Hesitant fuzzy spatial multi-criteria analysis for the site selection of micro electric car-sharing stations

Abstract Private cars are typically used by a single occupant and for less than one hour per day; therefore, this approach appears inefficient. In contrast, car-sharing systems have received considerable attention in recent years due to their economic, social, and environmental benefits. One of the major challenges in car-sharing systems is ensuring convenient access to shared vehicles. Accordingly, the main aim of the current study is to determine suitable sites for micro electric car-sharing stations. To address this issue, an integrated approach was employed, combining Geographical Information Systems (GIS) with the Hesitant Fuzzy Linguistic Stepwise Weight Assessment Ratio Analysis (HFL-SWARA) method, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Under four main groups, a total of fifteen criteria considered to affect the location of sharing stations were determined based on the literature and experts opinions. For each criterion, a separate criterion map was produced and normalized; subsequently, a suitability map was generated based on the fifteen criterion maps weighted using the HFL-SWARA method. Pixel values between 0.0 and 0.175 were considered in the suitability map, and a total of eight sharing locations were identified. According to the TOPSIS results, CS4 and CS3 were identified as the most suitable locations, whereas CS8 was the least suitable. At the final stage of the study, a service area analysis was conducted to evaluate the service coverage capability of sharing stations. The results showed that 81.1% of the population and 76.5% of the focus area were covered within the 3000 m service area. Finally, comparative and sensitivity analyses were conducted to assess the robustness of the results under varying conditions. The current study presents a new approach to site selection for micro electric car-sharing stations using a GIS-based HFL-SWARA and TOPSIS methodology.