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Smart city article details
| Title | Distribution Prediction For Reconfiguring Urban Dockless E-Scooter Sharing Systems |
|---|---|
| ID_Doc | 20754 |
| Authors | He S.; Shin K.G. |
| Year | 2022 |
| Published | IEEE Transactions on Knowledge and Data Engineering, 34, 12 |
| DOI | http://dx.doi.org/10.1109/TKDE.2021.3062074 |
| Abstract | Dockless E-scooter Sharing (DES) has become a popular means of last-mile commute for many smart cities. As e-scooters are getting deployed dynamically and flexibly across city regions that expand and/or shrink, accurate prediction of the e-scooter distribution given the reconfigured regions becomes essential for city planning. We present GCScoot, a novel flow distribution prediction approach for reconfiguring urban DES systems. Based on real-world datasets with reconfiguration, we analyze e-scooter distribution features and flow dynamics for the data-driven designs. We propose a novel spatio-temporal graph capsule neural network within GCScoot to predict future dockless e-scooter flows given the reconfigured regions. GCScoot pre-processes historical spatial e-scooter distributions into flow graph structures, where discretized city regions are considered as nodes and inter-region flows as edges. To facilitate initial training, we cluster the regions and generate virtual data for new deployment regions based on their peers in the same cluster. Given above designs, the region-to-region correlations embedded within the temporal flow graphs are captured via the multi-graph capsule convolutional neural network which accurately predicts the DES flows. Extensive studies upon four e-scooter datasets (total $>$>3.4 million rides) in four populous US cities have corroborated accuracy and effectiveness of GCScoot in predicting the e-scooter distributions. © 1989-2012 IEEE. |
| Author Keywords | distribution prediction; Dockless e-scooter; reconfiguration |
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