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DLR Urban Traffic Dataset (DLR UT)#

This example should give a short overview on how to load the DLR Urban Traffic Dataset which is hosted on Zenodo.

Download dataset#

At first, we need to download the dataset. For that purpose, helper classes are available in tasi that we will utilize in the following. In particular, since we want to download the DLR UT dataset, we use the. The class contains a tasi.dlr.dataset.DLRUTVersion enumerator that may be used to specify the version of the dataset to download or to get the latest version.

[1]:

import os
from tasi.dlr.dataset import DLRUTDatasetManager, DLRUTVersion

dataset = DLRUTDatasetManager(DLRUTVersion.latest)
path = dataset.load()
path

[2025-04-25 12:19:07 | dataset.py:load:130] > INFO:  Checking if dataset already downloaded /tmp/DLR-Urban-Traffic-dataset_v1-2-0
[2025-04-25 12:19:07 | dataset.py:load:166] > INFO:  Dataset already available at /tmp/DLR-Urban-Traffic-dataset_v1-2-0

[1]:

PosixPath('/tmp/DLR-Urban-Traffic-dataset_v1-2-0')

The dataset is now available in the /tmp directory. Let’s have a look into the dataset and list the available traffic information

[2]:

folders = os.listdir(path)
folders

[2]:

['meta_data', 'raw_data']

Load trajectory data#

We can now utilize the tasi.dlr.dataset.DLRTrajectoryDataset class to load the trajectory data from the directory. For demonstration purpose, let’s load the first batch of the dataset.

[3]:

from tasi.dlr import DLRTrajectoryDataset

ds = DLRTrajectoryDataset.from_csv(dataset.trajectory()[0])
ds

[3]:
acceleration center classifications dimension interpolated velocity yaw
easting magnitude northing easting northing bicycle car motorbike pedestrian truck van height length width easting magnitude northing
timestamp id
2023-09-24 00:00:00.016482+00:00 1695513598769889 0.047 0.093 0.080 604825.208 5792819.993 0.0 0.645 0.205 0.0 0.054 0.096 1.562 4.294 1.811 False -12.789 13.496 -4.311 -161.405
2023-09-24 00:00:00.066482+00:00 1695513598769889 0.052 0.097 0.081 604824.540 5792819.770 0.0 0.645 0.205 0.0 0.054 0.096 1.562 4.294 1.811 False -12.788 13.494 -4.308 -161.411
2023-09-24 00:00:00.116482+00:00 1695513598769889 0.058 0.101 0.083 604823.871 5792819.547 0.0 0.645 0.205 0.0 0.054 0.096 1.562 4.294 1.811 False -12.787 13.493 -4.305 -161.418
2023-09-24 00:00:00.166482+00:00 1695513598769889 0.063 0.105 0.084 604823.202 5792819.325 0.0 0.645 0.205 0.0 0.054 0.096 1.562 4.294 1.811 False -12.786 13.490 -4.302 -161.426
2023-09-24 00:00:00.216482+00:00 1695513598769889 0.069 0.109 0.085 604822.533 5792819.103 0.0 0.645 0.205 0.0 0.054 0.096 1.562 4.294 1.811 False -12.785 13.488 -4.298 -161.435
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2023-09-24 00:14:59.766482+00:00 1695514432518548 -0.006 0.013 -0.012 604743.005 5792776.259 0.0 0.135 0.865 0.0 0.000 0.000 1.656 1.058 0.675 False -0.021 0.022 -0.006 8.619
2023-09-24 00:14:59.816482+00:00 1695514432518548 -0.005 0.012 -0.011 604743.003 5792776.260 0.0 0.135 0.865 0.0 0.000 0.000 1.656 1.058 0.675 False -0.022 0.023 -0.008 8.619
2023-09-24 00:14:59.866482+00:00 1695514432518548 -0.004 0.011 -0.010 604743.001 5792776.261 0.0 0.135 0.865 0.0 0.000 0.000 1.656 1.058 0.675 False -0.022 0.025 -0.011 8.619
2023-09-24 00:14:59.916482+00:00 1695514432518548 -0.004 0.009 -0.009 604742.999 5792776.262 0.0 0.135 0.865 0.0 0.000 0.000 1.656 1.058 0.675 False -0.023 0.027 -0.013 8.619
2023-09-24 00:14:59.966482+00:00 1695514432518548 -0.003 0.008 -0.007 604742.996 5792776.258 0.0 0.135 0.865 0.0 0.000 0.000 1.656 1.058 0.675 False -0.024 0.028 -0.015 8.619

33614 rows × 19 columns

Note that the Dataset is represented as a pandas.DataFrame since it inherits from it. The index of the Dataset contains the timestamp of a traffic participant’s state and its id as a unique identifier.

The traffic participant’s state include various information, including the center position, the velocity, dimension and classification type.

