# Short-Term Travel Demand Prediction of Online Ride-Hailing Based on Multi-Factor GRU Model
## 4 Result Analysis  
### Selection of the Evaluation Function  
In this paper, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and decision coefficient \(R2\) score are selected as the accuracy evaluation indexes to compare these prediction algorithms [29; 30; 31; 32]. The root mean square error (RMSE) is the square root of the ratio of the square of the deviation between the predicted value and the true value to the number of observations n. In actual measurements, n is always finite, and the true value can only be replaced by the most reliable (best) value. RMSE ranges from 0 to infinity, and is equal to 0 when the predicted value is in perfect agreement with the real value. The larger the error, the greater the value. The full name of MAE is mean absolute error, which is the mean of the absolute error. MAE is another commonly used regression loss function. It is the mean of the absolute sum of the difference between the target value and the predicted value. It represents the mean error margin of the predicted value, regardless of the direction of the error, and ranges from 0 to infinity. When the predicted value is exactly consistent with the true value, it is equal \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Number of hidden layers & 2 \\ \hline Number of each hidden layer neurons & 24 \\ \hline Training times & 50 \\ \hline Activation function of hidden recurrent layers & tanh \\ \hline \multirow{4}{*}{Details of weight parameters} & Weight from 12:00 a.m. Monday to 2:00 p.m. \\  & Friday is 0.4 \\ \cline{1-1}  & Weight from 2:00 p.m. Friday to 12:00 a.m. \\ \cline{1-1}  & Sunday is 0.6 \\ \cline{1-1}  & Weight of holidays is 0.8 \\ \cline{1-1}  & Rainfall weights are based on actual rainfall \\ \cline{1-1}  & intensity \\ \hline Backstep & 24 \\ \hline \end{tabular}
\end{table} 
Table 3: Detailed description of parameters. to 0, which is the perfect model. The larger the error, the greater the value. The advantage of MAE is that it is less sensitive to outliers and more stable as a loss function. The mean absolute percentage error (MAPE) is one of the most popular metrics used to evaluate prediction performance. MAPE ranges from 0 to infinity. When the MAPE value is 0%, it means the model is perfect, while if the MAPE value is greater than 100%, it means the model is poor. The value of the MAPE alone is meaningless because MAPE is a relative value, not an absolute value. The MAPE depends on the size of the numbers in the data. For example, if you have two numbers, one is 100, one is 1, and your predictions are 101 and 2, each with an error of 1, but the MAPE value is bigger and the other is smaller. Similarly, when the actual value is large, the values of RMSE and MAE will also be large. Therefore, it is meaningless to look at the values of RMSE and MAE alone; they need to be compared with other models under the same data. \(R^{2}\) refers to goodness of fit, and is the fitting degree of the regression line to the observed value. In deep learning, \(R^{2}\) is usually called the \(R2\) score. The \(R2\) score can be colloquially understood as using the mean as the error base to see if the prediction error is greater than or less than the mean base error. The value of the \(R2\) score ranges from 0 to 1. An \(R2\) score of 1 means that the predicted and true values in the sample are exactly the same without any error. In other words, the model we built perfectly fits all the real data, and is the best model with the highest \(R2\) score. However, the model is not usually this perfect, there is always an error, and when the error is small, the numerator is smaller than the denominator, the model is going to approach 1, which is still a good model, but as the error gets bigger and bigger, the \(R2\) score is going to get further and further away from the maximum of 1.