Citation

Ham SW, Cho JH, Park S, Kim DK. Transp. Res. Rec. 2021; 2675(11): 34-43.

Copyright

(Copyright © 2021, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981211003896

PMID

unavailable

Abstract

The electric scooter (e-scooter) sharing service has attracted significant attention because of its extensive usage and eco-friendliness. Since e-scooters are mostly accessed by foot, the presence of e-scooters within walking distance has a crucial effect on the service quality. Therefore, to maintain appropriate service quality, relocation strategies are often used to properly distribute e-scooters within service areas. There are extensive literatures on demand forecasting for an efficient relocation. However, the study of the relocation of small-scale spatial units within walking distance level is still inadequate because of the sparsity of demand data. This research aims to establish an effective methodology for predicting the demand for e-scooters in high spatial resolution. A new grid-based spatial setting was created with the usage data. The model in the methodology predicts not only the identified demand but also the unmet demand to increase practicality. A convolutional autoencoder is used to obtain the latent feature that can reduce the problem of representing sparse data. An encoder-recurrent neural network-decoder (ERD) framework with a convolutional autoencoder resulted in a huge improvement in predicting spatiotemporal events. This new ERD framework shows enhanced prediction performance, reducing the mean squared error loss to 0.00036 from 0.00679 compared with the baseline long short-term memory model. This methodological strategy has its significance in that it can solve any prediction issue with spatiotemporal data, even those with sparse data problems.

Language: en