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GIS-based highway maintenance prioritization model: an integrated approach for highway maintenance in Nepal mountains

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  • Pantha, Bhoj Raj
  • Yatabe, Ryuichi
  • Bhandary, Netra Prakash

Abstract

Roadside slope failures often result in day to week-long traffic disruption and are a major problem in Nepal so that timely maintenance of roadside slopes needs greater priority for smooth operation of traffic. In this paper, we develop a Geographic Information Systems (GIS)-based maintenance model considering pavement and roadside slope stability conditions. A pavement maintenance priority map is prepared based on pavement condition. International Roughness Index (IRI) is used as the pavement condition index. Similarly, a roadside slope maintenance priority map is produced by analyzing slope stability condition. An integrated maintenance priority map is produced by combining both pavement and roadside slope maintenance priority maps with consideration of weighting of each maintenance component. The integrated maintenance priority index developed in the research lies from 1 to 3, and is distributed heterogeneously. Higher index values qualify for higher priority in maintenance planning. Thus, a conventional method of maintenance planning under the constraints of budget, time and resources can be improved by incorporating the actual condition of pavement and roadside slope failure susceptibility, which has the potential to improve significantly the serviceability of the road system. The study shows that GIS, which can manage and visualize different types of data together or separately, can help the decision making process for road maintenance planning in the Himalayan regions.

Suggested Citation

  • Pantha, Bhoj Raj & Yatabe, Ryuichi & Bhandary, Netra Prakash, 2010. "GIS-based highway maintenance prioritization model: an integrated approach for highway maintenance in Nepal mountains," Journal of Transport Geography, Elsevier, vol. 18(3), pages 426-433.
    • Handle: RePEc:eee:jotrge:v:18:y:2010:i:3:p:426-433
    DOI: 10.1016/j.jtrangeo.2009.06.016
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    References listed on IDEAS

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    Cited by:

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    2. Takumi Asada & Tran Vinh Ha & Mikiharu Arimura & Shuichi Kameyama, 2022. "A Novel Approach for Urban Road Network Maintenance Plans Using Spatial Autocorrelation Analysis and Roadside Conditions: A Case Study of Muroran City, Japan," Sustainability, MDPI, vol. 14(23), pages 1-17, December.
    3. Prabin Kayastha & Megh Dhital & Florimond Smedt, 2012. "Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 479-498, September.
    4. Agostino Torti & Marika Arena & Giovanni Azzone & Piercesare Secchi & Simone Vantini, 2022. "Bridge closure in the road network of Lombardy: a spatio-temporal analysis of the socio-economic impacts," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 901-923, October.
    5. Rodrigues, Daniel Souto & Ribeiro, Paulo Jorge Gomes & da Silva Nogueira, Isabel Cristina, 2015. "Safety classification using GIS in decision-making process to define priority road interventions," Journal of Transport Geography, Elsevier, vol. 43(C), pages 101-110.
    6. Chen, Cong & Zhang, Su & Zhang, Guohui & Bogus, Susan M. & Valentin, Vanessa, 2014. "Discovering temporal and spatial patterns and characteristics of pavement distress condition data on major corridors in New Mexico," Journal of Transport Geography, Elsevier, vol. 38(C), pages 148-158.

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