# Predicting Freeway Travel Time Using Multiple-Source Heterogeneous Data Integration
## Case Study
### Results and Comparative Analysis  
The data adopted in this study were obtained during the Chinese Spring Festival from 15 to 21 February 2018.Therefore, the data were divided into weekdays and holidays. There were 336 sets of data from 1 to 14 February 2018 (14 days data) in each group of toll stations; 264 groups (11 days) were randomly selected as training data input, and 72 groups (3 days) were adopted as prediction numbers. There were 168 sets of data from 15 to 21 February 2018 (11 days data) in each group of toll stations; 96 groups (4 days) were randomly selected as training data input, and 72 groups (3 days) were adopted as prediction numbers. The detection time is from 17:00 to 19:00, and the value was taken as test data. BP neural network, SVM, and optimized SVM were used for the prediction. The root mean square error (RMSE), the mean absolute percent error (MAPE) and the covariance protocol (CP) were selected as the error evaluation criteria in the prediction process [33]. The RMSE is a comprehensive evaluation indicator of the prediction effect, the MAPE is the prediction relative error, while the CP is the error component analysis indicator. Figure 5 show the forecasting effect diagram of the holiday evening peak. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Section** & **Penalty Parameter, \(C\)** & **Nuclear Parameter, \(\sigma\)** & **Insensitive Loss** \\  & & & **Function Parameter, \(\epsilon\)** \\ \hline Changsha West–Guanshan & 6.8755 & 0.0064 & 0.3461 \\ Changsha West–Ningxiang & 8.6485 & 0.0034 & 0.6991 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimization of parameter values. Figure 4: The parameter optimization curve. From Table 5, one can observe that the weekday was predicted by the RMSE, the MAPE, and the CP data between the two toll stations. It appears that all three models can be used for predicting travel time. Although the prediction error of the BP neural network may be larger than those of the SVM and the optimized SVM models, there is no deviation between the error of the SVM and the optimized SVM model. However, when forecasting holidays with heavy traffic and long travel times, the RMSE of the optimized SVM model is significantly better than those of the BP neural network and the SVM model. Figure 5: Results of freeway travel time prediction. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Changsha West–Guanshan Station**} & \multicolumn{3}{c}{**Changsha West–Ningxiang Station**} \\ \cline{3-10}  & & **RMSE** & **L-RMSE** & **MAPE** & **CP** & **RMSE** & **L-RMSE** & **MAPE** & **CP** \\ \hline \multirow{3}{*}{Weekday} & BPNN & 7.6544 & 9.3541 & 5.0873 & 0.5489 & 10.5124 & 11.9621 & 5.5104 & 0.5809 \\  & SVM & 6.3405 & 8.3952 & 4.8225 & 0.5810 & 9.0375 & 11.6215 & 4.5422 & 0.6930 \\  & OSVM & 6.0369 & 8.0369 & 4.6536 & 0.5952 & 8.3069 & 10.6541 & 4.4524 & 0.6404 \\ \hline \multirow{3}{*}{Weekend} & BPNN & 11.6308 & 13.6169 & 9.1023 & 0.5990 & 16.0845 & 16.9551 & 9.6148 & 0.6454 \\  & SVM & 11.5152 & 13.1510 & 7.8645 & 0.6460 & 13.3215 & 14.4151 & 8.1153 & 0.6905 \\  & OSVM & 9.6218 & 11.7411 & 6.2451 & 0.7621 & 12.2548 & 15.0215 & 7.8651 & 0.7245 \\ \hline \hline \end{tabular}  
* BPNN: BP Neural Network; SVM: Support Vector Machine; OSVM: Optimized SVM; L-RMSE: an RMSE that lacks multi-source data and uses only past data for prediction.  
\end{table} 
Table 5: Error evaluation of forecasting freeway travel time. In the prediction of Changsha West-Guanshan, the accuracy of the optimized SVM model using artificial fish swarm algorithm is 17.27% higher than that of the BP neural network model and 16.44% higher than the conventional SVM model. In the prediction of Changsha West-Ningxiang, the accuracy of the optimized SVM model using artificial fish swarm algorithm is 23.80% and 8.01% higher than those of the BP neural network model and the conventional SVM model, respectively. The RMSE lacking multi-source data was added to compare with the normal RMSE.