Research On Tourists’ Spatiotemporal Behaviors And Tourism Travel Simulation Model

Understanding the pattern and formational mechanism of tourists’ spatiotemporal behaviors is a crucial foundation for tourism transportation planning and management.However,previous researches on tourists’ spatiotemporal behaviors mostly present phenomena analyses and lack in theory,thereby hardly providing systematic guidance for practical work.By observing and analyzing tourists’ spatiotemporal data at different spatial scales,this paper employs mathematical models of utility maximization problems to describe tourists’ travel planning process.Based on the parameters in the mathematical model and its numerical algorithm,an Agent-Based Travel Simulation Model(ABTSM)is developed.ABTSM manages to provide a reasonable explanation for the formational mechanism of tourists’ spatiotemporal behaviors,and successfully simulates various scenarios for tourism transportation planning and management.To make full use of different types of spatiotemporal behavior data at their own applicable spatial scales,this paper analyzes the behaviors and develops corresponding models at three spatial scales-intracity,intercity and multi-destination.At the intracity scale,this paper analyzes the travel survey data collected from a tourist city(Chengde,China)and describes tourists’ multi-day travel planning process as a Team Orienteering Problem(TOP).Based on a simple insertion algorithm to solve the TOP,tourists’ movement rules for simulation are established,then the ABTSM can be developed.As an application,this paper tries to use ABTSM to simulate scenarios in a tourist city after the operation of Intelligent Transportation System(ITS).To this end,further analysis on tourists ’ responses to transportation services is required to formulate reasonable simulation rules.With the help of a Mixed Ranked Logit model that is incorporated with random coefficients,the impacts of ITS-supported travel information services and public transportation service on tourists’ transportation mode choices are successfully quantified after analyzing tourists’ stated preference survey data on transportation modes.Simulation result verifies the ability of ITS to improve tourists’ mobility and to benefit the tourist city as well.At the intercity scale,Weibo check-in data are observed to correlate strongly with tourism activities.With the advantages in spatiotemporal coverage and stability of aggregated data,Weibo check-in data are employed to establish a non-linear regression model to depict intercity tourist flow.This model successfully uncovers the impacts of Nanjing-Hangzhou intercity High-speed rail on temporal-redistribution of tourism arrivals.At the multi-destination scale,this paper proposes two aggregated indices of spatiotemporal behavior pattern when using Weibo check-in data,namely,the matrix of inter-destination movement probability and tourism circles that are detected by community detection algorithms.As for the ABTSM at multi-destination scale,the TOP is revised as an Orienteering Problem with an additional restriction as the ABTSM is developed with the travel demand overflow theory.By calculating the similarity between the matrixes generated from ABTSM and Weibo data,it is found that the simulation model can effectively rebuilt tourists’ multi-destination travel and explain the formational mechanism of spatial relationship among tourism destinations.As an applicable method to predict tourism travel,this model is applied to predict the impacts of Hanzhou-Huangshan High-speed rail on tourism travels that are generated from Hangzhou’s tourists.