# A Framework of Vehicle Usage Optimization for Tour Purposes
## 1 Introduction  
Recently, we have observed that tourism craziness is rising daily in Bangladesh, where tourists have been interested in visiting beautiful spots. However, they must learn the proper process and details for visiting these places. Thus, our main aim is to reduce travel and agency costs. When they first decide to travel elsewhere, travelers want to find good travel agencies and travel at a low cost. Similarly, travel agencies seek to maximize their profits. Our main focus is on how travel agencies have achieved their profit goals. Numerous recent research papers [1; 2] have utilized machine learning. During the tourist season, a multitude of visitors frequent tourist destinations. The academic literature on tourism has led to a more relaxed touring experience for visitors. In a previous research paper, we explored the utilization of various systems, such as group trip planning, package and blog systems, mobile tourist guides, and more, by many tourists. The authors and developers of earlier research articles and systems [1; 3] did not work for travel companies; they exclusively served the interests of travelers. However, tour agencies constitute a significant component of the tourism system, and, notably, previous literature has failed to fully consider these aspects. We have engineered a superior system compared to previous implementations. Consider the scenario where destinations A and B share identical routes: 52 travelers travel to location A, whereas only 20 travel to location B. However, with each bus accommodating a maximum of 36 seats, it becomes evident that two buses are needed for location A, while a single bus suffices for location B. In this context, our attention turns to unoccupied bus seats. The elevated travel expenses incurred thus far underscore the necessity of deployingan optimized system. Our primary goal has been optimizing travel expenses for travel agencies and travelers, maximizing overall efficiency. Our system incorporates novel features. Among these is the distinct ability of travel agencies to optimize their vehicle utilization, setting our system apart. Simultaneously, this optimization translates to reduced costs for travelers. This proposed method is named Static Group Trip Planning (SGTP). Both travelers and travel agents will benefit from our outcomes. Because of this system, travelers can travel easily at a low cost, while travel agencies can earn more profit. To optimize vehicle usage for tour purposes, we utilized a database table. This table contained information such as the number of passengers, the distance of the trip, the route, and the type of vehicle needed. Using this information, we determined the optimal vehicle for each tour and minimized the number of empty seats. We have segmented our findings into blog, booking, and package segments. In the blog segment, we discuss the tourist spot. In the booking segment, we provide customers with information on how they can book tours. Finally, in the packaging segment, we offer tour packages. Here, we have provided these different types of packages, such as two days, three nights packages, one day, two nights packages, etc. According to our research, improving vehicle use for tours can result in financial savings and higher client satisfaction. By utilizing a database table and segmenting our findings into blog, booking, and package segments, we have provided valuable insights and tools for tour agencies and customers alike. The main contributions of this paper are as follows:  
* We have developed a system that allows travel agencies to minimize vehicle usage. * We have optimized the costs of providing low-cost, high-value services to travel agency customers. * We have developed an optimization process, which can reduce the environmental carbon footprint. * We have analyzed the graphical representation to understand quality attributes. This paper has been organized as follows. Section 2 and Section 3 focus on related research. We note the proposed solution in Section 4.