Citation

Saum N, Piantanakulchai M, Sugiura S. Transp. Res. Interdiscip. Persp. 2024; 23: e101019.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.trip.2024.101019

PMID

unavailable

Abstract

Accurate demand forecasting is a key success for mobility service businesses, especially shared electric (e-)scooters, for their volatile demand, high operational costs, and strict regulations. The heteroscedasticity of transportation demand is usually overlooked even it is very important for designing efficient supply management. This study proposed a supply planning framework considering heteroscedasticity in the hourly e-scooter demand. Three shared e-scooter datasets (Austin TX, Minneapolis MN, and Thammasat TH) were examined to extract temporal patterns. These features were used as inputs for the demand prediction models, including machine learning and deep learning models. Then, the squared residuals were subjected to variance prediction, including constant or daily variance and variance predicted by Autoregressive Conditional Heteroscedasticity (ARCH). Finally, the outputs of these models were combined to determine the supply level. Four supply level models (with constant, daily, Seasonal Generalized ARCH or SGARCH, and Box Cox variances) were compared based on the Mean Oversupply (MO) metric. As a result, demand prediction models with Box Cox transformed data possibly provide higher prediction accuracy than those with original or normalized data, specifically Mean Absolute Error (MAE). Supply level models with Box Cox variance had the lowest MO at lower percentages of served demand, whereas those with SGARCH variance had lower MO at higher percentages of served demand. At 95 % served demand, considering heteroscedastic demand in supply level planning could reduce oversupply by 26.22 %. From a policy perspective, operators could use our framework to minimize the demand uncertainty for daily operation, along with other potential policies such as customer incentives and hybrid real-time and periodic rebalancing.

Language: en

Keywords

Box Cox Transformation; Deep Learning; Machine Learning; SGARCH; Shared E-Scooters; Supply Planning