# Analysis and Prediction of Carsharing Demand Based on Data Mining Methods
## 5 Conclusions  
On the basis of actual operation data, this study analyzes the travel characteristics and demand characteristics of users in depth. GBDT is proposed to predict the travel demand of users. Compared with ARIMA, GDBT has higher prediction accuracy. Unlike [27], both ARIMA and GBDT use a nonlinear algorithm. Ref. [27] proposed an overall framework for analyzing historical free-floating carsharing service usage data and predicting short-term vehicle availability. In addition, the proposed framework allows us to analyze spatial and temporal context conditions. However, its considerations are not very comprehensive. Compared with [28; 28] comprehensively compares various machine learning methods. Moreover, random regression forest is a more useful method. Various influencing factors \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Station Collection** & **Mean Absolute Error/Per** & **Root Mean Square Error/Per** \\ \hline \multirow{9}{*}{GBDT} & Station collection 1 & 2.01 & 1.99 \\  & Station collection 2 & 1.89 & 1.92 \\  & Station collection 3 & 0.53 & 0.62 \\  & Station collection 4 & 0.48 & 0.54 \\  & Station collection 5 & 1.32 & 1.45 \\  & Station collection 6 & 0.38 & 0.46 \\  & Station collection 7 & 0.44 & 0.32 \\  & Station collection 8 & 1.03 & 0.92 \\  & Station collection 9 & 0.87 & 1.02 \\  & Station collection 10 & 0.52 & 0.65 \\  & Station collection 11 & 1.01 & 0.91 \\  & Station collection 12 & 0.74 & 0.68 \\  & Station collection 13 & 0.23 & 0.31 \\  & Station collection 14 & 1.34 & 1.28 \\  & Station collection 15 & 0.31 & 0.34 \\  & Station collection 1 & 3.22 & 3.47 \\  & Station collection 2 & 3.15 & 3.32 \\  & Station collection 3 & 0.94 & 0.88 \\  & Station collection 4 & 0.85 & 0.91 \\  & Station collection 5 & 2.11 & 1.98 \\  & Station collection 6 & 1.13 & 1.34 \\  & Station collection 7 & 0.78 & 0.64 \\ ARIMA & Station collection 8 & 1.87 & 2.08 \\  & Station collection 9 & 1.51 & 1.72 \\  & Station collection 10 & 1.02 & 0.99 \\  & Station collection 11 & 1.42 & 1.28 \\  & Station collection 12 & 1.33 & 1.41 \\  & Station collection 13 & 0.58 & 0.57 \\  & Station collection 14 & 2.12 & 2.01 \\  & Station collection 15 & 0.62 & 0.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Prediction error analysis. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Error Term** & **Error Value/Per** \\ \hline \multirow{4}{*}{ARIMA} & Maximum error & 7.23 \\  & Minimum error & \(-\)0.04 \\  & Mean absolute error & 1.51 \\  & Root mean square error & 1.56 \\ \hline \multirow{4}{*}{GBDT} & Maximum error & 5.84 \\  & Minimum error & 0.02 \\ \cline{1-1}  & Mean absolute error & 0.87 \\ \cline{1-1}  & Root mean square error & 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of prediction results. are considered in this study, such as historical demand status of adjacent stations, weather conditions, and so on. The parameters of these factors are discretized in time. Dozens of dimensional characteristics are extracted as model inputs, and the prediction accuracy is improved. However, this study still has some shortcoming. The data of this study are taken from carsharing operators in Lanzhou, Gansu Province, China. However, different cities and operators have different characteristics. Few factors are considered in this study. Price, travel reasons, and other factors will also affect users' willingness to use carsharing. Whether a user has coupons or not will also affect their choice. In the future, we hope to obtain more data and improve the prediction accuracy. Conceptualization and methodology, C.W. and J.B.; formal analysis, J.B.; validation, J.B.; writing--original draft, C.W. ; writing--review and editing, Q.S. and J.B.; supervision, Z.Y. All authors have read and agreed to the published version of the manuscript. This work is partially supported by the National Natural Science Foundation of China under Grant [No. 71961137008] and National Natural Science Foundation of China [No. 71621001].