The Electric Scooter Collection Problem: A Case Study in the City of Vienna

Abstract

The urban population has been growing rapidly, especially in the European Union. The trend of urbanization has led to an increased demand for mobility, through both passenger and goods transportation. One of latest trends in passenger transportation is electric scooters, which have been offered under a framework of shared mobility since 2017. This paper addresses an optimization problem emerging from the process of collecting e-scooters from the streets of Vienna during the night. One of the major planning issues for rental companies is the uncertainty of service times, i.e., the time needed to locate and load the e-scooters onto the vans. We formulated the e-scooter collection problem as an extension of the vehicle routing problem with the goal of minimizing the number of vans needed to collect the scooters and the distance traveled by vans, as well as penalizing belated collection. We proposed a solution method based on a large neighborhood search and solved problem instances generated based on real-world data. We then evaluated the impact of the service time uncertainty on the total system costs through a scenario analysis. Furthermore, we proposed a dynamic re-optimization policy that made use of real-time information on service times. We showed that the dynamic policy outperformed the static policy by 4–17% and could lead to reductions in delays of 49–54%, depending on the standard deviation.