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A multi-objective genetic algorithm approach to design optimal zoning systems for freight transportation planning

Chandra, A and Sharath, MN and Pani, A and Sahu, PK (2021) A multi-objective genetic algorithm approach to design optimal zoning systems for freight transportation planning. In: Journal of Transport Geography, 92 .

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Official URL: https://doi.org/10.1016/j.jtrangeo.2021.103037


This paper contributes to the existing research on freight transportation, spatial and land use planning by investigating an improved spatial aggregation technique to delineate desirable freight traffic analysis zones. Zoning is a process of spatially aggregating several predefined basic spatial units (BSUs) into multiple zones. It plays a vital role in the transportation planning and decision-making process and is well-documented as the modifiable areal unit problem (MAUP). MAUP involves aggregating BSUs to obtain optimal zones satisfying specific criteria and constraints. This paper proposes an improved spatial aggregation methodology to develop a freight traffic analysis zone system by applying the multiobjective optimization concept using a genetic algorithm. The decision variables, namely, (i) Freight trip density; (ii) Number of establishments; (iii) Employment density; and (iv) Compactness, are chosen to represent the elements of freight, passenger traffic, and land use. The problem is formulated as a multiobjective network partitioning problem. The four objectives aim to create zones with better homogeneity and compactness. It is solved using a genetic algorithm with a weighted distance metric approach to prioritize the four objectives. Results show that zones resulting from the improved methodology are superior to the existing zones in terms of homogeneity of decision variables and compactness. The findings are expected to help the decision-making process of urban, transportation, and land-use planners in selecting appropriate freight traffic zone delineation strategies for a given region. © 2021 Elsevier Ltd

Item Type: Journal Article
Publication: Journal of Transport Geography
Publisher: Elsevier Ltd
Additional Information: The copyright for this article belongs to Elsevier Ltd.
Keywords: freight transport; genetic algorithm; land use change; optimization; traffic management; transportation development; transportation planning; zoning system
Department/Centre: Division of Mechanical Sciences > Civil Engineering
Date Deposited: 22 Feb 2023 03:33
Last Modified: 22 Feb 2023 03:33
URI: https://eprints.iisc.ac.in/id/eprint/80428

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