Load traffic light data#

The DLR UT dataset also contains information of the traffic lights. We utilize the tasi.dlr.dataset.DLRTrajectoryDataset class to load the information. For demonstration purpose, let’s load the first batch of the dataset.

[4]:

from tasi.dlr import DLRUTTrafficLightDataset

traffic_lights = DLRUTTrafficLightDataset.from_csv(dataset.traffic_lights()[0])
traffic_lights

[4]:
state
timestamp id
2023-09-24 00:00:00.992000+00:00 1 3
2 3
3 5
4 3
5 3
... ... ...
2023-09-24 00:14:59.987000+00:00 26 3
27 3
28 3
29 3
30 9

26910 rows × 1 columns

Load weather data#

The DLR Research Intersection is equipped with a weather station stat collects various information. We can utilize the tasi.dataset.WeatherDataset to load some of this information.

[5]:

from tasi.dlr.dataset import DLRWeatherDataset

weather = DLRWeatherDataset.from_csv(dataset.weather()[0])
weather

[5]:
air_pressure_msl air_temperature dew_point_temperature hail_intensity present_weather rain_accumulation rain_intensity relative_humidity snow_accumulation solar_radiation visibility wet_bulb_temperature wind_direction wind_gust_direction wind_speed
timestamp id
2023-09-24 00:00:00+00:00 DLR 1021.141 13.3 10.235 0.0 NaN NaN NaN 81.717 NaN NaN NaN 11.566 154.0 147.0 2.034
2023-09-24 00:00:10+00:00 DLR 1021.141 13.3 10.238 0.0 NaN NaN NaN 81.717 NaN NaN NaN 11.567 133.0 147.0 1.133
2023-09-24 00:00:20+00:00 DLR 1021.141 13.3 10.238 0.0 0.0 0.0 0.0 81.717 0.0 5.250 20000.0 11.567 163.0 147.0 2.333
2023-09-24 00:00:30+00:00 DLR 1021.141 13.3 10.238 0.0 NaN NaN NaN 81.733 NaN NaN NaN 11.567 163.0 147.0 1.701
2023-09-24 00:00:40+00:00 DLR 1021.141 13.3 10.238 0.0 NaN NaN NaN 81.733 NaN NaN NaN 11.567 122.0 147.0 1.500
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2023-09-24 00:14:10+00:00 DLR 1021.366 13.1 10.185 0.0 NaN NaN NaN 82.500 NaN NaN NaN 11.454 207.0 150.0 1.135
2023-09-24 00:14:20+00:00 DLR 1021.366 13.1 10.185 0.0 0.0 0.0 0.0 82.517 0.0 5.278 20000.0 11.454 196.0 150.0 0.898
2023-09-24 00:14:30+00:00 DLR 1021.366 13.1 10.185 0.0 NaN NaN NaN 82.533 NaN NaN NaN 11.454 163.0 150.0 1.432
2023-09-24 00:14:40+00:00 DLR 1021.366 13.1 10.185 0.0 NaN NaN NaN 82.550 NaN NaN NaN 11.454 182.0 150.0 0.734
2023-09-24 00:14:50+00:00 DLR 1021.366 13.1 10.185 0.0 0.0 0.0 0.0 82.567 0.0 NaN 20000.0 11.454 232.0 150.0 1.398

90 rows × 15 columns

Load air quality data#

Although also collected by the same weather station, information about the local air quality is available via another dataset.

[6]:

from tasi.dlr.dataset import DLRAirQualityDataset

air_quality = DLRAirQualityDataset.from_csv(dataset.air_quality()[0])
air_quality

[6]:
co_gas_concentration coarse_particle_mass_concentration fine_particle_mass_concentration no2_gas_concentration no_gas_concentration o3_gas_concentration so2_gas_concentration
timestamp id
2023-09-24 00:00:20+00:00 DLR 119.48 4.5 2.9 32.504 44.892 82.0 0.0
2023-09-24 00:01:20+00:00 DLR 118.32 4.5 2.9 32.504 44.892 84.0 0.0
2023-09-24 00:02:20+00:00 DLR 117.16 4.5 2.9 32.504 44.892 82.0 0.0
2023-09-24 00:03:20+00:00 DLR 116.00 4.5 2.9 30.592 44.892 84.0 0.0
2023-09-24 00:04:20+00:00 DLR 114.84 4.5 2.9 30.592 44.892 86.0 0.0
2023-09-24 00:05:20+00:00 DLR 114.84 4.5 2.9 30.592 44.892 86.0 0.0
2023-09-24 00:06:20+00:00 DLR 113.68 4.5 2.9 30.592 44.892 86.0 0.0
2023-09-24 00:07:20+00:00 DLR 112.52 4.5 2.9 32.504 44.892 86.0 0.0
2023-09-24 00:08:20+00:00 DLR 113.68 4.5 2.9 32.504 44.892 84.0 0.0
2023-09-24 00:09:20+00:00 DLR 112.52 4.5 2.9 32.504 44.892 84.0 0.0
2023-09-24 00:10:20+00:00 DLR 112.52 5.4 3.1 30.592 44.892 84.0 0.0
2023-09-24 00:11:20+00:00 DLR 112.52 5.4 3.1 32.504 44.892 84.0 0.0
2023-09-24 00:12:20+00:00 DLR 113.68 5.4 3.1 32.504 44.892 82.0 0.0
2023-09-24 00:13:20+00:00 DLR 113.68 5.4 3.1 32.504 44.892 82.0 0.0
2023-09-24 00:14:20+00:00 DLR 112.52 5.4 3.1 32.504 44.892 82.0 0.0

