Two-Stage Tour Route Recommendation Approach by Integrating Crowd Dynamics Derived from Mobile Tracking Data

Tourism activities essentially represent the interaction between crowds and attractions. Thus, crowd dynamics are critical to the quality of the tourism experience in personalized tour recommendations. In order to generate dynamic, personalized tour routes, this paper develops a tourist trip design problem with crowd dynamics (TTDP-CD), which is quantified with the crowd dynamics indicators derived from mobile tracking data in terms of crowd flow, crowd interaction, and crowd structure. TTDP-CD attempts to minimize the perceived crowding and maximize the assessed value of destinations while minimizing the total distance and proposes a two-stage route strategy of global optimization first, local update later to deal with the sudden increase in crowding in realistic scenarios. An evolutionary algorithm is extended with container-index coding, mixed mutation operators, and a global archive to create a personalized day tour route at the urban scale. To corroborate the performance of this approach, a case study was carried out in Dalian, China. The results demonstrate that the suggested method outperforms previous approaches, such as NSGA-II, MOPSO, MOACO, and WSM, in terms of performance and solution quality and decreases real-time crowding by an average of 7%.

Automated summary

This paper proposes a tourist trip design problem with crowd dynamics, aiming to generate dynamic and personalized tour routes by quantifying crowd dynamics indicators and using a two-stage strategy of global optimization and local update. A case study in Dalian, China demonstrates that this method outperforms previous approaches and reduces real-time crowding by an average of 7%.