# Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
## 2 Data-Driven Traffic Estimation and Prediction
### GPS Trajectory Data  
GPS trajectory data are often collected from floating vehicles, which are equipped with on-board positioning systems and communication devices. Additionally, smartphones can also be used for GPS trajectory data collection, e.g., in a crowd-sourced approach. While GPS trajectory data are easy to collect, the preprocessing steps are necessary, e.g., location detection to group points into one meaningful location and trajectory segmentation to split a trajectory in sub-trajectories. Map matching is also used to map the GPS coordinates into the road network. Due to advantages, such as fine granularity, GPS-based \"Track and Trace\" data were formally defined and highlighted for transportation modeling and policy-making in a recent survey [33]. Taxi trajectory data can also be used for trip purpose inference and travel pattern discovery [4]. More relevant studies are using GPS trajectory data for traffic estimation and prediction, e.g., travel time is estimated with the GPS trajectory data in [34] by combining the gradient boosting decision tree (GBDT) model and the deep neural network (DNN) model, and OD pairs are predicted with big GPS data in [35]. Due to privacy concerns, most of the open GPS trajectory data are collected from taxis, with only a few exceptions, as shown in Table 4. Additionally, in many cases, the raw GPS trajectory data are not shared, and only the estimated traffic states derived from the GPS trajectory are publicly available. The pros of using GPS trajectory data include the high spatial and temporal resolution as well as the tracking ability of the full traces. Taxis can also be seen as mobile probes, which reflect all road traffic situations. The cons are similar to CDRs, which include the preprocessing requirement and the noise in the data. Moreover, the GPS trajectory data are often collected at a high frequency, e.g., in seconds, so the huge volume of such data requires storage and computation abilities, which can only be fulfilled by big data tools. While taxi GPS trajectory datasets are widely used in the literature, there is a concern that taxi drivers are not the best representatives since they are experienced drivers with a \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Name** & **Spatial Range** & **Temporal Range** & **Download Link** \\ \hline Telecommunications-SMS, call, internet-MI [32] & Milan, Italy & 1 November 2013 to 1 January 2014 & [[https://dataverse.harvard.edu/dataset.xhtm](https://dataverse.harvard.edu/dataset.xhtm)]([https://dataverse.harvard.edu/dataset.xhtm](https://dataverse.harvard.edu/dataset.xhtm)) \\ \hline OpenCellID & Multiple cities globally & Regularly updated & [[https://www.opencellid.org](https://www.opencellid.org)]([https://www.opencellid.org](https://www.opencellid.org)) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The list of CDR data. higher possibility of finding optimal routes. In some studies, this wisdom from taxi drivers is used to build better navigation services [36]. On the contrary, the driving behavior gap between taxi drivers and private car drivers are becoming smaller as these navigation services are now widely used by both groups. In the studies for traffic estimation and prediction, taxi drivers are preferred as probes because they drive longer and provide more traffic situation measurements with a larger coverage. In addition to the existing datasets, we also contribute a new GPS trajectory dataset for the research community in this study. The GPS trajectory data were collected in Beijing during three time periods, namely, November 2012, November 2014, and November 2015. Each GPS data sample contains the following attributes: anonymous taxi identity, timestamp, latitude, longitude, azimuth, spot speed, and operation status (occupied, vacant, or stopped). The data were sampled with an interval of approximately one minute.