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Visualization of trajectories#

If we work with trajectory data, we often want to visualize them from a so called birds eye view. The following example demonstrates how to achieve this with tasi using the DLR Urban Traffic Dataset.

Load trajectories#

At first, let’s load trajectories from the DLR dataset.

[1]:

from tasi.dlr import DLRUTDatasetManager, DLRUTVersion, DLRTrajectoryDataset

dataset = DLRUTDatasetManager(DLRUTVersion.latest)
# select a trajectory file from the middle of the dataset to include data from all object classes
ut = DLRTrajectoryDataset.from_csv(dataset.trajectory()[48])
ut

[1]:
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 12:00:00.016482+00:00 1695556712692966 0.003 0.010 0.009 604755.977 5792823.363 0.025 0.824 0.150 0.000 0.000 0.000 1.625 2.407 1.334 False 0.004 0.020 0.019 -73.593
1695556715491157 -1.093 1.235 -0.575 604795.259 5792804.167 0.000 0.883 0.109 0.005 0.003 0.000 1.556 3.463 2.045 False -3.971 4.113 -1.074 -164.869
1695556722944961 -0.000 0.001 0.001 604753.090 5792830.880 0.000 0.579 0.334 0.000 0.087 0.000 1.682 2.135 1.124 False -0.000 0.008 -0.008 -69.165
1695556743992329 -0.005 0.007 0.006 604751.898 5792835.737 0.000 0.590 0.060 0.000 0.344 0.000 1.990 2.508 1.565 False -0.012 0.016 0.010 -68.221
1695556773745982 0.013 0.021 0.016 604793.176 5792766.007 0.002 0.811 0.176 0.011 0.000 0.000 1.366 2.739 1.101 False 0.014 0.026 -0.022 110.513
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2023-09-24 12:14:59.966482+00:00 1695557695944822 -0.626 0.632 -0.085 604725.113 5792777.272 0.008 0.925 0.056 0.000 0.011 0.000 1.450 2.909 1.466 True 10.926 11.018 1.419 7.340
1695557698396270 0.127 0.327 -0.301 604798.425 5792719.248 0.000 0.989 0.003 0.006 0.001 0.000 1.472 3.275 1.686 False -4.345 13.879 13.181 108.258
1695557698694887 0.149 0.161 0.061 604810.204 5792821.677 1.000 0.000 0.000 0.000 0.000 0.000 1.656 0.909 0.497 False -4.130 4.296 -1.184 -163.998
1695557699646115 -0.706 0.709 0.068 604685.307 5792776.662 0.000 0.996 0.000 0.000 0.000 0.004 1.637 3.301 1.654 False 11.289 11.297 0.410 2.078
1695557700093347 -1.155 1.187 -0.274 604652.341 5792771.867 0.000 0.692 0.308 0.000 0.000 0.000 1.306 3.873 1.524 True 12.743 12.759 -0.639 -2.968

299053 rows × 19 columns

Plot trajectories#

We now utilize the TrajectoryPlotter to visualize the trajectories of the traffic participants that we’ve loaded.

[2]:

from tasi.plotting import TrajectoryPlotter
import matplotlib.pyplot as plt

f, ax = plt.subplots()

plotter = TrajectoryPlotter()
plotter.plot(ut, ax=ax)
../../_images/user_guide_data_visualization_trajectory_3_0.png

Change trajectory color#

Note that the trajectories are colorized with the default color which we can change so any arbitrary color, such as lightgray.

[3]:

f, ax = plt.subplots()
plotter.plot(ut, ax=ax, color="lightgray")
../../_images/user_guide_data_visualization_trajectory_5_0.png

Change color of a specific trajectory#

You can also define the color of a specific trajectory.

[4]:

f, ax = plt.subplots()
plotter.plot(
    ut, ax=ax, color="black", trajectory_kwargs={ut.ids[-20]: {"color": "red"}}
)
../../_images/user_guide_data_visualization_trajectory_7_0.png

Change trajectory opacity#

or even change the opacity of each trajectory to quickly get an overview of the traffic density. Let’s change the opacity to 20% via the alpha argument.

[5]:

f, ax = plt.subplots()
plotter.plot(ut, ax=ax, alpha=0.2)
../../_images/user_guide_data_visualization_trajectory_9_0.png

The plot already indicates the various traffic volumes on the different routes at the intersection.

Plot DLR UT trajectories on orthophoto#

We can also combine the TrajectoryPlotter with the BoundingboxPlotter to visualize trajectories on an orthophoto.

[6]:

import numpy as np
from tasi.plotting import BoundingboxPlotter, LowerSaxonyOrthophotoTile

f, ax = plt.subplots()

# plot the orthophoto first
bbox_plotter = BoundingboxPlotter(ut.roi, LowerSaxonyOrthophotoTile())
bbox_plotter.plot(ax)

# and the trajectories on top
tj_plotter = TrajectoryPlotter()
tj_plotter.plot(ut, ax=ax)

# hide the axis
_ = ax.axis("off")

[2025-04-25 12:19:22 | tiles.py:fetch:184] > INFO:  Requesting https://opendata.lgln.niedersachsen.de/doorman/noauth/dop_wms?bbox=604649,5792683,604883,5792859&service=WMS&crs=EPSG%3A25832&format=image%2Fpng&request=GetMap&layers=ni_dop20&styles=&version=1.3.0&width=512&height=385
../../_images/user_guide_data_visualization_trajectory_12_1.png

Note that it may become hard to see the trajectories on the background. To increase the contrast, we can adapt the opacity of the ortophoto via the alpha argument. Let’s set is to 0.5 (50%) to highlight the trajectories.

