Logistical routing of park tours with waiting times: case of Beijing Zoo

Abstract

Site planning for parks consists of synthetic strategies to improve visitors’ experience and appreciation of park features. An important aspect in site planning is to coordinate visitor flows in order to avoid excessive congestion that may depreciate visiting experience. An emerging need in the coordination strategies is to personalize visiting routes and enhance the enjoyment of the tour for individual visitors. On the individual level, visitors have diverse preferences for park attractions. Scheduling a tour to visit attractions is restricted by not only the layout of park facilities but also the uncertainty of waiting induced by different lengths of lines at attractions. This paper proposes a tentative solution to optimize the logistics of individual tours by considering the dynamic nature of waiting time at park attractions derived from empirical data. The optimal solution is achieved using the branch-and-bound algorithm and is implemented in a real-world case of Beijing Zoo, a metropolitan zoology park in Beijing, China. The case study provides corroborating evidence for studying the logistical routing of park tours that: (1) visitors arriving at the park earlier can avoid crowds and excessive lines whereas visiting at midday would encounter excessive waiting and (2) the shortest tour route may not necessarily be the most efficient; strategically scheduling the visit to popular exhibits in their off-peak hours could effectively shorten overall tour time. This problem, called the Traveling Salesman Problem with Waiting Times (TSPWT) increases the realism of the routing problem while shedding new light on personalized routing strategies for improving individual touring experience.

Keywords:

• traveling salesman problem
• logistics
• waiting times
• time-dependent
• route planning
• optimization

Acknowledgement

This work was supported by National Natural Science Foundation of China [grant number 41371489].

Additional information

Notes on contributors

Haiying Xu

Haiying Xu is a PhD candidate in the College of Resources Science and Technology at Beijing Normal University, Beijing, China. Her research interests include city traffic demand management and emergency management.

Qiang Li

Qiang Li is a professor in the College of Resources Science and Technology at Beijing Normal University, Beijing, China. Her research interests include city traffic demand management, regional planning and resource management.

Xiang Chen

Xiang Chen is an assistant professor in the Department of Emergency Management at Arkansas Tech University, Russellville, AR, USA. His research interests are GIS applications for transportation, emergency management, and food science.

Jin Chen

Jin Chen is a professor in the Academy of Disaster Reduction and Emergency Management at Beijing Normal University, Beijing, China. His research interests include resources and environment remote sensing, emergency management.

Jingting Guo

Jingting Guo is a master in Academy of Disaster Reduction and Emergency Management at Beijing Normal University, Beijing, China. She majors in Cartography and Geographic Information Engineering.

Yu Wang

Yu Wang is a research assistant in Beijing Research Center of Urban System Engineering Beijing, China. Her research interests include public safety and emergency management.