# A Framework of Vehicle Usage Optimization for Tour Purposes
## 2 Related Research  
In Ref. [1], the authors have provided the concept of a \"dynamic group.\" Their main approach is to provide a dynamic group trip. As a result, they included two approaches: the Naive approach and the Fast approach. Finally, they selected the FAST approach for their experiments because it was shown to be more efficient. Extensive experiments and synthetic datasets showed that their approach is, on average, 99.74% faster and requires 94% less I/Os than the naive approach. The author presents a web-based tour and trip management system in Ref. [3]. Here, they raised the issue that it is more difficult for travelers to look for additional packages, contact the helpdesk for information, and communicate with the helpdesk to make travel arrangements. Therefore, the author has developed a website where visitors can browse all products according to location. A traveler can also see a description of the location. Additionally, they offer a Google Map with the location's distance, lodging, and travel choices. The author of this paper has suggested group travel preparation (GTP) [2]. A group trip planning (GTP) search returns points of interest (POIs) for various establishments, including malls, eateries, and theaters. Occasionally, group members who live a long distance away from one another must determine the collection of POIs that minimizes the group's total journey distance. The author creates approximate methods for GTP inquiries for road networks and Euclidean space in this article. They presented unique strategies to expand the POI search space for the GTP question based on the geometric features of ellipses, greatly lowering the overall mileage of trips. In Ref. [4], the authors addressed the problem of routing a fleet of vehicles from a central depot to customers with known demand. Routes originate and terminate at the central depot and obey vehicle capacity restrictions. They assumed that all vehicles were identical. In their work, they relaxed the homogeneous fleet assumption. The objective was to determine the optimal fleet size and mix by minimizing a total cost function that includes fixed and variable cost components. They also described several efficient heuristic solution procedures and techniques for generating a lower bound and underestimating the optimal solution. Finally, they have presented some encouraging computational results and suggestions for further study. The authors of [5] found that a significant issue for the shared use of autonomous vehicles is how to project serving routes efficiently. From another perspective, this issue could be understood as segmenting the maximum number of passengers into groups. Therefore, in their paper, the authors intended to investigate passengers' similarities instead of directly matching autonomous vehicles and passengers. They aim to determine the minimum number of groups and assign each group autonomous vehicles. Thus, they proposed a cluster-based algorithm to classify passengers. The researchers conducted numerical experiments with both small and large demands to demonstrate the validity of the proposed algorithm. Furthermore, they showed that the cluster-based algorithm could minimize the number of vehicles and total travel distance. Finally, they showed that vehicle capacity had little impact when the number of seats exceeds four, and time windows could continuously influence gathering passengers using a sensitivity analysis of critical parameters. In Ref. [6], the authors discovered that mobile tourist guides assist travelers in choosing places to visit. Thus, the attraction of mobile tourist guides was considered. In this paper, collaborative travel recommendation systems were extended by utilizing filtering techniques to save information such as the current user's location, time, weather conditions, and places the user has already visited. They also worked on context-aware ratings from the wireless sensor network.