Skip to main content

Advertisement

Springer Nature Link
Log in

Snow removal resource location and allocation optimization for urban road network recovery: a resilience perspective

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Recently, the vulnerability of urban road network has become increasingly obvious in the face of natural emergencies. The extreme snow weather, a kind of natural emergencies, can severely reduce the service capability of road network. It has attracted a wide attention that how the urban road network system get recovered after such unexpected events. In this paper, we proposed the urban road network resilience evacuation method under snow event. In order to improve the resilience of road network, we establish the mathematical model for road network recovery under extreme weather to solve the snow removal resource location-allocation problem (LAP) with uncertain weather information. The routes for snow removal vehicle are determined as several Vehicle Routing Problems (VRPs). The corresponding tabu search algorithm is designed. Finally, we verify the effectiveness of proposed model and algorithm by a real case to provide decision-making support for the city traffic management departments and enhance the resilience of city in the extreme snow weather.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Aven T (2011) On Some Recent Definitions and Analysis Frameworks for Risk, Vulnerability, and Resilience. Risk Anal 31(4),515–522

    Article  Google Scholar 

  • Bles T, Bessembinder J, Chevreuilc M, Danielsson P, Falemo S, Venmans A, Ennesserc Y, Löfroth H (2016) Climate change risk assessments and adaptation for roads-results of the ROADAPT project. Transp Res Procedia 14:58–67

    Article  Google Scholar 

  • Boonmee C, Arimura M, Asada T (2017) Facility location optimization model for emergency humanitarian logistics. Int J Disaster Risk Reduct 24:485–498

    Article  Google Scholar 

  • Campbell JF (1995) Operations management for urban snow removal and disposal. Transp Res Part A Policy Pract 29(5):359–370

    Article  Google Scholar 

  • Cui JX, Liu F, Hu J, Janssens D, Wets G, Cools M (2016) Identifying mismatch between urban travel demand and transport network services using GPS data: a case study in the fast growing Chinese city of Harbin. Neurocomputing 181:4–18

    Article  Google Scholar 

  • Dussault B, Golden B, Wasil E (2014) The Downhill plow problem with multiple plows. J Oper Res Soc 65:1465–1474

    Article  Google Scholar 

  • Francis R, Bekera B (2014) A metric and frameworks for resilience analysis of engineered and infrastructure systems. Reliab Eng Syst Saf 121:90–103

    Article  Google Scholar 

  • Fu L, Trudel M, Kim V (2009) Optimization winter road maintenance operations under real time information. Eur J Oper Res 196:332–341

    Article  MATH  Google Scholar 

  • Holling CS (1973) Resilience and stability of ecological systems. Ann Rev Ecol Syst 4:1–23

    Article  Google Scholar 

  • Hu SL, Han CF, Meng LP (2015) A scenario planning approach for propositioning rescue centers for urban waterlog disasters. Comput Ind Eng 87:425–435

    Article  Google Scholar 

  • Hualou L, Tu S, Ge D, Li T, Liu Y (2016) The allocation and management of critical resources in rural China under restructuring: problems and prospects. J Rural Stud 47(Part B):392–412

    Google Scholar 

  • Ip WH, Wang D (2011) Resilience and friability of transportation networks: evaluation, analysis and optimization. IEEE Syst J 5(2):189–198

    Article  Google Scholar 

  • Jägerbrand AK, Sjöbergh J (2016) Effects of weather conditions, light conditions, and road lighting on vehicle speed. SpringerPlus 5:505

    Article  Google Scholar 

  • Levalle RR, Nof SY (2017) Resilience in supply networks: definition, dimensions, and levels. Ann Rev Control 43:224–236

    Article  Google Scholar 

  • Lv Y, Huang GH, Guo L et al (2013) A scenario-based modeling approach for emergency evacuation management and risk analysis under multiple uncertainties. J Hazard Mater 246:234–244

    Article  Google Scholar 

  • Nan C, Sansavini G (2017) A quantitative method for assessing resilience of independent infrastures. Reliab Eng Syst Saf 157:35–53

    Article  Google Scholar 

  • Perrier N, Langevin A, Campbell JF (2008) The sector design and assignment problem for snowdisposal operations. Eur J Oper Res 189:508–525

    Article  MATH  Google Scholar 

  • Salman FS, Yücel E (2015) Emergency facility location under random network damage: Insights from the Istanbul case. Comput Oper Res 62:266–281

    Article  MathSciNet  MATH  Google Scholar 

  • Starita S, Scaparra M, O’Hanley P (2017) A dynamic model for road protection against flooding. J Oper Res Soc 68(1):174–188. https://doi.org/10.1057/s41274-016-0019-0

    Article  Google Scholar 

  • Tran HT, Balchanos M, Domerçant JC et al (2017) A framework for the quantitative assessment of performance-based system Resilience. Reliab Eng Syst Saf 158:73–84

    Article  Google Scholar 

  • Warszawska K, Kraslawski A (2016) Method for quantitative assessment of safety culture. J Loss Prev Process Ind 42:27–34

    Article  Google Scholar 

  • Zhang W, Wang N (2016) Resilience-based risk mitigation for road networks. Struct Saf 62:57–65

    Article  Google Scholar 

Download references

Acknowledgements

This Project has received funding from National Social Science Foundation of China (16CGL033); Funding Program for High-level Talents of Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality of China (CIT&TCD201704033).

Author information

Authors and Affiliations

  1. Beijing Technology and Business University, Haidian, Beijing, 100048, China

    Jing Wang & Haotian Liu

Authors
  1. Jing Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Haotian Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Jing Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, J., Liu, H. Snow removal resource location and allocation optimization for urban road network recovery: a resilience perspective. J Ambient Intell Human Comput 10, 395–408 (2019). https://doi.org/10.1007/s12652-018-0717-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-018-0717-3

Keywords