Tourism is a key driver of regional development, particularly in emerging destinations around the globe. Despite its critical role, aligning tourism services with tourists’ needs requires effective planning and management that reflect key stakeholder perceptions and resource allocation to enhance the overall competitiveness of a destination. This study aims to integrate the perspectives of tourists and tourism service providers using a logistics performance framework to support data-driven tourism management. Using the Best–Worst Method (BWM), stakeholder-based evaluations are conducted to prioritize key decision criteria, including economic efficiency, reliability, responsiveness, and safety, which are essential for supporting operational efficiency and tourism planning. Next, a Multi-Objective Traveling Salesman Problem (MOTSP) model is applied to evaluate travel routing and ensure efficient resource allocation. Data collection and analysis are conducted for popular tourist attractions, using a case study of Ubon Ratchathani, Thailand. The findings provide practical insights for regional planners and tourism managers seeking to enhance infrastructure planning through stakeholder-informed and logistics-driven decision-making for regional tourism development.

Smart logistics parks (SLPs) drive modern economies and global supply chains, making maturity assessment essential for guiding investment, improvement and strategic decision-making. Existing maturity assessment models for traditional logistics parks (TLPs) overlook the distinctive advancements of SLPs, underscoring the need for specialized frameworks. In this study, an SLP maturity evaluation model was developed that differentiates SLPs from TLPs assessments to support smart logistics transformation. The framework comprises five core dimensions with 20 sub-factors: Smart Economy, Public Services and Smart Governance, Smart Infrastructure and Intelligent Technology Application, Skilled Human Capital, and Environmental Sustainability. This structure was validated through a literature review and expert input. For comparison, the TLPs model is based on China’s national standard. Using the Analytic Hierarchy Process (AHP) to determine factor importance, results indicate that SLPs prioritize Smart Infrastructure and Intelligent Technology Application (0.3229), followed by Public Services and Smart Governance (0.2447). In contrast, TLPs place the highest emphasis on Service Capability and Operation Management (0.3317). While both models value infrastructure, operational services, and environmental considerations, SLPs place stronger emphasis on technological innovation and digital governance, whereas TLPs focus on operational efficiency and service quality. These findings confirm that although infrastructure and operations remain central to both, transitioning to SLPs demands a greater focus on intelligent technology. This study provides empirical evidence that the SLPs' transformation necessitates the integration of intelligent systems while simultaneously maintaining efficiency, service quality, and sustainability. This research offers practical guidance for investors, policymakers, and operators.