Speakers
Description
Dockless e-scooters have flooded into many cities as a new type of shared vehicle, giving transportation planners great challenges in providing timely support towards setting up necessary infrastructure and regulations. Despite the need, there have been relatively few studies on shared dockless e-scooters and even less attention to identifying anomalous usage patterns. These studies are an essential to guide policymaking and assist in the management of e-scooter fleets. In this paper, we identify and analyze anomalous usage patterns of dockless e-scooters using an unsupervised deep learning approach, ConvLSTM-Autoencoder, applied to large sets of data from three e-scooter companies operating in Washington, DC. The approach used in this study has successfully identified meaningful anomalies in the dockless e-scooter data collected in the city. During the evaluation process, we were able to associate specific driving factors to specific identified anomalies, including adverse weather, large social events, government policy mandates, and company maintenance operations. Our results suggest that an unsupervised deep learning approach, specifically ConvLSTM-Autoencoder, can effectively identify abnormal usage patterns of dockless e-scooters (and similar shared vehicle use) in an automatic way with high reliability.
