# Data-Driven Modular Vehicle Scheduling in Scenic Areas
## 3 Problem Statement and Solution Method  
This study addresses the scheduling problem for a tourist shuttle fleet composed of modular vehicles operating between tourist attractions. Modular vehicles, such as those developed by NEXT Future Transportation ([[https://www.next-future-mobility.com/maas](https://www.next-future-mobility.com/maas)]([https://www.next-future-mobility.com/maas](https://www.next-future-mobility.com/maas)), accessed on 10 October 2024), are innovative transportation solutions designed to enhance flexibility, efficiency, and sustainability in passenger transportation. These vehicles are characterized by their modular design, which allows individual units or pods to connect and disconnect seamlessly while in motion. This modularity enables the vehicles to adapt to varying tourist demands and optimize routes in real-time. The shuttle vehicle is assumed to travel on a fixed route between two designated attractions. The scheduling optimization problem primarily focuses on optimizing vehicle allocation before departure. The core of data-driven methods lies in leveraging existing or historical data to inform the creation of future strategies. To assess the effectiveness of this approach in tourism shuttle scenarios, this study compares data-driven methods with the traditional instant response model and the theoretical global optimal model. We will refer to the instant response model, data-driven model, and theoretical global optimal model as Model 1, Model 2, and Model 3, abbreviated as M1, M2, and M3, respectively. The dispatch center is assumed to make a total of \(T\) decision cycles. Using a commonly applied 4:1 dataset segmentation ratio [49], the data are divided into training and testing sets accordingly. Here, \(T_{1}\) represents the training dataset, where each time period \(t\in\{1,2,...,\ T_{1}\}\), and \(T_{2}\) denotes the test set, with each period \(t^{\prime}\in\{1,2,...,\ T_{2}\}\). Since the training set is used for strategy development and the test set for evaluating strategy performance, decisions are made during the \(T_{2}\) period. Next, we use the test set to demonstrate our algorithm. During a specific decision period \(t^{\prime}\in\{1,2,...,\ T_{2}\}\), multiple requests may be generated, represented by \(n_{t^{\prime}}\). The optimization objective in this study is to minimize total operating costs, including transport and waiting costs. For transport costs, only the leading vehicle in a fleet composed of multiple modular units requires a driver, so transport costs account for the energy consumption and wear of all vehicles in the fleet, as well as the driver cost for the leading vehicle. The total transport cost for the leading vehicle is defined as \(k_{1}\) and for each following vehicle as \(k_{0}\). Since trailing vehicles do not require drivers and encounter less air resistance, we assume \(k_{0}<k_{1}\). In terms of waiting costs, when passenger demand is received at time \(t^{\prime}\), the vehicle dispatch center must decide whether to send vehicles for transport. Let \(x_{t^{\prime}}\) be a binary variable, where \(x_{t^{\prime}}=1\) indicates dispatching vehicles at time \(t^{\prime}\), and \(x_{t^{\prime}}=0\) indicates not dispatching. If a vehicle is dispatched, the waiting cost at this time is zero. However, if no vehicle is dispatched, each passenger incurs a per-unit waiting penalty, denoted by \(p_{t^{\prime}}\). If the passengers' needs remain unmet at the next time step \((t^{\prime}+1)\), the waiting penalty is incurred again. Thus, waiting costs are the cumulative waiting penalties for all passengers over these periods. To simplify the model, we assume that if vehicles are dispatched at time \(t^{\prime}\), all unmet passenger demand is fulfilled, disregarding cases where only a portion of passengers is transported, leaving others to wait.