Towards a Taxonomy of Tourist Route Traffic Models: A Systematic Literature Review

Smart tourism has transformed tourist route planning into an information technology-driven process supported by a rapidly expanding range of computational and data-driven models. However, existing research remains fragmented, employs inconsistent terminology, and lacks a systematic framework for organising and selecting tourist route traffic models from an information technology perspective. Accordingly, this review aims to (1) develop an inductive taxonomy of tourist route traffic models and their defining characteristics, (2) synthesise the operational measures and application contexts of different model families, and (3) identify emerging trends, research gaps, and future directions in smart tourism route planning. Following the PRISMA 2020 guidelines, a systematic review of 59 peer-reviewed studies published between 2011 and 2026 across 12 countries was conducted. The review identified 11 model families, 33 defining characteristics, and 147 family-tag assignments. Interest Field Models were the most frequently adopted approach. At the same time, artificial intelligence (AI), machine learning, geographic information systems (GIS), and network analysis have emerged as the fastest-growing methodological directions since 2021. Unlike previous reviews that primarily summarised algorithms or application domains, this review develops the first information technology-oriented taxonomy of tourist route traffic models, linking model families with their defining characteristics, operational measures, and application contexts within a unified framework. The proposed taxonomy provides a structured basis for technology-based model selection, highlights the limited integration of sustainability considerations across existing models, and supports future research and evidence-based smart destination management.