Station-level short-term demand forecast of carsharing system via station-embedding-based hybrid neural network

Abstract

Station-based one-way carsharing system brings transformation to public mobility and spurs the growth of sharing economy. The accurate estimation of rental and return demand of carsharing stations to support vehicle relocation is essential, hence a station-level short-term demand forecasting method named as station-embedding-based hybrid neural network (SEHNN) integrated by variational graph auto-encoder (VGAE) and long short-term memory network (LSTM) is proposed. The VGAE module undertakes the functions of station feature extraction and embedding, while the LSTM module captures the time series regularity and forecast the station-level demand. The results from the real data of Lanzhou, China demonstrate that, compared with ElasticNet, ARIMA, LSTM, and ConvLSTM, the mean absolute error of the proposed model targeted at hourly demand forecasting is reduced by 56.5%, 47.2%, 38.7%, and 38.5%, respectively, and it also outperforms some widely used models among different intervals and scales including main stations and subset carsharing system.

KEYWORDS:

• Urban traffic
• Demand forecast
• SEHNN
• Carsharing
• Machine learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (Nos. 91846202, 71621001, 72001018, 71931003), the 111 Project (No. B20071), and the Fundamental Research Funds for the Central Universities (No. 2021RC237).