# Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
## 2 Data-Driven Traffic Estimation and Prediction
### Road Sensor Data  
Static sensors can be deployed to collect traffic data, e.g., inductive loop detectors, magnetic and pneumatic tube sensors, radar sensors, infrared sensors, and acoustic sensors. Loop detectors are the most mature technology among road sensors and contribute many famous open datasets for traffic estimation and traffic prediction. A complete list of open  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Name** & **Type** & **Spatial Range** & **Temporal Range** & **Download Link** \\ \hline GB Road Traffic Counts & Aggregated traffic counts & Great Britain & 2000–2019 & [[https://data.gov.uk/dataset/208c0e7b-353f-4e2d-8b7a-1a7118467](https://data.gov.uk/dataset/208c0e7b-353f-4e2d-8b7a-1a7118467) acc/gb-road-traffic-cou]([https://data.gov.uk/dataset/208c0e7b-353f-4e2d-8b7a-1a7118467](https://data.gov.uk/dataset/208c0e7b-353f-4e2d-8b7a-1a7118467) acc/gb-road-traffic-cou) nts \\ \hline Highways England network journey time and traffic flow data & Traffic flow, travel time & Great Britain & 2006–2020 & [[https://data.gov.uk/dataset/9562c512-4a0b-45](https://data.gov.uk/dataset/9562c512-4a0b-45) ee-b6 ad-afc0f9b841f/highways-england-network-journey-time-and-traffic-flow-data]([https://data.gov.uk/dataset/9562c512-4a0b-45](https://data.gov.uk/dataset/9562c512-4a0b-45) ee-b6 ad-afc0f9b841f/highways-england-network-journey-time-and-traffic-flow-data) \\ \hline DataMall & Multiple types & Singapore & Frequently updated & [[https://dataall.lta.gov.sg/content/datamall/en/dynamic-data.html](https://dataall.lta.gov.sg/content/datamall/en/dynamic-data.html)]([https://dataall.lta.gov.sg/content/datamall/en/dynamic-data.html](https://dataall.lta.gov.sg/content/datamall/en/dynamic-data.html)) \\ \hline Minnesota Department of Transportation Traffic Data & Traffic flow, traffic speed & Twin Cities, MN, USA & Since 2011 & [[http://data.dot.state.mn.us/datatools/](http://data.dot.state.mn.us/datatools/)]([http://data.dot.state.mn.us/datatools/](http://data.dot.state.mn.us/datatools/)) \\ \hline Chicago Traffic Tracker & Traffic speed & Chicago, IL, USA & 2011–2018 & [[https://data.cityofchicago.org/Transportatio](https://data.cityofchicago.org/Transportatio)]([https://data.cityofchicago.org/Transportatio](https://data.cityofchicago.org/Transportatio)) n/Chicago-Traffic-Tracker-Historical-Congestion-Esti/77 hq-huss/data \\ \hline US-Accidents [19] & Traffic accident & USA & February 2016 to December 2020 & [[https://smoosavi.org/datasets/us](https://smoosavi.org/datasets/us)]([https://smoosavi.org/datasets/us](https://smoosavi.org/datasets/us))\_accidents \\ \hline \hline \end{tabular}
\end{table}
Table 1: The list of open survey data.  
road sensor data used in previous studies is shown in Table 2. The pros of road sensor data are their abilities to collect large volume data continuously and automatically, usually in minutes or seconds.  
The cons of these data are their fixed coverage in the spatial range as well as the missing data problem caused by sensor failures. Additionally, the traffic speed obtained from traffic sensors could be a point measurement and may not be suitable to calculate the average travel time across the road link.  
Traffic signals also belong to road sensor data, which are often seen in the literature. Different types of traffic signal data can be collected for traffic lights, traffic signal lanterns, traffic signal controller boxes, traffic signal poles, etc. For example, traffic light data are collected in those traffic lights with an internet connection, with a typical data type of numeric values for time to green. They also contain location and attributes of traffic controls located at each intersection. This type of data is more often used for traffic control relevant studies, instead of traffic estimation or prediction. For example, an open collection of the traffic light data is provided in [25] for the study of reinforcement learning based traffic control.