Site Selection of Car Parking with the GIS-Based Fuzzy Multi-Criteria Decision Making

Alireza Dehghani () and Ali Soltani
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Alireza Dehghani: Department of Urban, Planning Faculty of Art & Architecture Shiraz University, Shiraz, Iran
Ali Soltani: FHMRI Injury Studies, Bedford Park, South Australia 5042, Australia UniSA Business, UniSA, Adelaide, Australia Shiraz University, Shiraz, Iran

International Journal of Information Technology & Decision Making (IJITDM), 2024, vol. 23, issue 02, 715-740

Abstract: Rapid motorization and uncertainty in urban growth patterns make parking space management a serious task, especially in middle-income developing countries, and this has severe social, economic, and environmental repercussions including increased congestion, crash frequency, fuel and time consumption, and air pollution. Due to the complexity of the urban transportation issue and the wide variety of variables involved, a multicriteria assessment is essential. This study used fuzzy logic and geographical information systems (GIS) to develop a multi-criteria decision making (MCDM) model for managing parking in Shiraz’s central business district (CBD). The literature was mined for information on the variables that affect parking site placement, and a poll of experts (n = 11) was used to determine their relative importance. The distance to travel attraction centers, distance to roads, land price, population density, and available land for multi-storey parking were among the factors considered. Meanwhile, the parking space shortage for each TAZ is calculated by subtracting the estimated parking space supply from the estimated parking space demand. An overlay of these two layers distinguishes locations that are in parking shortage zones and also meet multiple criteria. The results may aid policymakers in controlling parking demand by pinpointing the most promising places for investment.

Keywords: Traffic management; parking demand estimation; multi-storey parking; site selection; GIS; fuzzy MCDM (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219622023500293

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