Research Article
Dynamic Scheduling Strategy Based on Demand Prediction of Shared Bike
@INPROCEEDINGS{10.1007/978-3-031-70507-6_32, author={Xiangkai Qiu and Wenbing Yang and Haoyang Zhou and He Wang and Shangjing Lin}, title={Dynamic Scheduling Strategy Based on Demand Prediction of Shared Bike}, proceedings={IoT as a Service. 9th EAI International Conference, IoTaaS 2023, Nanjing, China, October 27-29, 2023, Proceedings}, proceedings_a={IOTAAS}, year={2024}, month={10}, keywords={Shared bike Spatio-temporal analysis User incentive scheduling}, doi={10.1007/978-3-031-70507-6_32} }- Xiangkai Qiu
Wenbing Yang
Haoyang Zhou
He Wang
Shangjing Lin
Year: 2024
Dynamic Scheduling Strategy Based on Demand Prediction of Shared Bike
IOTAAS
Springer
DOI: 10.1007/978-3-031-70507-6_32
Abstract
This paper aims to address the issue of imbalanced supply and demand in shared bikes by proposing the concept of ‘red packet bike’, utilizing the open-source Beijing Shared Bike Dataset. The Temporal Graph Convolutional Network (T-GCN) is selected to predict the demand for shared bikes based on the analysis of spatio-temporal correlations in the order data. Additionally, a user incentive scheduling algorithm is designed using the breadth-first algorithm (BFS) and presented in the form of distributing the red packet bikes, thereby delegating the scheduling problem to the users to solve.
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