Main Article Content

Abstract

Improper location of gas stations leads to waste of resources, time, and user dissatisfaction. On the other hand, the optimal location of these facilities will have a significant impact not only on the quality of traffic in the network but also on their economic success. The aim of this research is the spatial-physical organization of inner-city structures with an emphasis on the location of gas stations using the weighted linear integrated model method on the GIS platform using the descriptive-analytical method. First, the location of the existing stations and the areas that need gas stations were determined using the weighted linear integrated model (WLC) and ArcGIS. A scoring-based method was used to convert the maps into a standard scale ranging from 0 to 1 and 0 to 255. The Analytical Hierarchy Process (AHP) method and the Expert Choice app were used to determine the criteria weights. Then, the GIS and WLC capability to provide a suitable model for locating stations was tested. The result states that for the construction of gas stations, the Bahmanyar region will be the priority. North Khani Abad region is the second priority, and South Khani Abad and Esfandiari regions are the following priorities. Finally, with the local investigation of the prioritized areas by WLC, it was found that these areas are suitable for constructing gas stations. This method can be used for finding a suitable location for gas station construction in all other cases.

Keywords

Geographic Information System (GIS) WLC model Localization Gas station

Article Details

How to Cite
Estelaji, F., Naseri, A., Keshavarzzadeh, M., Zahedi, R., Yousefi, H., & Ahmadi, A. (2023). Potential measurement and spatial priorities determination for gas station construction using WLC and GIS. Future Technology, 2(4), 24–32. Retrieved from https://fupubco.com/futech/article/view/98
Bookmark and Share

References

  1. Zahedi, R., S. Daneshgar, and S. Golivari, Simulation and optimization of electricity generation by waste to energy unit in Tehran. Sustainable Energy Technologies and Assessments, 2022. 53: p. 102338.
  2. Estelaji, F., A. Naseri, and R. Zahedi, Evaluation of the Performance of Vital Services in Urban Crisis Management. Advances in Environmental and Engineering Research, 2022. 3(4): p. 1-19
  3. Aguilera, T., F. Artioli, and C. Colomb, Explaining the diversity of policy responses to platform-mediated short-term rentals in European cities: A comparison of Barcelona, Paris and Milan. Environment and Planning A: Economy and Space, 2021. 53(7): p. 1689-1712.
  4. Evans, D., M. Stephenson, and R. Shaw, The present and future use of ‘land’below ground. Land Use Policy, 2009. 26: p. S302-S316.
  5. Jorge, D., G. Molnar, and G.H. de Almeida Correia, Trip pricing of one-way station-based carsharing networks with zone and time of day price variations. Transportation Research Part B: Methodological, 2015. 81: p. 461-482.
  6. Gunawan, R.K., A Study of Spatiotemporal Distribution of Mobility-On-Demand in Generating Pick-Up/Drop-Offs Location Placement. Smart Cities, 2021. 4(2): p. 746-766.
  7. Nama, M., et al., Machine learning‐based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges. International Journal of Communication Systems, 2021. 34(9): p. e4814.
  8. Zahedi, R., A. Ahmadi, and S. Gitifar, Feasibility study of biodiesel production from oilseeds in Tehran province. Journal of Renewable and New Energy, 2022 Nov 11.
  9. Bonevski, B., et al., Reaching the hard-to-reach: a systematic review of strategies for improving health and medical research with socially disadvantaged groups. BMC medical research methodology, 2014. 14(1): p. 1-29.
  10. Marušić, B.G. and D. Marušić, Behavioural maps and GIS in place evaluation and design. Application of geographic information systems, 2012: p. 115-138.
  11. Özmen, M. and E.K. Aydoğan, Robust multi-criteria decision making methodology for real life logistics center location problem. Artificial Intelligence Review, 2020. 53(1): p. 725-751.
  12. Angeli, D., R. Amrit, and J.B. Rawlings, On average performance and stability of economic model predictive control. IEEE transactions on automatic control, 2011. 57(7): p. 1615-1626.
  13. Zahedi, R., et al., Modeling and interpretation of geomagnetic data related to geothermal sources, Northwest of Delijan. Renewable Energy, 2022.
  14. Shorabeh, S.N., et al., Spatial modeling of areas suitable for public libraries construction by integration of GIS and multi-attribute decision making: Case study Tehran, Iran. Library & Information Science Research, 2020. 42(2): p. 101017.
  15. Mat, N.A., A.M. Benjamin, and S. Abdul-Rahman, A review on criteria and decision-making techniques in solving landfill site selection problems. Journal of Advanced Review on Scientific Research, 2017. 37(1): p. 14-32.
  16. Foroozesh, F., et al., Assessment of sustainable urban development based on a hybrid decision-making approach: Group fuzzy BWM, AHP, and TOPSIS–GIS. Sustainable Cities and Society, 2022. 76: p. 103402.
  17. Shafiei Nikabadi, M. and F. Hashemi, Site selection for multi-story car parks with emphasis on urban sustainable development management. Road, 2021. 29(109): p. 123-140.
  18. Mohammad, A. and A.A. Ali, Site selection for small gas stations using GIS. Scientific Research and Essays, 2011. 6(15): p. 3161-3171.
  19. Zahedi, R. and S. Golivari, Investigating Threats to Power Plants Using a Carver Matrix and Providing Solutions: A Case Study of Iran. International Journal of Sustainable Energy and Environmental Research, 2022. 11(1): p. 23-36.
  20. Zhu, Y., et al., Analysis and assessment of the Qingdao crude oil vapor explosion accident: lessons learnt. Journal of Loss Prevention in the Process Industries, 2015. 33: p. 289-303.
  21. Manisalidis, I., et al., Environmental and health impacts of air pollution: a review. Frontiers in public health, 2020: p. 14.
  22. Sharpe, R., et al., Household energy efficiency and health: Area-level analysis of hospital admissions in England. Environment international, 2019. 133: p. 105164.
  23. Daneshgar, S., R. Zahedi, and O. Farahani, Evaluation of the concentration of suspended particles in underground subway stations in Tehran and its comparison with ambient concentrations. Ann Environ Sci Toxicol, 2022. 6(1): p. 019-025.
  24. Xing, Y. and P. Brimblecombe, Urban park layout and exposure to traffic-derived air pollutants. Landscape and Urban Planning, 2020. 194: p. 103682.
  25. Moeinaddini, M., et al., Siting MSW landfill using weighted linear combination and analytical hierarchy process (AHP) methodology in GIS environment (case study: Karaj). Waste management, 2010. 30(5): p. 912-920.
  26. Abdulkareem, K.H
  27. Gemitzi, A., et al., Assessment of groundwater vulnerability to pollution: a combination of GIS, fuzzy logic and decision making techniques. Environmental Geology, 2006. 49(5): p. 653-673.
  28. Piasecki, K., E. Roszkowska, and A. Łyczkowska-Hanćkowiak, Simple additive weighting method equipped with fuzzy ranking of evaluated alternatives. Symmetry, 2019. 11(4): p. 482.
  29. Wieczorek, W.F. and A.M. Delmerico, Geographic information systems. Wiley Interdisciplinary Reviews: Computational Statistics, 2009. 1(2): p. 167-186.