# Data-Driven Modular Vehicle Scheduling in Scenic Areas
## 2 Literature Review  
Tourist shuttle services play a crucial role in enhancing connectivity between tourist attractions and elevating the overall quality of the tourism experience [1]. In recent years, with the rapid expansion of tourism, the issue of vehicle scheduling at tourist attractions has increasingly drawn the attention of scholars. Typically, the operation of vehicles within scenic areas resembles that of traditional bus services. Currently, most optimization models for shuttle bus scheduling in these areas focus primarily on refining attributes such as passenger demand allocation, travel time, and vehicle capacity. For example, a self-driving tourist mode segmentation method based on the logit function was proposed to optimize the transfer plan between private vehicles and tourist buses [6]. [PERSON] et al. [5] proposed a flexible vehicle scheduling model with priority constraints, incorporating the simultaneous optimization of passenger capacity, transport completion time, and overall transportation cost. [PERSON] et al. [9] utilized a task partitioning graph to model the transportation problem involving non-fixed matching between tourists and vehicles, allowing each vehicle to be reassigned to another transport task after completing a trip to a scenic area. These studies have significantly advanced research on shuttle bus scheduling in tourist attractions. However, they have a common limitation: these models rely on traditional tourist buses as shuttles, with each bus operating independently and requiring a dedicated driver, resulting in high operating costs. However, unlike traditional tourist buses, tourist shuttle trains composed of modular vehicles offer high flexibility, allowing the fleet length to be adjusted promptly based on passenger demand. In addition, each fleet requires only one driver [10], which effectively can reduce staffing and resource costs significantly in scenic areas. Currently, research on the application of modular vehicles in scenic shuttle transportation is limited. Therefore, this article proposes an optimization model to minimize total costs by varying the number of modular vehicles and examines its performance under different travel demand scenarios. Modular vehicles have received increasing attention from scholars in recent years. They can adjust fleet length by adding or removing modules in response to changes in demand during operation. Due to their high flexibility, modular vehicles were primarily utilized for last-mile transportation [11; 12]. [PERSON] and [PERSON] [13] reviewed current research on modular vehicles and suggested that modular systems have been shown to have the potential to reduce congestion by reducing the size and number of vehicles during certain periods of urban traffic. Many recent studies have examined the use of modular vehicles in urban public transport or multimodal networks, emphasizing their benefits in dynamic route planning. The main advantage is that modular vehicles help reduce operating costs. For example, [PERSON] et al. [14] compared a fleet of modular vehicles with traditional fixed-route buses and found that the strategy of using modular vehicles could save 72% of additional costs. Similarly, [PERSON] et al. [15] compared modular autonomous vehicles with traditional bus models and verified that this new flexible scheduling strategy helps to reduce operating costs and improve service quality. Some scholars have proposed modular autonomous vehicles to address bus clustering caused by varying distances between buses. The results indicate that this approach effectively reduces costs and helps alleviate bus clustering [16; 17]. Some other researchers have combined modular vehicles with discrete and continue modeling to solve the problem of saturated traffic, achieving an optimal balance between scheduling costs and user waiting costs [18; 19; 20].