# Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
## 2 Data-Driven Traffic Estimation and Prediction
### Location-Based Service Data  
With the development of the mobile internet, location-based service (LBS) data have arisen with GPS functionality on smartphones. Various location-based big data are collected from location-based social networks, e.g, checkins, geotagged tweets, and micro-blogs, and Maps and Navigation Apps, e.g., Google Map and Baidu Map. These data are often collected in a crowd-sourced approach, and a traffic information extraction step is necessary. To effectively collect, integrate, and process crowd-sourced data, including mobile applications, webs, and external data sources, a prototype system was developed and validated in [47] to infer traffic conditions. Traffic speed data of 29 cities across the world over a 40-day period were gathered from Google Map API and further used for the analysis of traffic congestion patterns [48]. Social media texts may also contain traffic information, e.g., geotagged tweets and micro-blogs. However, the challenge is to extract traffic-relevant information from natural languages. Deep-learning models have been proposed for conducting information extraction, e.g., an LSTM-CNN was proposed to extract traffic-relevant microblogs in [49], which outperformed the baselines, including the support vector machine model based on a bag of n-gram features. Social media data were further proven effective for traffic accident detection and reporting [50; 51], traffic jam management [52]. Combining social media data from multiple social networks can further improve the traffic event detection accuracy, e.g., the case of combining Arabic and English data streams from Twitter and Instagram used in the SNSJam system [53]. However, location-based big data may be low-quality with noise. Taking Bluetooth speed data as the ground truth, the quality of Waze data in Sevierville, TN, USA was evaluated in [54], in which a kNN method managed to achieve a prediction accuracy of 84.5% and 82.9% for traffic speed estimation based on Waze data as the input. The list of open location-based service data is shown in Table 6. The pros of using LBS data include their wide availability and semantic information, e.g., restaurants and malls. However, the cons are also clear. Human mobility can only be partially detected when check-ins or geotagged texts are made. The data are thus very sparse and can be sparser than CDRs. There is also self-selection bias, which would cause inaccurate traffic information. \begin{table}
\begin{tabular}{p{113.8 pt} 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 Q-Traffic or Baidu-Traffic [55] & Traffic speed from navigation apps & Beijing, China & 1 April 2017 to 31 May 2017 & [[https://github.com/JingqingZ/Baidu](https://github.com/JingqingZ/Baidu)]([https://github.com/JingqingZ/Baidu](https://github.com/JingqingZ/Baidu)) Traffic \\ \hline Alibaba Cloud Tianchi Dataset & Travel time from navigation Apps & Guiyang, China & April 2017 & [[https://tianchi.aliyun.com/competition](https://tianchi.aliyun.com/competition)]([https://tianchi.aliyun.com/competition](https://tianchi.aliyun.com/competition)) on/entrance/231598/information \\ \hline Brightkite [56] & Check-ins & N/A & April 2008 to October 2010 & [[https://snap.stanford.edu/data/loc-b](https://snap.stanford.edu/data/loc-b)]([https://snap.stanford.edu/data/loc-b](https://snap.stanford.edu/data/loc-b)) rightkite.html \\ \hline Gowalla [56] & Check-ins & N/A & February 2009 to October 2010 & [[https://snap.stanford.edu/data/loc-g](https://snap.stanford.edu/data/loc-g)]([https://snap.stanford.edu/data/loc-g](https://snap.stanford.edu/data/loc-g)) owalla.html \\ \hline Foursquare [57] & Check-ins & NYC, USA & 24 October 2011 to 20 February 2012 & [[https://sites.google.com/site/yangdi](https://sites.google.com/site/yangdi)]([https://sites.google.com/site/yangdi](https://sites.google.com/site/yangdi)) ngqi/home/foursquare-dataset \\ \hline \hline \end{tabular}
\end{table}