A Knowledge Graph-Enhanced Hidden Markov Model for Personalized Travel Routing: Integrating Spatial and Semantic Data in Urban Environments

Abstract

Highlights: What are the main findings? We introduced a novel knowledge graph-based Hidden Markov Model (KHMM) that significantly enhances personalized route recommendations by effectively integrating spatial and semantic data from POIs. By leveraging knowledge graphs, the KHMM expands the traditional Hidden Markov Model’s state space to capture multi-dimensional and higher-order relationships between POIs. What are the implications of the main findings? Improving adaptability to fluctuating user preferences and real-time travel conditions is crucial for developing intelligent transportation systems that can respond to the evolving needs of urban travelers, thereby fostering smarter cities. Providing insights into factors influencing travel behavior deepens the understanding of how spatial and semantic relationships shape route selection, having far-reaching implications for transportation planners and urban policymakers implementing tailored solutions to enhance urban mobility and efficiency. Personalized urban services are becoming increasingly significant in smart city systems. This shift from intelligent transportation to smart cities broadens the scope of personalized services, encompassing not just travel but a wide range of urban activities and needs. This study proposes a knowledge graph-based Hidden Markov Model (KHMM) to improve personalized route recommendations by incorporating both spatial and semantic relationships between Points of Interest (POIs) in a unified decision-making framework. The KHMM expands the state space of the traditional Hidden Markov Model using a knowledge graph, enabling the integration of multi-dimensional POI information and higher-order relationships. This approach reflects the spatial complexity of urban environments while addressing user-specific preferences. The model’s empirical evaluation, focused on Changsha, China, examined how temporal variations in public attention to POIs influence route selection. The results show that incorporating dynamic temporal and spatial data significantly enhances the model’s adaptability to changing user behaviors, supporting real-time, personalized route recommendations. By bridging individual preferences and road network structures, this research provides key insights into the factors shaping travel behavior and contributes to the development of adaptive and responsive urban transportation systems. These findings highlight the potential of the KHMM to advance intelligent travel services, offering improved spatial accuracy and personalized route planning.