# Predicting Freeway Travel Time Using Multiple-Source Heterogeneous Data Integration
## 3 Support Vector Machine Model
### Model Construction  
The freeway travel time prediction model is based on the SVM algorithm and is constructed based on the relationship between the current travel time of the road segment and the past travel time of the road segment, the current weather, and the possibility of traffic control.  
In this study, we selected for analysis data from two toll stations with different distances from east to west of G5513. Moreover, because the passenger car accounts for the vast majority of the data, the travel time of the passenger car was taken as the prediction object.  
In order to guarantee the uniformity of the multiple source data, time interval is set as five minutes. Table 2 presents the characteristics of the toll stations.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Toll Station** & **Start Point** & **End Point** & **Distance (km)** \\ \hline
1 & Changsha West & Guanshan & 10.6 \\
2 & Changsha West & Ningxiang & 23.2 \\ \hline \end{tabular}
\end{table}
Table 2: Toll station for travel time prediction.  
Figure 2: Structure of Support Vector Machine (SVM) model.  
The structure of the SVM is similar to the neural network. The output is a linear combination of intermediate nodes, and each intermediate node corresponds to a support vector. To determine the optimal classification function, this study takes the four travel times of the time period before the prediction time as the input, namely \(t_{k-1}\), \(t_{k-2}\), \(t_{k-3}\), \(t_{k-4}\).  
\[t_{k}=g(t_{k-1},t_{k-2},t_{k-3},t_{k-4}) \tag{8}\]  
\(k\) is the current time period, and \(t_{k}\) represents the average travel time of all vehicles in the current predicted time period.  
In the prediction process, due to the continuity of time, we can use the travel time to characterize the traffic flow, thus using the four travel times of the time period before the prediction time as the input. In addition, there are variables that can influence the predictions: weather, accidents that increases the travel time, holiday or non-holiday, and day of the week; as in Table 3:  
Many traffic accidents occur on the freeway every day. To simplify the parameters, we divide traffic accidents into two categories: traffic accidents that increase travel time and those that do not. In this study, traffic accidents were regarded as invariant.  
Figure 3 shows a comparison of travel time increased by an accident with normal travel time on the afternoon of 17 February.  
Since SVM is a machine learning model, sample training is required before prediction. Multiple groups of any four consecutive travel times, daily weather, traffic accidents that increase the travel time, holidays, and weekday data were used as training samples to obtain a trained model. In the trained model, \(t_{k-1}\), \(t_{k-2}\), \(t_{k-3}\), \(t_{k-4}\), weather, accident, holiday, and week are used to predict the travel time of the next time period. When a certain number of training samples is achieved, real-time  
Figure 3: Comparison of accidents and normal travel time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable Category** & **Variable Name** & **Variable Type** & **Variable Value** & **Variable Meaning** \\ \hline \multirow{3}{*}{Meteorological} & \multirow{3}{*}{Weather status} & \multirow{3}{*}{Discrete} & Clear; Cloudy; Fog; & Clear; Cloudy; Fog; & \\  & & & Overcast; Light rain; & Overcast; Light rain; & Overcast; Light rain; \\  & & & mod rain; hvy rain & mod rain; hvy rain \\ \hline \multirow{3}{*}{Time} & Holiday & Discrete & 0 & N \\  & & & 1 & Y \\ \cline{2-5}  & \multirow{3}{*}{Weekday} & \multirow{3}{*}{Discrete} & 1 & Monday \\  & & & &... \\ \cline{1-1}  & & & 7 & Sunday \\ \hline Accident & Traffic accidents & Discrete & 0 & N \\ \cline{1-1}  & & & 1 & Y \\ \hline \hline \end{tabular}
\end{table}
Table 3: Variables type and its meanings.  
data input can be adopted to predict future results. Moreover, the model can be constantly modified based on the relation between the predicted data and the predicted data to prediction accuracy.