## 3 Methodology  
### Research Framework and Definitions for TTDP-CD  
This paper proposes a novel research framework for solving TTDP-CD, shown in Figure 1, for generating personalized, dynamic one-day tour routes with a high experience value for the tourists. We divided the research process into four sections. (1) Data processing. A massive amount of mobile tracking data with rich location and time information is used as the data source to extract past tourist trips, which are represented by tourism activity chains. (2) Indices derivation. Tourism activity chains are used to derive indicators of three types of crowd dynamics, including crowd flow, crowd interaction, and crowd structure. (3) Model construction. With the derived crowd dynamics indicators, this study defines three objectives, including minimizing crowding, maximizing value, and minimizing distance, with multiple constraints and employing a two-stage route strategy for generating initial pre-tour routes and dynamically adjusted in-tour itineraries to avoid sudden overcrowding. (4) Algorithm operation. The constructed multi-objective model is solved with a heuristic algorithm, INSGA-II, both in the pre-tour initial routing stage and in-tour dynamic adjustment stage, thus generating the initial pre-tour routes and the adjusted in-tour routes. The proposed TTDP-CD is novel in terms of model and algorithm. Unlike the traditional TTDP model, the TTDP-CD applies the crowd dynamics patterns obtained from past tourist trips to tourist itinerary planning for the first time in the literature, as reflected in the avoidance of peak crowds, attraction scores with reference to realistic choices, and adaptive in-tour dynamic adjustments under unexpected circumstances. These improvements can not only lead to better tourist experiences and more flexible itineraries in realistic and unexpected situations, but also provide a novel perspective on the application of crowd dynamics in tourism research. As an optimization problem in mathematics, TTDP is commonly solved by employing meta-heuristic algorithms. Among them, the evolutionary algorithms augmented with specific search components, such as NSGA-II, are commonly used to solve multi-objective problems. Our INSGA-II is further improved based on NSGA-II from the aspects of chromosome coding, mutation operation, and evolving strategy to overcome the difficulties in route planning in supervities, thus generating better Pareto solutions of the recommended routes. **Definition 1** (Attraction Network). : _The attraction network \(G=(A,E,W)\) is constructed with all attractions in the city, where \(A\) is a set of attraction vertices. The edge \(E\) represents the directed transfer between vertices, whose weight consists of two attributes of distance travelled and time spent on the transfer, and \(W=\left\{\left(d_{ij},t_{ij}\right)|i,j\in A,i\
eq j\right\}\). \(A_{i}\) denotes a tourism attraction, and \(i=1\), \(2\),..., \(N_{A}\), where \(N_{A}\) denotes the total number of attractions. For a more realistic route, the elements  
Figure 1: Information derivation and route recommendation strategies for TTDP-CD. The crowd flow, reflected by the time-varying crowd flow indicator \(F_{i}\), is used to assist tourists in avoiding peak hours. The crowd interaction between attractions reveals the status of each attraction in the interaction network, which is reflected by the degree of nodes \(D_{i,in}\) and \(D_{i,out}\) and is used to determine the value of destinations. As the dynamic adjustment is activated, the attraction replacement scheme is determined by the similarity of the crowd structure \(S_{CSI}\). in weight \(W\) are obtained via the Baidu Map API ([[https://llbsynu.baidu.com](https://llbsynu.baidu.com)]([https://llbsynu.baidu.com](https://llbsynu.baidu.com))) which can provide the optimal path between the determined departure and destination locations for various transportation modes.