Seattle Inductive Loop Detector Dataset V.1 (2015)

Dataset for Network-wide Traffic Forecasting

The data is collected by the inductive loop detectors deployed on freeways in Seattle area. The freeways contains I-5, I-405, I-90, and SR-520, shown in the above picture. This dataset contains spatio-temporal speed information of the freeway system. In the picture, each blue icon demonstrates loop detectors at a milepost. The speed information at a milepost is averaged from multiple loop detectors on the mainlanes in a same direction at the specific milepost. The time interval of the dataset is 5-minute.

The data download link contains a list of files:

speed_matrix_2015: Loop Speed Matrix, which is a pickled file that can be read by pandas or other python packages.
Loop_Seattle_2015_A.npy: Loop Adjacency Matrix, which is a numpy matrix to describe the traffic network structure as a graph.
Loop_Seattle_2015_reachability_free_flow_Xmin.npy: Loop Free-flow Reachability Matrix during X minites' drive.
nodes_loop_mp_list.csv: List of loop detectors' milepost, with the same order of that in the Loop Speed Matrix.

A demo of the speed_matrix_2015 is shown as the following figure. The horizontal header denotes the milepost and the vertical header indicates the timestamps.

The name of each milepost header contains 11 characters:

1 char: 'd' or 'i', i.e. decreasing direction or increasing direction.
2-4 chars: route name, e.g. '405' demonstrates the route I-405.
5-6 chars: 'es' has no meanings here.
7-11 chars: milepost, e.g. '15036' demonstrates the 150.36 milepost.

Data Download Link: Seattle Loop Dataset

If you use this dataset in your work, please cite the following reference:

Reference:

Cui, Z., Ke, R., & Wang, Y. (2018). Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction. arXiv preprint arXiv:1801.02143.
Cui, Z., Henrickson, K., Ke, R., & Wang, Y. (2018). High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. arXiv preprint arXiv:1802.07007.

BibTex:

@article{cui2018deep,
  title={Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction},
  author={Cui, Zhiyong and Ke, Ruimin and Wang, Yinhai},
  journal={arXiv preprint arXiv:1801.02143},
  year={2018}
} ,
@article{cui2018high,
  title={High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting},
  author={Cui, Zhiyong and Henrickson, Kristian and Ke, Ruimin and Wang, Yinhai},
  journal={arXiv preprint arXiv:1802.07007},
  year={2018}
}