This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Pick-Up Time Analysis and Prediction for Carsharing Users Based on Decision Tree
Journal Article
13-03-02-0010
ISSN: 2640-642X, e-ISSN: 2640-6438
Sector:
Citation:
Sai, Q., Bi, J., Wang, Y., Zhi, R. et al., "Pick-Up Time Analysis and Prediction for Carsharing Users Based on Decision Tree," SAE J. STEEP 3(2):115-127, 2022, https://doi.org/10.4271/13-03-02-0010.
Language:
English
Abstract:
The development of carsharing can reduce the number of private cars, which can
save resources. Due to the limited supply of vehicles and diversified demands of
users, it is necessary to plan the temporal and spatial distribution of cars.
Predicting the pick-up time of carsharing users is of great significance to
understand the travel preference of carsharing users, which can help operators
formulate operational strategies such as relocation and pricing. To this end,
this study adopts an improved decision tree (DT) to analyze and predict pick-up
time for carsharing users. Firstly, the ordered clustering method is used to
discretize time. Secondly, the random forest (RF) model is constructed to
extract key features. Finally, the model of the C5.0 DT is constructed to
predict the pick-up time of users. A case study is conducted to demonstrate the
proposed model. The results indicate that the prediction accuracy of users’
pick-up time can reach 87%. The characteristic of pick-up time of carsharing
users is clearly analyzed.