# Predicting Freeway Travel Time Using Multiple-Source Heterogeneous Data Integration
## 1 Introduction
### Overview of Prediction Method of Multiple Source Data  
Nowadays, traffic detector method and monitoring facilities have progressed rapidly. With the support of a large amount of data, it is possible to clearly visualize traffic flow changes under the joint action of different factors. Also, we can improve the adaptability and accuracy of traffic estimating model [11], and if the same state occurs, it can be predicted based on historical results. The more populated the database is, the higher the quality and the higher the likelihood of finding commonalities and predicting accurate results will be. This concept can be applied by searching for common traffic states prediction. Owing to progress in the dynamic traffic information acquisition system, various traffic data can be collected more easily. Big data platform can integrate these different data sources, and apply the international standardized data description methods to facilitate data fusion and expansion. Data fusion can integrate the information of different data source and mine the useful hidden information from multiple data sources. In data analysis, it is of great significance to mine the hidden semantic information in the data to assist in exploration. [PERSON] et al. [12] converted the geographic coordinate information (latitude and longitude) in the data to street names to reflect the contextual semantic information. The trajectory of each taxi is regarded as a document composed of the street names passed by the taxi, and then introduced. [PERSON] et al. [13] proposed a new method, _Semantic Traj_, to manage and visualize taxi trajectory data in an intuitive, semantic-rich, and efficient manner. Using multi-source data for prediction can overcome the limitations of the single data source. Semantic information in multi-source data can better describe the specific traffic flow state [14]. In other words, the single data source is of low quality and not comprehensive. The traffic state is described from different angles and directions to improve the accuracy of prediction and reduce disturbance from unexpected factors. The idea of data interoperability is used to facilitate the integration of data from different data sources, enabling multi-source data to be seamlessly linked as a single piece of data. The integration of data includes roads, directions, travel time, weather conditions, traffic accidents, holidays, etc. Many studies have been conducted on travel time prediction, especially studies based on the historical data travel time from multi-source data. Common prediction methods and their characteristics are summarized in Table 1.  
Compared to the single source data method, the multi-source data method can extract deep information within data, significantly reduce the cost of data acquisition, and make up for the lack \begin{table}
\begin{tabular}{c c c} \hline \hline
**Prediction Method** & **Author** & **Data Source** \\ \hline \multirow{4}{*}{Kalman filter} & [PERSON], 2006 [15] & Travel time data \\ \cline{2-3}  & [PERSON], 2014 [16] & Floating vehicle and fixed detector data \\ \cline{2-3}  & [PERSON], 2016 [17] & Electronic Toll Collection (ETC) and traditional \\ \cline{2-3}  & [PERSON], 2011 [18] & The real loop detector data of an I-66 segment in \\ \cline{2-3}  & [PERSON], 2016 [19] & A large-scale taxi trip dataset from New York City \\ \cline{2-3}  & [PERSON], 2012 [20] & Two detectors data in Prague and also in the \\ \cline{2-3}  & [PERSON], 2005 [21] & Travel time data \\ \cline{2-3}  & [PERSON], [PERSON], 2005 [22] & Travel data and Some missing or corrupt travel data \\ \hline \multirow{4}{*}{Consolidated behaviormodels} & [PERSON], 2001 [23] & Origin-Destination flow data \\ \cline{2-3}  & [PERSON], 2001 [24] & O rigin-Destination trip information \\ \cline{1-1} \cline{2-3}  & [PERSON], 2016 [25] & The flow and the user’s behavior \\ \cline{1-1} \cline{2-3}  & [PERSON], 2017 [26] & Traffic loops and the signal control plans in \\ \cline{1-1} \cline{2-3}  & Santander urban area \\ \hline \hline \end{tabular}
\end{table}