# Modeling the Trip Distributions of Tourists Based on Trip Chain and Entropy-Maximizing Theory  
[PERSON]  
1 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; [EMAIL_ADDRESS] (Z.-W.H. ); [EMAIL_ADDRESS] (T.J)2 College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China 2  
[PERSON]  
3 College of Civil Science and Engineering, Yangzhou University, Yangzhou 225127, China 3  
[PERSON]  
1 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; [EMAIL_ADDRESS] (Z.-W.H. ); [EMAIL_ADDRESS] (T.J)2 College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China 2  
[PERSON] [6] examined the spatial-temporal dynamics of tourist flow in tourist destinations and built the spatial concentration index model and the tourism center of gravity model. [PERSON] and [PERSON] [7] analyzed the travel laws of tourists using mobile communication big data and established a statistical model based on the big data of tourist flow. On the other hand, the most commonly used trip distribution forecasting methods are aggregate and disaggregate models [8]. Aggregate models mainly include the growth-factor methods (e.g., the Fratar method), gravity models [9; 10; 11], and intervening opportunities models [12]. In particular, the gravity models have later been explained using the maximum entropy theory and maximum likelihood principle. The intervening opportunities model was modified by [PERSON] [13] to make it more widely used. Disaggregate models include random utility models such as Logit and Nested Logit [8]. In the last few decades, activity-based models which view travel as a demand derived from travelers' needs to participate in activities have attracted much attention [14; 15]. In this context, based on the travel laws of tourists, [PERSON] et al. [16] proposed a trip distribution model using trip chain [17; 18], a sequence of linked trip segments between a pair of anchor activities, as the unit of trip distribution analysis. This makes it different from traditional trip distribution models [19] in which the trips are independent from each other. Besides, [PERSON] et al. [20] reported a new method based on the Entropy-Maximizing (EM) theory to model the Origin-Destination (OD) distribution of taxi trips. Prior probability was used in it to improve the prediction performance of trip distribution. Furthermore, new data sources and techniques such as social media data and Machine Learning (ML), respectively, have been increasingly used to improve the predictive power and accuracy of trip distribution models [21; 22]. In summary, these methods have significantly improved the understanding of tourists' trip activities and transport demand. However, existing studies mainly focus on the model of trip distribution of traditional transportation modes; very little attention has been paid so far to solve the problem of forecasting the tourist flow distribution of a suburban tourist railway. As a result, planners and policy-makers from the local government of a tourist city still lack proper methods and solid evidence to plan and design suburban tourist railways. To fill this gap, this paper proposes a method based on the Entropy-Maximizing theory and trip chain to model the tourist flow distribution of suburban tourist railway. By analyzing the trip distribution characteristics of tourists (Section 2), a trip chain-based model was built to describe the trip distribution of tourists (Section 3). Then, a tourist flow distribution forecasting model for the suburban tourist railway was built based on the trip chain model and Entropy-Maximizing theory (Section 4). A case study was conducted using trip distribution data collected from the tourists who take suburban tourist railways around the H city to demonstrate the use of the proposed method (Section 5).