2022
Yu, Liang; Feng, Tao; Li, Tie; Cheng, Lei
Demand Prediction and Optimal Allocation of Shared Bikes Around Urban Rail Transit Stations Journal Article
In: Urban Transit Rail, 2022.
Abstract | Links | BibTeX | Tags: Big data, Bike sharing, Machine learning, Transit
@article{nokey,
title = {Demand Prediction and Optimal Allocation of Shared Bikes Around Urban Rail Transit Stations},
author = {Liang Yu and Tao Feng and Tie Li and Lei Cheng},
url = {https://link.springer.com/content/pdf/10.1007/s40864-022-00183-w.pdf?pdf=button},
doi = {https://doi.org/10.1007/s40864-022-00183-w},
year = {2022},
date = {2022-12-13},
journal = {Urban Transit Rail},
abstract = {The imbalance between the supply and demand of shared bikes is prominent in many urban rail transit stations, which urgently requires an efficient vehicle deployment strategy. In this paper, we propose an integrated model to optimize the deployment of shared bikes around urban rail transit stations, incorporating a seasonal autoregressive integrated moving average with long short-term memory (SARIMA-LSTM) hybrid model that is used to predict the heterogeneous demand for shared bikes in space and time. The shared bike deployment strategy was formulated based on the actual deployment process and under the principle of cost minimization involving labor and transportation. The model is applied using the big data of shared bikes in Xicheng District, Beijing. Results show that the SARIMA-LSTM hybrid model has great advantages in predicting the demand for shared bikes. The proposed allocation strategy provides a new way to solve the imbalance challenge between the supply and demand of shared bikes and contributes to the development of a sustainable transportation system.},
keywords = {Big data, Bike sharing, Machine learning, Transit},
pubstate = {published},
tppubtype = {article}
}
The imbalance between the supply and demand of shared bikes is prominent in many urban rail transit stations, which urgently requires an efficient vehicle deployment strategy. In this paper, we propose an integrated model to optimize the deployment of shared bikes around urban rail transit stations, incorporating a seasonal autoregressive integrated moving average with long short-term memory (SARIMA-LSTM) hybrid model that is used to predict the heterogeneous demand for shared bikes in space and time. The shared bike deployment strategy was formulated based on the actual deployment process and under the principle of cost minimization involving labor and transportation. The model is applied using the big data of shared bikes in Xicheng District, Beijing. Results show that the SARIMA-LSTM hybrid model has great advantages in predicting the demand for shared bikes. The proposed allocation strategy provides a new way to solve the imbalance challenge between the supply and demand of shared bikes and contributes to the development of a sustainable transportation system.