Freeway Inductive Loop Detector Dataset for Network-wide Traffic Speed Prediction

<p>The data is collected by the inductive loop detectors deployed on freeways in Seattle area. The freeways contain&nbsp;I-5, I-405, I-90, and SR-520. This data set contains spatiotemporal speed information of the freeway system. At each milepost, the speed information collected from main lane loop detectors in the&nbsp;same direction are averaged and integrated into 5 minutes interval speed data. The raw&nbsp;data is provided by Washington Start Department of Transportation (WSDOT) and processed by the <a href="http://www.uwstarlab.org/">STAR Lab</a> in the University of Washington according to data quality control and data imputation procedures [1][2].&nbsp;&nbsp;</p> <p>The data file is a pickle file that can be easily read using the read_pickle() function in the Pandas package. The data forms as a matrix and each cell of the matrix is speed value for the specific milepost and time period. The&nbsp;horizontal header of the data set denotes the milepost and the vertical header indicates the timestamps. For more information on the definition of milepost, please refer to this <a href="http://data.wsdot.wa.gov/traffic/">website</a>.</p> <p>This data set been used for traffic prediction tasks in several research studies [3][4]. For more detailed information about the data set, you can also refer to this <a href="https://github.com/zhiyongc/Seattle-Loop-Data">link</a>.</p> <p><strong>References</strong>:</p> <p>[1].&nbsp;Henrickson, K., Zou, Y., &amp; Wang, Y. (2015). Flexible and robust method for missing loop detector data imputation.&nbsp;<em>Transportation Research Record</em>,&nbsp;<em>2527</em>(1), 29-36.</p> <p>[2]. Wang, Y., Zhang, W., Henrickson, K., Ke, R., &amp; Cui, Z. (2016).&nbsp;<em>Digital roadway interactive visualization and evaluation network applications to WSDOT operational data usage</em>&nbsp;(No. WA-RD 854.1). Washington (State). Dept. of Transportation.</p> <p>[3].&nbsp;Cui, Z., Ke, R., &amp; Wang, Y. (2018). Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction.&nbsp;<em>arXiv preprint arXiv:1801.02143</em>.</p> <p>[4]. Cui, Z., Henrickson, K., Ke, R., &amp; Wang, Y. (2018). Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting.&nbsp;<em>arXiv preprint arXiv:1802.07007</em>.</p>