# Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
## 2 Data-Driven Traffic Estimation and Prediction
### Public Transport Transaction Data  
Transaction data can be collected in various public transport systems, especially those with automatic fare collection (AFC) systems, and further used for traffic estimation and prediction. For example, smart card data are often used for public transit origin-destination (OD) estimation [59]. Based on smart card and bus trajectory data, a two-stage transportation analysis approach is proposed to reconstruct the individual passenger trips and cluster these trips to identify the transit corridors in [60].  
Based on 10 million taxi trip records in New York and Shenzhen and using spatio-temporal clustering, Bayesian probability, and Monte Carlo simulation, a two-layer framework, which consists of an activity inference model and a pairing journey model, was proposed to extract and predict travel patterns [61]. Using the gravity model and Bayesian rules, the purpose of dockless shared-bike users was inferred from a shared bike dataset in Shenzhen, China, and the introduction of activity type proportion and service capacity of point of interest (POIs) was proven effective for inference [62].  
Based on the daily OD amount by transportation and by purpose and several surveys on time use and activities, an open people mass movement dataset named Open PFLOW was built in [63], which achieved a comparable accuracy with other approaches, e.g., commercial mobility data and traffic census.  
Another approach of collecting public transport transaction data is based on internet of Things and counter devices installed in the vehicle doors, e.g., infrared or RGB image-based passenger counters. This passive approach of collecting data can be conducted silently without the burden of passenger involvement. However, more errors may exist in the collected due to the device fault or misjudgement caused by passenger's strange behaviors.  
There are also many public transport transaction data that are available for the research community as shown in Table 7. The pros of using public transport transaction data are their wide availability and close connection with traffic states. The cons are the storage and computation requirements, which can be high if the data cover wide spatial and temporal ranges.  
Another problem with public transport transaction data is that they can be incomplete. For example, in some countries and regions, tickets are only validated at the entrance to the vehicles, e.g., bus or subway. In those cases, only the inflow data can be collected, and the scope of follow-up studies is limited without being able to obtain the outflow or OD flow situations.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Name** & **Type** & **Spatial Range** & **Temporal Range** & **Download Link** \\ \hline \multirow{2}{*}{Yelp} & \multirow{2}{*}{Check-ins} & \multirow{2}{*}{Multiple cities globally} & \multirow{2}{*}{Regularly updated} & \multirow{2}{*}{[[https://www.yelp.com/dataset](https://www.yelp.com/dataset)]([https://www.yelp.com/dataset](https://www.yelp.com/dataset))} \\ \cline{1-1} \cline{5-5}  & & & & \\ \hline MapBJ [58] & Traffic from & congestion navigation & Beijing, China & March 2016 to June 2016 & [[https://github.com/cxysteven/MapBJ](https://github.com/cxysteven/MapBJ)]([https://github.com/cxysteven/MapBJ](https://github.com/cxysteven/MapBJ)) \\ \cline{1-1}  & & & & 2016 & \\ \hline \multirow{2}{*}{Tecent API} & Traffic flow index & China & Since 2015 & [[https://heat.qq.com/](https://heat.qq.com/)]([https://heat.qq.com/](https://heat.qq.com/)) \\ \hline \multirow{2}{*}{Uber Movement} & Travel & time and speed & Multiple cities globally & Since 2017 & [[https://movement.uber.com/](https://movement.uber.com/)]([https://movement.uber.com/](https://movement.uber.com/)) \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._