A Dempster–Shafer Enhanced Framework for Urban Road Planning Using a Model-Based Digital Twin and MCDM Techniques

Bibliographic Data

ID22033013
AuthorsZahra Maserrat (K.N.Toosi University of Technology), Ali Asghar Alesheikh (0000-0001-9537-9401, K.N.Toosi University of Technology, corresponding author), Ali Jafari (0000-0002-9488-4972, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 1996715433, Iran), Ali Asghar Jafari (0000-0002-4048-3268, K.N.Toosi University of Technology), Neda Kaffash Charandabi (0000-0002-6281-9009, University of Tabriz), Javad Shahidinejad (0009-0007-0402-8229, The University of Melbourne)
Year2024
Volume13
Issue9
Pages302
Publication date2024-08-25
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi13090302
OpenAlexW4401891935
LanguageEN
References cited42

Rapid urbanization in developing countries presents a critical challenge in the need for extensive and appropriate road expansion, which in turn contributes to traffic congestion and air pollution. Urban areas are economic engines, but their efficiency and livability rely on well-designed road networks. This study proposes a novel approach to urban road planning that leverages the power of several innovative techniques. The cornerstone of this approach is a digital twin model of the urban environment. This digital twin model facilitates the evaluation and comparison of road development proposals. To support informed decision-making, a multi-criteria decision-making (MCDM) framework is used, enabling planners to consider various factors such as traffic flow, environmental impact, and economic considerations. Spatial data and 3D visualizations are also provided to enrich the analysis. Finally, the Dempster–Shafer theory (DST) provides a robust mathematical framework to address uncertainties inherent in the weighting process. The proposed approach was applied to planning for both new road constructions and existing road expansions. By combining these elements, the model offers a sustainable and knowledge-based approach to optimize urban road planning. Results from integrating weights obtained through two weighting methods, the Analytic Hierarchy Process (AHP) and the Bayesian best–worst Method (B-BWM), showed a very high weight for the “worn-out urban texture” criterion and a meager weight for “noise pollution”. Finally, the cost path algorithm was used to evaluate the results from all three methods (AHP, B-BWM, and DST). The high degree of similarity in the results from these methods suggests a stable outcome for the proposed approach. Analysis of the study area revealed the following significant challenge for road planning: 35% of the area was deemed unsuitable, with only a tiny portion (4%) being suitable for road development based on the selected criteria. This highlights the need to explore alternative approaches or significantly adjust the current planning process

Analytic hierarchy process · Civil engineering · Data mining · Dempster–Shafer theory · Multiple-criteria decision analysis · Operations research · TOPSIS · Transport engineering · Urban planning · Weighting · Computer Science · Engineering · Infrastructure Maintenance and Monitoring · Urban Transport Systems Analysis

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