## 6 Discussion and Conclusions  
Previous studies have focused on analyzing raw data from GPS sensors to promote the accuracy of GPS data. Recent research has focused on developing methods to study link travel time prediction, such as the Kalman filter model and other data driven models. Generally, the existing methods can  
Figure 11: Influence of different input factors on ANN model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Testing** & **Model** & **Model** & **Model** & **Model** & **Model** & **Model** & **Model** & **Model** \\
**Dataset** & **M** & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** \\ \hline
1\% & 12.46\% & 17.85\% & 18.08\% & 19.28\% & 16.86\% & 15.79\% & 16.84\% & 15.97\% & 17.54\% \\
5\% & 15.90\% & 18.23\% & 18.56\% & 19.33\% & 17.73\% & 16.38\% & 17.17\% & 16.17\% & 17.78\% \\
10\% & 15.61\% & 18.27\% & 19.19\% & 19.88\% & 17.79\% & 16.65\% & 17.59\% & 16.45\% & 18.21\% \\
20\% & 15.02\% & 18.43\% & 19.27\% & 20.21\% & 17.97\% & 17.07\% & 17.47\% & 16.87\% & 18.37\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: MAPE of ANN with some influence factors excluded. best predict link travel times when there are abundant data. However, they ignore situations when there are insufficient data and do not account for meteorological factors. Consequently, to predict link travel time using sparse data, incorporating multi-factors is a challenge. We propose an ANN model for travel time prediction of target links using sparse data and incorporating the influence of multi-factors such as weather conditions. The running state of probe vehicle to some extent reflects the state of road traffic. Using data collected by probe vehicles, we aggregated data from Monday to Friday for single taxis traveling target link on weekdays. We extracted feature relationships between target links and adjacent links for time-periods based on big data from probe vehicles. Temperature and rainfall were also input to our ANN model. We designed a three-layer artificial neural network model with eight neurons in the input layer and one neuron in the output layer. Of all these extracted features, the ratio between target link and adjacent link was the output of ANN model and the other features were the input of ANN model. We used the designed artificial neural network model for training, validation. Finally, the trained neural network model was used for link travel time prediction. Different models constructed with different factors were used to evaluate the influence of factors on link travel time prediction. The main contribution of our research is that we can predict link travel time using sparse data. At the same time, we incorporated multi-factors into our model and researched their influence on link travel time. The model was verified by historical big data--about 224,830,178 records from probe vehicles from May to July 2014. The experimental results showed that, when data are sparse, we can obtain better results using an artificial neural network model based on feature relationships between target links and adjacent links and big historical data. We could know from model comparison, all those factors we selected have influence on link travel time prediction. Among those factors, the three factors the day of the week, the 30 min interval of the day, and the expected speed of adjacent links had higher influence on link travel time prediction than other factors Rainfall also affected link travel time prediction but is not as important as the three dominant factors. Our results are influenced by techniques and experimental conditions. For example, the location accuracy is influenced by GPS devices, while the map-matching algorithm affects the precision of GPS trajectories. Meanwhile, travel time is also affected by the calculation algorithms for single probe vehicles traveling target links. This paper only focused on link travel time on a local road network.