# Data-Driven Modular Vehicle Scheduling in Scenic Areas
## 5 Conclusions  
This study introduces modular vehicles into the tourism shuttle context. It establishes a data-driven model to enable real-time scheduling of shuttle vehicles to minimize operating costs under uncertain passenger demand. We compared the data-driven model with the traditional instant response model to evaluate total cost and vehicle utilization optimization, conducting sensitivity analysis by varying vehicle load and penalty. Additionally, the data-driven model with the theoretical global optimal model was compared to validate its effectiveness. The key findings of this study include the following aspects. (i) Data-driven models achieve cost savings and improved vehicle utilization compared to traditional instant response models, with their advantages becoming more pronounced as vehicle load increases or penalties vary. (ii) While vehicle load has a limited impact on the optimization performance of the data-driven model compared to the theoretical optimal model, penalty variation brings the data-driven model's performance closer to that of the theoretical optimal model. (iii) The average optimization level of data-driven models across various scenarios reaches 70.2% of the theoretical optimal, demonstrating their capability to handle complex, real-world situations and confirming the effectiveness of data-driven models. This study offers valuable recommendations for reducing operating costs at tourist attractions based on these results. First, adopting a modular fleet for shuttle transportation and replacing the instant response model with the data-driven model can lower driver costs and enhance vehicle utilization, leading to overall cost reductions. Secondly, utilizing larger-capacity vehicles whenever feasible can further leverage the advantages of data-driven models. Lastly, 'penalties' can be interpreted as the inconvenience tourists experience due to waiting, which, in reality, has a certain degree of subjectivity. However, the conclusion that changes in penalties can enhance optimization effects suggests that scenic area managers can take relevant measures at different times to change tourists' experiences and increase the probability of achieving better optimization results. The contribution of this research is to enable real-time scheduling of shuttle bus transport to tourist attractions using modular vehicles to effectively manage unpredictable future demand. By applying data-driven algorithms to the modular vehicle scheduling model, this paper addresses the shortcomings of current research that does not simultaneously consider future demand forecasts and dynamic adjustment of vehicle capacity, and the method effectively reduces operating costs and improves vehicle utilization, providing a referable solution for vehicle scheduling in other dynamic demand scenarios. It is worth noting that the parameters settings in this paper (e.g., number of passengers, waiting penalties, vehicle costs, etc.) are mainly based on the general cases, and researchers can change these parameter settings to achieve simulations in different cases, such as seasonality or extreme events. While the proposed model has broad applicability across various real-life scenarios, it has some limitations. Firstly, although the model provides feasible solutions, it may require longer computation times for large-scale problems. Secondly, extending the model to different types of vehicles would be a worthwhile research direction. Thirdly, this paper only considers fixed start and end points of shuttle routes, and scenarios with changing start and end points have not yet been explored. However, these limitations can be overcome. For future research, firstly, we can develop more efficient algorithms to improve computational efficiency. Secondly, different types of vehicles can be represented by changing parameter settings, such as different vehicle loads and vehicle costs.