# Data-Driven Modular Vehicle Scheduling in Scenic Areas
## 2 Literature Review
In addition to passenger transport, modular vehicles can also be used for freight or multimodal transport [21; 22], which not only reduces costs but also highlights the benefits of modular vehicles in enhancing transportation efficiency and reducing environmental pollution [23]. For example, [PERSON] et al. [24] leveraged modular vehicles to design urban shuttle public transportation services with the assumption that a fleet of modular vehicles can add or detach units at designated stations and merge into other fleets, enabling passenger transfers across routes. [PERSON] et al. [25] arranged passengers and freight into separate compartments in different modular vehicles, which were linked to form a fleet. The fleet can be reconfigured at designated stations to carry both passengers and freight at the same time, improving transport efficiency. Furthermore, modular vehicle technology offers similar benefits in America and Europe. [PERSON] et al. [26] use modular vehicles to boost the efficiency of last-mile material transport and address the issue of low public transport occupancy rates in the United States. [PERSON] et al. and [PERSON] et al. [27; 28] have demonstrated that the European modular vehicle system provides greater payload capacity while controlling pollutant emissions. Overall, modular vehicle technology is highly applicable worldwide. However, a common feature in these studies is the assumption of fixed transport demand, such as a set volume of commuters [14; 15; 24; 29] or a specific amount of freight [25; 26]. This fixed demand assumption is not very realistic in some cases, such as public transport, where we cannot predict how many passengers will be at the next station or at the next time step making dynamic demand research inevitable. In response to unknown or constantly changing requirements, data-driven methods provide an effective solution. Data-driven refers to the use of data to guide behavior, which has achieved good application results in artificial intelligence [30], intelligent transport [31; 32], public management [33], and other areas. For dynamic programming problems, [PERSON] et al. [34] used data-driven methods to extract vehicle stopping point information from historical data, enabling efficient processing of both static and dynamic fleet requirements. [PERSON] et al. [35] integrated the scheduling and dynamic capacity allocation of modular buses into a data-driven model to improve the dynamic matching of capacity to demand. [PERSON] et al. [36] proposed an Adaptive Hierarchical Incremental Prediction (AHIF) method to address the challenges of demand forecasting in online-to-offline (O2O) services in a data-driven manner. Table 1 summarizes more forecasting algorithms and optimization techniques for dynamic demand conditions. Table 1 shows that most of the algorithms used in the research are based on data-driven approaches, such as machine learning methods, which are also a type of data-driven approach. However, these studies either did not consider the characteristics of uncertain demand and ignored demand forecasting, or did not account for dynamic capacity adjustment strategies, which may limit the improvement of transport efficiency. Therefore, this paper takes the scenic shuttle buses with uncertain passenger numbers as the research  
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object, applies the data-driven method to predict the number of passengers in different periods, and integrates the prediction results into a modular vehicle scheduling model to make full use of the advantage of the variable capacity of modular vehicles, to minimize the operating cost of scenic transport and maximize the vehicle utilization rate under the conditions of limited vehicle capacity and fluctuating passenger demand. The main contributions of this article are summarized as follows.