Multi-source data-driven prediction for the dynamic pickup demand of one-way carsharing systems

Abstract

The one-way carsharing system has been widely used in the carsharing field due to its flexibility. However, one of its main disadvantages is the imbalance between supply and pickup demand. At present, multi-source data are available for the real-time prediction of pickup demand. The multi-source data that are used for this purpose include real-time user application log data, historical order data, real-time station data, and user characteristic data. Based on these data, a demand prediction model was used to predict in real-time whether there is a pickup demand, and a demand time prediction model was applied to forecast the time at which a sharing vehicle is needed. Finally, a case study was conducted using 10 stations’ one-week field data to test the benefits of the models. The potential application of this study would effectively guide the system to formulate an active operation optimisation strategy to meet users’ demand.

KEYWORDS:

• One-way carsharing system
• multi-source data
• demand prediction
• user application log data

Acknowledgment

This study was supported by the National Natural Science Foundation of China (51722809) and the Fundamental Research Funds for the Central Universities (22120190205).

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Ling Wang http://orcid.org/0000-0001-7901-3995

Wanjing Ma http://orcid.org/0000-0002-9403-3174

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 51722809]; Fundamental Research Funds for the Central Universities [grant number 22120190205].