[7]:

f, ax = plt.subplots()

bbox_plotter.plot(ax, alpha=0.5)
tj_plotter.plot(ut, ax=ax, alpha=0.25)

_ = ax.axis("off")
../../_images/user_guide_data_visualization_trajectory_14_0.png

Plot DLR HT trajectories on orthophoto#

We can also use the TrajectoryPlotter with the BoundingboxPlotter to visualize trajectories of the DLR HT dataset on an orthophoto. Let’s load and plot the trajectories from the DLR HT dataset. This time, let’s use the DLRTrajectoryPlotter to color the trajectories in default DLR-colors.

[8]:

from tasi.dlr.dataset import DLRHTDatasetManager, DLRHTVersion
from tasi.dlr.plotting import DLRTrajectoryPlotter

# load dataset
dataset = DLRHTDatasetManager(DLRHTVersion.latest)
path = dataset.load()
# use 3 as it contains objects from all object classes
ht = DLRTrajectoryDataset.from_csv(dataset.trajectory()[3])

f, ax = plt.subplots()

# plot the orthophoto first
bbox_plotter = BoundingboxPlotter(ht.roi, LowerSaxonyOrthophotoTile())
bbox_plotter.plot(ax)

# and the trajectories on top
tj_plotter = DLRTrajectoryPlotter()
tj_plotter.plot(ht, ax=ax)

# hide the axis
_ = ax.axis("off")

[2025-04-25 12:19:27 | dataset.py:load:130] > INFO:  Checking if dataset already downloaded /tmp/DLR-Highway-Traffic-dataset_v1-1-0
[2025-04-25 12:19:27 | dataset.py:load:166] > INFO:  Dataset already available at /tmp/DLR-Highway-Traffic-dataset_v1-1-0
[2025-04-25 12:19:29 | tiles.py:fetch:184] > INFO:  Requesting https://opendata.lgln.niedersachsen.de/doorman/noauth/dop_wms?bbox=614174,5791044,617606,5796205&service=WMS&crs=EPSG%3A25832&format=image%2Fpng&request=GetMap&layers=ni_dop20&styles=&version=1.3.0&width=512&height=769
../../_images/user_guide_data_visualization_trajectory_16_1.png

Visualizing geospatial trajectories#

The tasi model for representing trajectory data using geospatial objects opens the world to utilize tools that support geopandas. For instance, if you want an interactive view on trajectory data, we can utilize folium via geopandas. For this purpose, we need two conversion steps.

At first, we need to convert the dataset to a native tasi representation.

[9]:

ds = ut.to_tasi()
ds.head()

[9]:
position velocity acceleration heading yaw_rate ... boundingbox
easting northing easting magnitude northing easting magnitude northing ... front front_right right rear_right rear
... easting northing easting northing easting northing easting northing easting northing
timestamp id
2023-09-24 12:00:00.016482+00:00 1695556712692966 604755.977 5792823.363 0.004 0.020 0.019 0.003 0.010 0.009 -73.593 NaN ... 604756.316939 5.792822e+06 604755.677100 5.792822e+06 604755.337161 5.792823e+06 604754.997222 5.792824e+06 604755.637061 5.792825e+06
1695556715491157 604795.259 5792804.167 -3.971 4.113 -1.074 -1.093 1.235 -0.575 -164.869 -inf ... 604793.587528 5.792804e+06 604793.320628 5.792805e+06 604794.992100 5.792805e+06 604796.663572 5.792806e+06 604796.930472 5.792805e+06
1695556722944961 604753.090 5792830.880 -0.000 0.008 -0.008 -0.000 0.001 0.001 -69.165 inf ... 604753.469686 5.792830e+06 604752.944436 5.792830e+06 604752.564750 5.792831e+06 604752.185064 5.792832e+06 604752.710314 5.792832e+06
1695556743992329 604751.898 5792835.737 -0.012 0.016 0.010 -0.005 0.007 0.006 -68.221 inf ... 604752.363268 5.792835e+06 604751.636622 5.792834e+06 604751.171353 5.792835e+06 604750.706085 5.792837e+06 604751.432732 5.792837e+06
1695556773745982 604793.176 5792766.007 0.014 0.026 -0.022 0.013 0.021 0.016 110.513 inf ... 604792.696100 5.792767e+06 604793.211694 5.792767e+06 604793.691594 5.792766e+06 604794.171494 5.792765e+06 604793.655900 5.792765e+06

5 rows × 35 columns

Note that the boundingbox attribute was added in this step. Since ds is a tasi.TrajectoryDataset it also uses pandas. Hence, we will now convert to use geopandas while the position attribute should be encoded as a GeoObject.

[10]:

gds = ds.as_geopandas("position")

We need to set the position attribute as the active geometry.

[11]:

gds.set_geometry("position", inplace=True)

Now, we are ready to visualize the trajectories in a dynamic window, while we use the “CartoDB positron” background layer for reference.

[12]:

gds.explore(crs="EPSG:32632", tiles="CartoDB positron")

[12]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Note that for each traffic participant (or trajectory), additional information is available when hovering over its representation in the map. You can customize the representaton of the trajectories and add or remove attributes to you liking.

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