# Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
## 2 Data-Driven Traffic Estimation and Prediction
### Call Detail Records  
Call detail records (CDRs), cellular signaling data, and cell phone position data are all generated from the cellular network, which contains similar information on human mobility patterns. These data are produced by a phone carrier to store details of calls passing through a device and usually contain various attributes of the call, e.g., timestamp, source, destination, base transceiver station (BTS), duration. Then, the position and trajectory (sequence of locations) can be inferred from CDRs. Compared with GPS trajectory data with a higher spatial resolution, CDRs provide a coarse positioning approach. Data preprocessing is necessary not only for removing noise but also for identifying the key locations. As one of the pioneering studies, cellular phone tracking data are used for traffic volume estimation and further evaluated with loop detector data in [26]. Afterwards, an increasing number of studies have used call detail records and cell phone position data for traffic status inference, even in recent years. One-month cellular signaling data (SD) are used to extract road-level human mobility with a multi-information fusion framework, which takes the SD uncertainty issue into consideration [27]. \begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Name** & **Type** & **Spatial Range** & **Temporal Range** & **Download Link** \\ \hline Performance Measurement (PeMS) Data & Traffic speed, traffic flow & California, USA & 2001–2019 & [[http://pems.dot.ca.gov/](http://pems.dot.ca.gov/)]([http://pems.dot.ca.gov/](http://pems.dot.ca.gov/)) \\ \hline METR-LA [21] & Traffic speed, traffic flow & Los Angeles, USA & 1 March to 30 June 2012 & [[https://github.com/liyaguang/D](https://github.com/liyaguang/D)]([https://github.com/liyaguang/D](https://github.com/liyaguang/D)) \\ \hline Seattle-Loop-Data [22] & Traffic Speed & Seattle, USA & 1 – 31 January 2015 & [[https://github.com/zhiyongc/Se](https://github.com/zhiyongc/Se)]([https://github.com/zhiyongc/Se](https://github.com/zhiyongc/Se)) \\ \hline Traffic Speed Guangzhou [23] & Traffic speed & Guangzhou, China & 1 August 2016 to 30 September 2016 & [[https://zenodo.org/record/120522](https://zenodo.org/record/120522)]([https://zenodo.org/record/120522](https://zenodo.org/record/120522)) \\ \hline Traffic Flow Madrid & Traffic flow & Madrid, Spain & Since 2013 & [[http://datos.madrid.es](http://datos.madrid.es)]([http://datos.madrid.es](http://datos.madrid.es)) \\ \hline Traffic Flow Whitemud Drive & Traffic flow & Whitemud Drive, Canada & Whitemud Drive, August 2015 & 6 August 2015 to 28 a1/700.jhtml \\ \hline UTD19 [24] & Traffic capacity & 40 cities globally & Various time periods & [[https://utd19.ethz.ch/index.html](https://utd19.ethz.ch/index.html)]([https://utd19.ethz.ch/index.html](https://utd19.ethz.ch/index.html)) \\ \hline PORTAL & Traffic speed & Portland-Vancouver Metropolitan region, USA & Regularly updated & [[https://portal.its.pdx.edu/home](https://portal.its.pdx.edu/home)]([https://portal.its.pdx.edu/home](https://portal.its.pdx.edu/home)) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The list of open road sensor data. Based on a regression model, a cell probe (CP)-based method is proposed to estimate the vehicle speed with the normal location update (NLU) procedure and the consecutive handoff (HO) event as inputs, and the proposed method achieved a 97.63% accuracy [28]. Long-term evolution (LTE) access data are used as input for a deep-learning-based road traffic prediction system, to predict the real-time speed of traffic [29]. Characteristics of human mobility patterns were revealed by high-frequency cell phone position data in [30]. More usages can be found in recent surveys [31]. The publicly available CDR or similar data are shown in Table 3. The pros of using CDR or similar data are their ubiquitous availability (with mobile phones), large volume, and multiple dimensions (social, mobile, time, and demographics).