# Predicting Freeway Travel Time Using Multiple-Source Heterogeneous Data Integration
## 5 Conclusions  
This study performed an in-depth analysis of freeway travel time prediction to provide a high-quality travel experience for users. We found that freeway travel time is increased by holiday, weather, accident and traffic. We analyzed the effect of different factors, such as accidents and holidays,  
Figure 7: Comparison of weekday and holiday.  
by removing the effects of accidents and traffic through data integration and comparing them with original data.  
Bad weather reduces the overall traffic rate, which increases the travel time. Traffic accidents lead to reduced road traffic capacity, which increases the travel time. Free passage on highways during holidays and the increased demand for travel result in increased vehicle flow, which also increases the travel time.  
In this study, we analyzed basic data and proposed to base the traffic state prediction method on SVM data mining technology. This transformed the problem into a quadratic programming problem using the artificial fish swarm algorithm, which reduces the computational and local optimal problems of traditional neural networks. The parameters of the SVM were optimized using traditional network optimization, and a global optimal solution was obtained. Results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the conventional SVM model, respectively. Accurate prediction of freeway travel time was realized, which can provide data support for monitoring, early warning, and decision analysis for the freeway operation status.  
In this study, influencing factors such as weather, traffic, accidents and holidays were included in the optimization of the SVM prediction model. However, owing to limitations of the number of samples, the model was not fully trained. Therefore, a certain error occurred in the prediction results.  
In the future, it will be necessary to categorize traffic accidents, clarify the impact of each type of accident on the travel time, categorize the increase in holiday traffic, and clarify the impact of each level of accident on the travel time. Furthermore, the number of training samples, database capacity, and prediction accuracy should be continuously increased. In this study, only data from freeway toll stations were validated; application to actual large-scale road networks should be further explored in the future.  
This work was conducted by [PERSON] and [PERSON] with the help of graduate student [PERSON]. It was mainly drafted by [PERSON] and [PERSON], and checked and revised by [PERSON] and [PERSON]. [PERSON] and [PERSON] designed and analyzed the proposed model. [PERSON] and [PERSON] performed the simulation. [PERSON] and [PERSON] are responsible for the overall manuscript framework and language.  
This research was funded by National Natural Science Foundation of China (NSFC), grant number 51678076, Hunan Provincial Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, grant number 2017 TP1016.  
The authors express their thanks to all who participated in this research for their cooperation. The authors would like to give great thank to the hard work by the peer reviewers and editor.  
The authors declare no conflict of interest.