# Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
## 2 Data-Driven Traffic Estimation and Prediction
### External Data  
In addition to the above data that are used to extract and predict traffic states, some external data are also used for these problems as supplementary data, e.g., weather data, calendar information, emissions of air pollutants, and noise. These data do not have a large volume and are easily available. There are many public sources for weather data, e.g., Weather Underground API ([[https://www.wunderground.com/weather/api/](https://www.wunderground.com/weather/api/)]([https://www.wunderground.com/weather/api/](https://www.wunderground.com/weather/api/))), MesoWest ([[http://mesowest.utah.edu/](http://mesowest.utah.edu/)]([http://mesowest.utah.edu/](http://mesowest.utah.edu/))). Calendar information is also widely accessible from the internet, e.g., OfficeHolidays ([[https://www.officeholidays.com/](https://www.officeholidays.com/)]([https://www.officeholidays.com/](https://www.officeholidays.com/))). To name some relevant studies that use these external data, 48 weather forecasting factors were analyzed and used for traffic flow prediction based on a regression model in [77], and rainfall data have also been used for similar purposes [78; 79].  
This section is concluded with Table 9, which shows the relationship between the potential data sources with possible data attributes. This table is far from being perfect but is a preliminary reference.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name** & **Spatial Range** & **Temporal Range** & **Download Link** \\ \hline MIT Traffic Dataset [72] & One camera in MIT & 90 min long & [[http://mmlab.ie.cuhk.edu.hk/datasets/mit](http://mmlab.ie.cuhk.edu.hk/datasets/mit)]([http://mmlab.ie.cuhk.edu.hk/datasets/mit](http://mmlab.ie.cuhk.edu.hk/datasets/mit))\_traffic/index.html \\ \hline Video Surveillance Data [73] & One camera in & 982 video frames & [[https://github.com/alnfedorov/traffic-a](https://github.com/alnfedorov/traffic-a)]([https://github.com/alnfedorov/traffic-a](https://github.com/alnfedorov/traffic-a)) \\  & Chelyabinsk, Russia & & analysis \\ \hline \hline \end{tabular}
\end{table}
Table 8: The list of open video data.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **Read** & **Read** & **Cyclist** & **Bicycle** & **Passenger Flow in Public** & **Pedestrian** & **Pedestrian** & **Pedestrian** \\  & **Traffic Volume** & **Traffic Speed** & **Speed** & **Volume** & **Transport Systems** & **Speed** & **Volume** & **Rout** \\ \hline Traffic signal controllers \& Road sensors & + & + & & & & & \\ \hline GPS sensors & + & + & & & & & & \\ \hline \hline Bicycle counters & & & & + & & & & \\ \hline Passenger counters \& AFC systems & & & & + & & & \\ \hline On-board computers & & & & & + & & & \\ \hline Cellular phones \& Smartphones & & & & + & + & + \\ \hline CCTV cameras & + & + & + & + & + & + & + \\ \hline ANPR systems & + & +(1) & & & & & \\ \hline ETC systems & +(2) & & & & & & \\ \hline \hline \end{tabular} (1) only segmental speed measurement; (2) only volume measurement in the entrance.  
\end{table}
Table 9: A summary for the linking between data sources and possible results.