## 1 Introduction
### Related Works  
In recent years, in relation to the two basic problems of OP and AOP, researchers have conducted extensive and in-depth research on scenic route planning alone and by combining user preferences. Scenic route planning aims to plan routes with a large scenic score for users without considering their preferences. [PERSON] et al. [1] believed that the more photos and check-in times that are distributed on the road section, the higher its scenic score. Hence, they used Open Street Map (OSM) to extract basic road network data, check-in data on Foursquare, and Flickr photo data, to calculate the scenic value of the road section and propose a scenic route planning system based on multisource heterogeneous crowdsourcing data to recommend the best travel route between two points for users. [PERSON] et al. [9] proposed a two-stage scenic route planning framework. This framework first calculates the scenic score of the road section and then uses a memetic algorithm (MA) to search and plan travel routes with high scenic scores for users. [PERSON] et al. [10] proposed an enhanced GPS navigation system, GPSView, which takes landscape factors into account in the route planning process to plan a driving route with landscape and sightseeing properties for users so that travellers can sightsee while driving. [PERSON] et al. [11] extracted the spatial relationship between POIs (points of interest) from tourism blogs, quantized the relationship with a probability model, and established a POI relationship diagram. Then, Bayesian inference was used to calculate the probability measure of spatial intimacy. Finally, the method was applied to the road network to plan a more attractive route for users to guide them through their favourite areas. [PERSON] et al. [12] proposed a new genetic algorithm based on a path network. Each chromosome represents a feasible path which avoids the search cycle. The mutation at a specific location can quickly plan the path. [PERSON] et al. [13] maintain that with real-world spatial networks the edge's travel times are time dependent, where the arrival time to an edge determines the actual travel time on the edge, based on which they proposed a time-dependent A* algorithm to accelerate the calculation speed of the online path. [PERSON] et al. [14; 15; 16] assume that the scenic score and travel time cost of each arc in the road network are time dependent; that is, the scenic score of different POIs in various time periods changes, and thus the travel time cost associated with assorted departure times also differs. They defined this problem as a twofold time-dependent AOP and proposed an MA to solve this problem. Similarly [9], [PERSON] et al. [17] also believe that the utility value and travel time cost on the arc in the road network are time dependent and constructed a twofold time-dependent path planner to solve the problem. The difference is that they modelled the utility value of the path so that it can be quantified according to specific needs. For example, for security patrol personnel, the utility value could be the danger degree of the path; for those taking exercise, the value could be the path's quietness; and for tourists, it could be its scenic value. This unified modelling method expands the application scope of the method. [PERSON] et al. [18] proposed a fast local search metaheuristic method based on the ant colony algorithm, which combines the principle of this system with the time-dependent local search method to quickly provide an effective solution. Experiments show that the algorithm can obtain high-quality route planning results with low computational time, ensure that the route can be updated swiftly when new available traffic information appears, and help tourists reach their destination quickly. In subsequent work, [PERSON] et al. [19] proposed an efficient MA to model more specific details in the tourism route planning problem to improve the accuracy of recommendations for specific problems.