaluate the structural changes in the road network after index changes. The network was optimized, and the corresponding emergency avoidance network was proposed to provide a reference for the establishment of the mountainous ERN. The results show that the overall completeness of the mountainous ERNs in Southwest China is poor and prone to traffic accidents. Moreover, the local stability is poor, and the network is susceptible to natural hazards. The overall structure of the road network is balanced, but that of certain road sections is not. Road sections with different attributes present a “gathering-scattering” spatial distribution, i.e, some sections are clustered together while others are far apart. Accordingly, a planning optimization strategy is proposed to better understand the complexity and systematic nature of the mountainous ERN as a whole and to provide a reference for disaster prevention and mitigation planning in mountainous regions in Southwest China.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ball MO, Nemhauser GL (1979) Matroids and a reliability analysis problem. Math Operations Res 4(2): 132–143. https://doi.org/10.1287/moor.4.2.132
Bell M (2000) A game theory approach to measuring the performance reliability of transport networks. Transp Res Part B-Methodological 34(6): 533–545. https://doi.org/10.1016/S0191-2615(99)00042-9
Benrhaiem W, Hafid A, Sahu PK (2020) Reliable emergency message dissemination scheme for urban vehicular networks. IEEE Trans Intelligent Transp Syst 21(3): 1154–1166. https://doi.org/10.1109/TITS.2019.2902850
Chen A, Hai Y, Hong K L, et al. (2002) Capacity reliability of a road network: an assessment methodology and numerical results. Transp Res Part B 36(3): 225–252. https://doi.org/10.1016/S0191-2615(00)00048-5
Choi JH, Barnett GA, Chon BS (2010) Comparing world city networks: a network analysis of Internet backbone and air transport intercity linkages. Glob Networks 6(1):81–99. https://doi.org/10.1111/j.1471-0374.2006.00134.x
Derrible S, Kennedy C (2012) The complexity and robustness of metro networks. Phys A-Stat Mech Its Appl 389(17): 3678–3691. https://doi.org/10.1016/j.physa.2010.04.008
Huang X, Song L (2018) An emergency logistics distribution routing model for unexpected events. Ann Operations Res 269(6): 1–17. https://doi.org/10.1007/s10479-016-2300-7
Kaviani A, Thompson RG, Rajabifard A, et al. (2018) A model for multi-class road network recovery scheduling of regional road networks. Transp 1: 1–35. https://doi.org/10.1007/s11116-017-9852-5
Kato T, Uchida K, Lam WHK, et al. (2020) Estimation of the value of travel time and of travel time reliability for heterogeneous drivers in a road network. Transp 48: 1639–1670. https://doi.org/10.1007/s11116-020-10107-x
Kroes E, Koster P, Peer S (2017) A practical method to estimate the benefits of improved road network reliability: an application to departing air passengers. Transp 4: 1–16. https://doi.org/10.1007/s11116-017-9764-4
Leng JQ, Yang LH, Liu WY, et al. (2017) Measuring road network vulnerability with sensitivity analysis. PLOS ONE 12(1). https://doi.org/10.1371/journal.pone.0170292
Liu W, Hu G, Li J (2012) Emergency resources demand prediction using case-based reasoning. Safety Sci 50(3): 530–534. https://doi.org/10.1016/j.ssci.2011.11.007
Manfre LA, Cruz BB, Quintanilha JA (2020) Urban settlements and road network analysis on the surrounding area of the Almirante Alvaro Alberto Nuclear Complex, Angra dos Reis, Brazil. Appl Spat Anal Policy 13(1): 209–221. https://doi.org/10.1007/s12061-019-09299-2
Menoni S (2001) Chains of damages and failures in a metropolitan environment: some observations on the Kobe earthquake in 1995. J Hazardous Mater 86(1–3):101–119. https://doi.org/10.1016/S0304-3894(01)00257-6
Mooselu MG, Liltved H, Nikoo MR, et al. (2020) Assessing optimal water quality monitoring network in road construction using integrated information-theoretic techniques. J Hydrol 589. https://doi.org/10.1016/j.jhydrol.2020.125366
Sarma SS, Sinha K, Sub-R-Pa C, et al. (2020) Optimal distribution of traffic in manhattan road networks for minimizing routingtime. IEEE Transactions on Intelligent Transportation Systems 99: 1–22. https://doi.org/10.1109/TITS.2020.2994836
Sheu JB (2007) An emergency logistics distribution approach for quick response to urgent relief demand in disasters. Transp Res Part E Logistics & Transp Rev 43(6): 687–709. https://doi.org/10.1016/j.tre.2006.04.004
Soltani-Sobh A, Heaslip K, Stevanovic A, et al. (2016) Evaluation of transportation network reliability during unexpected events with multiple uncertainties. Int J Disaster Risk Reduction 17: 128–136. https://doi.org/10.1016/j.ijdrr.2016.04.011
Von Ferber C, Berche B, Holovatch T, et al. (2012) A tale of two cities. vulnerabilities of the London and Paris transit networks. Computer Science 4(1)74–89. https://doi.org/10.1007/s12198-012-0092-9
Wang Y, Wang J (2020) Measuring and maximizing resilience of transportation systems for emergency evacuation. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2019.2949098
Wellman B (2008) The development of social network analysis: A study in the sociology of science. Contemporary Sociology 37(3): 221. https://doi.org/10.1177/009430610803700308
Yang S, Hu F, Thompson RG, et al. (2018) Criticality ranking for components of a transportation network at risk from tropical cyclones. Int J Disaster Risk Reduction 28. https://doi.org/10.1016/j.ijdrr.2018.02.017
Yang Y, Liu Y, Zhou M, et al. (2015) Robustness assessment of urban rail transit based on complex network theory: A case study of the Beijing Subway. Safety Sci 79: 149–162. https://doi.org/10.1016/j.ssci.2015.06.006
Yucel E, Salman F S, Arsik I (2018) Improving post-disaster road network accessibility by strengthening links against failures. Eur J Operational Res 269(2): 406–422. https://doi.org/10.1016/j.ejor.2018.02.015
Zhang J, Wang S, Zhang Z, et al. (2016) Characteristics on hub networks of urban rail transit networks. Phys A Stat Mech Appl 447: 502–507. https://doi.org/10.1016/j.physa.2015.12.060
Zio E, Golea LR (2012) Analyzing the topological, electrical and reliability characteristics of a power transmission system for identifying its critical elements. Reliab Eng Syst Saf 101(1): 67–74. https://doi.org/10.1016/j.ress.2011.11.009
Acknowledgements
This research was jointly supported by the National Key R&D Program of China (2018YFD1100804). Cordial thanks go to two anonymous reviewers and editor of the journal for their valuable comments and suggestions that greatly improved the quality of this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wei, M., Huang, Y., Wan, D. et al. Emergency road network structure and planning optimization in mountainous regions in Southwest China under earthquake scenarios. J. Mt. Sci. 19, 771–780 (2022). https://doi.org/10.1007/s11629-020-6588-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11629-020-6588-z