Synthetic Population Generation for Autonomous Vehicle Demand Forecasting

The growing interest in Automated Mobility on Demand (AMoD) services in passenger transportation necessitates accurate forecasting for successful deployment. However, the paucity of real-world data is a significant challenge. In this study, we present a unique technique for developing a synthetic user population tailored to AMoD car services. We identify possible passengers using selection criteria such as age, gender, activity status, and income, and then utilize a multi-agent simulation tool to define passenger movements within the AMoD service and plan out daily journeys. Additionally, a spatiotemporal analysis reveals use patterns that are well captured by Machine Learning models such as Random Forest, XGBoost, Neural Networks (NN), and Linear Regression (LR). Finally, by estimating spatio-temporal demand for automated cars, our model gives critical insights into the ideal allocation of fleet resources, thereby advancing the progress of AMoD transportation systems.