# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
## 4 Method
### Time Planning Algorithm Based on Tour Itinerary Planning Satisfaction
Author [PERSON], affiliated with Aerial Photogrammetry and Remote Sensing Group Co., Ltd., contributed to the design of the study. The funding sponsors had no role in the design of the study. ## Appendix A

The following describes the basic concepts involved in the problem and introduces their significance in this study, specific forms, and quantitative methods. The concrete form is designed to adapt to the relevant function. The defined quantification method provides a reasonable reference, and it is used for the calculation of experimental data in this paper. It can be replaced by other methods in practical applications. **Definition 1**. : _Node. In this paper, all locations involved in the itinerary are collectively referred to as nodes. They possess the following attributes: role (origin, destination, or waypoint), type (scenic spot, restaurant, hotel), time windows, expected duration, travel time to other nodes, and the next node in a fixed itinerary sequence (default is none). For scenic spots, additional attributes include popularity, user interest level, ticket price, category, grade, physical exertion, availability of internal dining and accommodation, and whether they are candidate scenic spots. Additionally, they have attributes such as adjacent nodes in the itinerary sequence, node penalties, internal dining and accommodation arrangements, and time cycles, which are used in the algorithm's calculation process and output._

**Definition 2**. : _Meal and rest accommodation time parameters (MRTimePar). We guide the scheduling of meal and accommodation times in the itinerary planning with six parameters, including the expected lunch start time, dinner start time, rest and accommodation start time, lunch duration, dinner duration, and rest and accommodation duration. These parameters are collectively referred to as meal and rest time parameters._

**Definition 3**. : _Node time windows. The time window of a node represents a period during which it is accessible and available. In this paper, each time window is represented by a triplet consisting of the opening time (\(Open_{a_{i}}\)), the last entry time (\(Last_{a_{i}}\)), and the closing time (\(Close_{a_{i}}\)). A node may have multiple time windows, whereas for the starting point, ending point, and nodes open throughout the entire journey, their time windows are defined from the start time to the end time of the planned travel._

**Definition 4**. : _Node time period. The node time period is a time interval composed of the arrival time of the node \((ArrT_{a_{i}})\) and the arrival time of the next node \((NextNArrT_{a_{i}})\). It includes three stages: from the arrival time of the node to the start time \((StartT_{a_{i}})\) of node visitation, from the start time of node visitation to the end time \((EntT_{a_{i}})\) of node visitation, and from the end time of node visitation to the arrival time of the next node._

**Definition 5**. : _Scenic spot physical exertion. We evaluated and quantified the physical exertion (PE) required to visit a scenic spot. As shown in (A1), the \(PE_{a_{i}}\) of a scenic spot \(a_{i}\) is defined as the product of the recommended visit duration \((RVDur_{a_{i}})\) of the scenic spot and the physical exertion coefficient \(\beta_{a_{i}}\). The physical exertion coefficient \(\beta_{a_{i}}\) depends on the terrain of the scenic spot. In this paper, climbing scenic spots \(\beta=2\), and other scenic spots \(\beta=1\). Under the default daily physical exertion limit, it is possible to avoid planning itineraries that involve continuous climbing in a single day in the algorithm._

\[PE_{a_{i}}=RVDur_{a_{i}}*\beta_{a_{i}}\] (A1)

**Definition 6**. : _Scenic spot interest level \((Int_{a_{i}})\). We define two levels of scenic spot categories, where the secondary category serves as a further differentiation of the primary category.