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https://github.com/zhiyongc/Seattle-Loop-Data


https://github.com/zhiyongc/Seattle-Loop-Data

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# 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.



### Update (2021 Jan.)

Three Seattle loop detector datasets (pickled files) are added to the download link. The formats of the three files is similar to the speed matrix file.

* `volume_avg_matrix_2015`: containing the **averaged volume** over all lanes of a road segment (a set of loop detectors)

* `volume_total_matrix_2015`: containing the **total volume** information (total volume = averaged volume * lane number)

* `occupancy_avg_matrix_2015`: containning the **averaged occupancy** information.

---

### Data Download Link: [Seattle Loop Dataset](https://drive.google.com/drive/folders/1E-rRwIPFDZcTWc7zZDcyd4XbIgecW97q?usp=sharing)



---

#### 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. (2019). Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. IEEE Transactions on Intelligent Transportation Systems.`

###### 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{cui2019traffic,

  title={Traffic 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={IEEE Transactions on Intelligent Transportation Systems},

  year={2019},

  publisher={IEEE}

}

```

#### Note: This dataset should only be used for research.