Geospatial data analysis for determining optimal road construction routes: A comparative study

Abstract

The Geographic Information System (GIS) provides robust decision assistance for consumers obtaining the best route through route planning analysis; determining the best path is a crucial advanced analysis function. The purpose of this research is to create a comparative analysis of geospatial data approaches for determining the best routes to construct roads in various types of environments. Three different methodologies: analytical hierarchy process (AHP), fuzzy overlay, and TOPSIS, using GIS and multi-criteria decision-making (MCDM) techniques are assessed and contrasted. To examine possible routes in a case study region, high-resolution geospatial data, including topography, land use, slopes, and hydrology, are utilized. The effectiveness of each approach is evaluated by variables, including a snow slide, watercourse, soil wash, gradient, earth composition, visibility conditions, and road features. The findings indicate that the technique for Order Preference by Similarity to Ideal Solution (TOPSIS) delivers superior environmental sensitivity, while fuzzy overlay and analytical hierarchy process (AHP) give the best-balanced solutions. Road planners can use this comparison study’s insights to make intelligent choices about infrastructure development by evaluating the advantages and disadvantages of various geospatial methodologies.

Keywords

geospatial data analysis Geographic Information Systems (GIS)road construction routes multi-criteria decision-making (MCDM)

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References

Kuo

Leung

Yan

. Public transport for smart cities: recent innovations and future challenges. European J Oper Res 2023; 306(3): 1001–1026. DOI: 10.1016/j.ejor.2022.06.057.

, et al. Expansion of city scale, traffic modes, traffic congestion, and air pollution. Cities 2021; 108: 102974. DOI: 10.1016/j.cities.2020.102974.

Krell

King

Carrillo

. Autonomous surface vehicle energy-efficient and reward-based path planning using particle swarm optimization and visibility graphs. Appl Ocean Res 2022; 122: 103125. DOI: 10.1016/j.apor.2022.103125.

Gun

Hill

Vujanic

. Coordinating multiple cooperative vehicle trajectories on shared road networks. IEEE Trans Intell Transp Syst 2022; 24(1): 274–290. DOI: 10.1109/TITS.2022.3215573.

Szántó

Hidalgo

González

, et al. Trajectory planning of automated vehicles using real-time map updates. IEEE Access 2023; 11: 67468–67481. DOI: 10.1109/ACCESS.2023.3291350.

Corazza

. A comprehensive research agenda for integrating ecological principles into the transportation sector. Sustainability 2024; 16(16): 7081. DOI: 10.3390/su16167081.

Karimi-Mamaghan

Mohammadi

Meyer

, et al. Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: a state-of-the-art. European J Oper Res 2022; 296(2): 393–422. DOI: 10.1016/j.ejor.2021.04.032.

Fathollahi-Fard

Wong

Aljuaid

. An efficient adaptive large neighborhood search algorithm based on heuristics and reformulations for the generalized quadratic assignment problem. Eng Appl Artif Intell 2023; 126: 106802. DOI: 10.1016/j.engappai.2023.106802.

Krafft

Shmueli

Griffiths

, et al. Bayesian collective learning emerges from heuristic social learning. Cognition 2021; 212: 104469. DOI: 10.1016/j.cognition.2020.104469.

10.

Daemi

Kasap

Mahmoudabadi

. Proposing risk equity approaches of risk and distance based in route selection and site location for hazardous material transportation. Case Stud Transp Policy 2022; 10(4): 2486–2496. DOI: 10.1016/j.cstp.2022.11.001.

11.

Weng

Gan

Zhang

. A quantitative risk assessment model for evaluating hazmat transportation accident risk. Safety Sci 2021; 137: 105198. DOI: 10.1016/j.ssci.2021.105198.

12.

Comber

Brunsdon

Charlton

, et al. A route map for successful applications of geographically weighted regression. Geographical Analysis 2023; 55(1): 155–178. DOI: 10.1111/gean.12316.

13.

Shang

Chen

, et al. GIS aided sustainable urban road management with a unifying queueing and neural network model. Appl Energy 2021; 291: 116818. DOI: 10.1016/j.apenergy.2021.116818.

14.

Alhammad

Sun

Tao

. Optimal solar plant site identification using GIS and remote sensing: framework and case study. Energies 2022; 15(1): 312. DOI: 10.3390/en15010312.

15.

Ustaoglu

Sisman

Aydınoglu

. Determining agricultural suitable land in peri-urban geography using GIS and Multi Criteria Decision Analysis (MCDA) techniques. Eco Modelling 2021; 455: 109610. DOI: 10.1016/j.ecolmodel.2021.109610.

16.

Scott

Brown

. Route choice of bike share users: leveraging GPS data to derive choice sets. J Transp Geo 2021; 90: 102903. DOI: 10.1016/j.jtrangeo.2020.102903.

17.

Lovelace

. Open source tools for geographic analysis in transport planning. J Geogr Syst 2021; 23(4): 547–578. DOI: 10.1007/s10109-020-00342-2.

18.

Jiang

Broyd

, et al. Digital twin enabled sustainable urban road planning. Sustain Cities Soc 2022; 78: 103645. DOI: 10.1016/j.scs.2021.103645.

19.

Shorabeh

Samany

Minaei

, et al. A decision model based on decision tree and particle swarm optimization algorithms to identify optimal locations for solar power plants construction in Iran. Ren Energy 2022; 187: 56–67. DOI: 10.1016/j.renene.2022.01.011.

20.

Yang

Dao

, et al. BIM-GIS-DCEs enabled vulnerability assessment of interdependent infrastructures–a case of stormwater drainage-building-road transport Nexus in urban flooding. Autom Constr 2021; 125: 103626. DOI: 10.1016/j.autcon.2021.103626.

21.

Elboshy

Alwetaishi

Aly

, et al. A suitability mapping for the PV solar farms in Egypt based on GIS-AHP to optimize multi-criteria feasibility. Ain Shams Eng J 2022; 13(3): 101618. DOI: 10.1016/j.asej.2021.10.013.

22.

Gmira

Gendreau

Lodi

, et al. Tabu search for the time-dependent vehicle routing problem with time windows on a road network. European J Oper Res 2021; 288(1): 129–140. DOI: 10.1016/j.ejor.2020.05.041.

23.

Simões

Cavalcante

Lima

, et al. Optimal forest road density as decision-making factor in wood extraction. Forests 2022; 13(10): 1703. DOI: 10.3390/f13101703.

24.

Meskini

Remmal

Ejjaouani

, et al. Formulation and optimization of a phosphogypsum–fly ash–lime composite for road construction: a statistical mixture design approach. Constr Buil Materials 2022; 315: 125786. DOI: 10.1016/j.conbuildmat.2021.125786.

25.

Renjith

Robert

Setunge

, et al. Optimization of fly ash based soil stabilization using secondary admixtures for sustainable road construction. J Cleaner Produc 2021; 294: 126264. DOI: 10.1016/j.jclepro.2021.126264.