Time and space dynamic demand forecasting of station-free sharing bikes on campus

ABSTRACT

To put forward effective balance strategy of station-freesh a ring bike soncampus, massive trip data of station-free sharing bikes before the COVID-19 occurring on Southeast University were mined. The trip data included 15 687 trip productions and 15 410 trip attractions. Firstly, the data's temporal and spatial characteristics were analyzed, and a short-term travel prediction model for station-free sharing bikes on campus at intervals of 15 and 30 min separately using autoregressive integrated moving average (ARIMA) was established. Then, an identification method of station-free sharing bikes hot spots on campus by combining the density-based spatial clustering of applications with noise (DBSCAN) clustering method and the K-dist graphs was constructed. Finally, the management and control strategy was proposed for station-free sharing bikes on campus. The results show that imbalanced spatial and temporal demand of bike sharing trips on campus. From temporal demand, the average daily campus trip volume of station-free sharing bikes on weekdays is significantly higher than that on weekend, and the peak of daily campus trip volume occurs on Monday. The campus trip peak hours of station-free sharing bikes are closely related to the school time of teachers and students. The peak hours of trip origins are 0700 to 08 00 and 1300 to 1400 on weekdays, and the peak hours of trip destinations are 1100 to 1200 and 1700 to 1800 on weekdays. From spatial demand, the origin and destination locations of station-free sharing bikes appear obvious distribution of "hot spots", and the parking hot spots are concentrated in the school gate, library, gymnasiums and important teaching buildings. The time series forecasting model is developed and the mean absolute error value is between 0. 600 to 0. 989, and it indicating a high prediction accuracy of the model. The time series forecasting model can provide technical support for real-time scheduling of station-free sharing bikes on campus. By predicting the temporal and spatial travel demand of station-free sharing bikes on campus, research results can help establish sharing bikes' delivery or allocation mechanism which are adapt to campus space capacity, parking hardware facilities, travel demand distribution and so on. Meanwhile, research results can provide the basis for campus administrators and sharing bikes operators to optimize sharing bikes parking management on campus. 7 tabs, 9 figs, 26 refs. © 2022 Editorial Department of Journal of Chang'an University (Natural Science Edition). All rights reserved.