Load road quality information#

The same weather measurement station also collects information about the road condition.

[7]:

from tasi.dlr.dataset import DLRRoadConditionDataset

road_conditions = DLRRoadConditionDataset.from_csv(dataset.road_condition()[0])
road_conditions

[7]:
ice_layer_thickness snow_layer_thickness surface_grip surface_state surface_temperature water_layer_thickness
timestamp id
2023-09-24 00:00:20+00:00 DLR 0.0 0.0 0.82 1.0 14.4 0.0
2023-09-24 00:00:50+00:00 DLR 0.0 0.0 0.82 1.0 14.4 0.0
2023-09-24 00:01:20+00:00 DLR 0.0 0.0 0.82 1.0 14.4 0.0
2023-09-24 00:01:50+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:02:20+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:02:50+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:03:20+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:03:50+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:04:20+00:00 DLR 0.0 0.0 0.82 1.0 14.4 0.0
2023-09-24 00:04:50+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:05:20+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:05:50+00:00 DLR 0.0 0.0 0.82 1.0 14.4 0.0
2023-09-24 00:06:20+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:06:50+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:07:20+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:07:50+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:08:20+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:08:50+00:00 DLR 0.0 0.0 0.82 1.0 14.4 0.0
2023-09-24 00:09:20+00:00 DLR 0.0 0.0 0.82 1.0 14.4 0.0
2023-09-24 00:09:50+00:00 DLR 0.0 0.0 0.82 1.0 14.4 0.0
2023-09-24 00:10:20+00:00 DLR 0.0 0.0 0.82 1.0 14.4 0.0
2023-09-24 00:10:50+00:00 DLR 0.0 0.0 0.82 1.0 14.4 0.0
2023-09-24 00:11:20+00:00 DLR 0.0 0.0 0.82 1.0 14.4 0.0
2023-09-24 00:11:50+00:00 DLR 0.0 0.0 0.82 1.0 14.4 0.0
2023-09-24 00:12:20+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:12:50+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:13:20+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:13:50+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:14:20+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0
2023-09-24 00:14:50+00:00 DLR 0.0 0.0 0.82 1.0 14.5 0.0

Load traffic volume data#

The DLR UT dataset contains meta information like traffic volume data that were extracted from the raw trajectory data.

[8]:

from tasi.dlr.dataset import DLRTrafficVolumeDataset

traffic_volume = DLRTrafficVolumeDataset.from_csv(dataset.traffic_volume()[0])
traffic_volume

[8]:
East0South1 East1South1 East2West0 East2West1 East3West0 East3West1 North1South0 South1West1 South2North1 South3East0 South4East1 West0North1 West0West1 West1East0 West2East0 West2East1 West4South1
timestamp id
2023-09-24 00:00:00+00:00 DLR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2023-09-24 00:00:01+00:00 DLR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2023-09-24 00:00:02+00:00 DLR 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
2023-09-24 00:00:03+00:00 DLR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2023-09-24 00:00:04+00:00 DLR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2023-09-24 00:14:55+00:00 DLR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2023-09-24 00:14:56+00:00 DLR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2023-09-24 00:14:57+00:00 DLR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2023-09-24 00:14:58+00:00 DLR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2023-09-24 00:14:59+00:00 DLR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

900 rows × 17 columns

Load OpenSCENARIO files#

The DLR UT dataset contains the trajectory data in OpenSCENARIO format.

[9]:

openscenario_files = dataset.openscenario()
openscenario_files

[9]:

['/tmp/DLR-Urban-Traffic-dataset_v1-2-0/meta_data/openscenario/openscenario_230924-120000_230924-121500.xosc',
 '/tmp/DLR-Urban-Traffic-dataset_v1-2-0/meta_data/openscenario/openscenario_230924-121500_230924-123000.xosc',
 '/tmp/DLR-Urban-Traffic-dataset_v1-2-0/meta_data/openscenario/openscenario_230924-123000_230924-124500.xosc',
 '/tmp/DLR-Urban-Traffic-dataset_v1-2-0/meta_data/openscenario/openscenario_230924-124500_230924-130000.xosc']

That’s it for now. We hope this page helps you get started 😎

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