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A dataset for understanding urban transportation. This is an urban traffic speed dataset, which consists of 214 anonymous road segments within two months (i.e., from August 1, 2016 to September 30, 2016) at 10-minute interval, and the speed observations were collected in Guangzhou, China.

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Urban Traffic Speed Dataset of Guangzhou, China

1. Data Description

This is an urban traffic speed dataset which consists of 214 anonymous road segments within two months (i.e., from August 1, 2016 to September 30, 2016) at 10-minute interval, and the speed observations were collected from Guangzhou, China. A detailed description and files of this dataset are also available at Zenodo -- Urban Traffic Speed Dataset of Guangzhou, China.

2. File Description

speeddata.csv - the traffic speed dataset (contains 1,855,589 speed observations). Note that, for the convenience, speeddata.csv is separated into two files (i.e., speeddata_Aug.csv & speeddata_Sep.csv) where speeddata_Aug.csv covers the total observations during August, 2016 and speeddata_Sep.csv covers the total observations during September, 2016, respectively.

(1) road_id: a unique anonymous identifier for each road segment. As an example, 1 indicates the first road segment;

(2) day_id: a unique code indicating the date. In this column, 1 represents Aug. 1, 2016, 2 represents Aug. 2, 2016, as such, 61 represents Sep. 30, 2016;

(3) time_id: a unique code indicating the time windows. For example, 1 represents 00:00:00-00:10:00, 2 represents 00:10:00-00:20:00;

(4) speed: the speed values with unit km/h.


tensor.mat - the third-order tensor in Matlab and it can be directly loaded. In detail, we have

(1) the length of its first dimension corresponding to road semgent is 214;

(2) the length of second dimension corresponding to day is 61;

(3) the length of third dimension corresponding to time window is 144;

(4) the tensor entries is valued by traffic speed where 0 indicates the unobserved.

The number of non-zero entries of this tensor is 1,855,589 and the total entries is 1,879,776. So, the missing rate of this tensor is originally given by 1.29%.

3. Publication

Xinyu Chen, Zhaocheng He, Jiawei Wang, 2018. Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition. Transportation Research Part C: Emerging Technologies, 86, 59-77. Download PDF

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A dataset for understanding urban transportation. This is an urban traffic speed dataset, which consists of 214 anonymous road segments within two months (i.e., from August 1, 2016 to September 30, 2016) at 10-minute interval, and the speed observations were collected in Guangzhou, China.

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