Integrating demand forecasts into the operational strategies of shared automated vehicle mobility services: spatial resolution impacts
ridership - forecasting, ridership - demand, infrastructure - fleet management, technology - intelligent transport systems
Automated vehicles, mobility services, shared mobility, demand forecast
This study aims to evaluate and quantify the impact of demand forecast spatial resolution on the operational performance of a shared-use automated vehicle (AV) mobility service (SAMS) fleet. To perform the evaluation, this study employs an agent-based modeling framework that includes user requests, AVs, and an SAMS fleet controller. In the simulation, an SAMS fleet controller dynamically assigns AVs to on-demand user requests and repositions empty AVs throughout the service region to serve expected future demand requests. The fleet controller uses an offline demand forecast model and an online optimization model that jointly assigns AVs to users and repositioning trips. Results indicate that despite demand forecast quality decreasing at higher spatial resolutions, the operational efficiency of the SAMS fleet increases with higher spatial resolution forecasts (i.e. smaller subareas). Results also indicate that there is a significant operational value associated with improving short-term demand forecasts at high spatial resolutions.
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Hyland, M., Dandl, F., Bogenberger, K., & Mahmassani, H. (2020). Integrating demand forecasts into the operational strategies of shared automated vehicle mobility services: spatial resolution impacts. Transportation Letters, Vol. 12(10), pp. 671-676.