<![CDATA[The increasing adoption of electric vehicles (EVs) worldwide is driven by environmental concerns and the depletion of fossil fuels. Yet, the limited battery range remains a significant barrier to their widespread use, particularly for long-distance travel. This study addresses this challenge by developing a methodology to determine the number of recharging stops, their durations, and strategic locations for electric vehicle charging stations (EVCSs) using k-means clustering analysis, with proposed station locations visualized within a GIS framework. The method integrates quantitative data from questionnaires administered to transportation experts and professionals (N=24) and applies it to case studies across three major highway routes in Ceará, Brazil. The k-means clustering proposed a solution of three stops with a 28-minute recharge time; however, subsequent analysis revealed this time was an overestimate compared to a more precise, distance-based linear model. A key implication is that while clustering can group user preferences, its direct output for operational parameters like charging time can be imprecise. Therefore, we demonstrate the necessity of calibrating such models with physically-grounded equations to achieve practical and efficient trip planning. This approach highlights the importance of integrating user preference data with distance-based models to create more realistic EV infrastructure plans for long-distance corridors.]]>
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