Kaggle’s “Taxi Trajectory Data from ECML/PKDD 15: Taxi Trip Time Prediction (II) Competition” is one of the most used mobility / vehicle trajectory datasets in computer science. However, in contrast to other similar datasets, Kaggle’s taxi trajectories are provided in a format that is not readily usable in MovingPandas since the spatiotemporal information is provided as:
TIMESTAMP: (integer) Unix Timestamp (in seconds). It identifies the trip’s start;
POLYLINE: (String): It contains a list of GPS coordinates (i.e. WGS84 format) mapped as a string. The beginning and the end of the string are identified with brackets (i.e. [ and ], respectively). Each pair of coordinates is also identified by the same brackets as [LONGITUDE, LATITUDE]. This list contains one pair of coordinates for each 15 seconds of trip. The last list item corresponds to the trip’s destination while the first one represents its start;
Therefore, we need to create a DataFrame with one point + timestamp per row before we can use MovingPandas to create Trajectories and analyze them.
But first things first. Let’s download the dataset:
import datetime
import pandas as pd
import geopandas as gpd
import movingpandas as mpd
import opendatasets as od
from os.path import exists
from shapely.geometry import Point
input_file_path = 'taxi-trajectory/train.csv'
def get_porto_taxi_from_kaggle():
if not exists(input_file_path):
od.download("https://www.kaggle.com/datasets/crailtap/taxi-trajectory")
get_porto_taxi_from_kaggle()
df = pd.read_csv(input_file_path, nrows=10, usecols=['TRIP_ID', 'TAXI_ID', 'TIMESTAMP', 'MISSING_DATA', 'POLYLINE'])
df.POLYLINE = df.POLYLINE.apply(eval) # string to list
df
And now for the remodelling:
def unixtime_to_datetime(unix_time):
return datetime.datetime.fromtimestamp(unix_time)
def compute_datetime(row):
unix_time = row['TIMESTAMP']
offset = row['running_number'] * datetime.timedelta(seconds=15)
return unixtime_to_datetime(unix_time) + offset
def create_point(xy):
try:
return Point(xy)
except TypeError: # when there are nan values in the input data
return None
new_df = df.explode('POLYLINE')
new_df['geometry'] = new_df['POLYLINE'].apply(create_point)
new_df['running_number'] = new_df.groupby('TRIP_ID').cumcount()
new_df['datetime'] = new_df.apply(compute_datetime, axis=1)
new_df.drop(columns=['POLYLINE', 'TIMESTAMP', 'running_number'], inplace=True)
new_df
And that’s it. Now we can create the trajectories:
That’s it. Now our MovingPandas.TrajectoryCollection is ready for further analysis.
By the way, the plot above illustrates a new feature in the recent MovingPandas 0.16 release which, among other features, introduced plots with arrow markers that show the movement direction. Other new features include a completely new custom distance, speed, and acceleration unit support. This means that, for example, instead of always getting speed in meters per second, you can now specify your desired output units, including km/h, mph, or nm/h (knots).
These releases are a huge step forward towards making MovingPandas easier to install with fewer mandatory dependencies. All interactive plotting libraries are now optional. So if you are using MovingPandas for trajectory data processing in the background and don’t need the interactive visualization features, the number of necessary libraries is now much lower. This (and the fact that GeoPandas is now shipped with OSGeo4W) will also make it easier to use MovingPandas in QGIS plugins.
We have further improved our repo setup by adding an action that automatically creates and publishes packages from releases, heavily inspired by the work of the GeoPandas team.
Last but not least, we’ve created a Twitter account for the project. (And might soon add a Mastodon account as well.)
As always, all tutorials are available from the movingpandas-examples repository and on MyBinder:
If you have questions about using MovingPandas or just want to discuss new ideas, you’re welcome to join our discussion forum.
New functions to add a timedelta column and get the trajectory sampling interval #233
As always, all tutorials are available from the movingpandas-examples repository and on MyBinder:
The new distance measures are covered in tutorial #11:
Computing distances between trajectories, as illustrated in tutorial #11Computing distances between a trajectory and other geometry objects, as illustrated in tutorial #11
But don’t miss the great features covered by the other notebooks, such as outlier cleaning and smoothing:
Trajectory cleaning and smoothing, as illustrated in tutorial #10
If you have questions about using MovingPandas or just want to discuss new ideas, you’re welcome to join our discussion forum.
Speed and direction column names can now be customized
If you have questions about using MovingPandas or just want to discuss new ideas, you’re welcome to join our recently opened discussion forum.
As always, all tutorials are available from the movingpandas-examples repository and on MyBinder:
Besides others examples, the movingpandas-examples repo contains the following tech demo: an interactive app built with Panel that demonstrates different MovingPandas stop detection parameters
To start the app, open the stopdetection-app.ipynb notebook and press the green Panel button in the Jupyter Lab toolbar:
The Kalman filter in action on the Geolife sample: smoother, less jiggly trajectories.Top-Down Time Ratio generalization aka trajectory compression in action: reduces the number of positions along the trajectory without altering the spatiotemporal properties, such as speed, too much.
Behind the scenes, Ray Bell took care of moving testing from Travis to Github Actions, and together we worked through the steps to ensure that the source code is now properly linted using flake8 and black.
Being able to work with so many awesome contributors has made this release really special for me. It’s great to see the project attracting more developer interest.
As always, all tutorials are available from the movingpandas-examples repository and on MyBinder:
In the last few days, there’s been a sharp rise in interest in vessel movements, and particularly, in understanding where and why vessels stop. Following the grounding of Ever Given in the Suez Canal, satellite images and vessel tracking data (AIS) visualizations are everywhere:
Using movement data analytics tools, such as MovingPandas, we can dig deeper and explore patterns in the data.
The MovingPandas.TrajectoryStopDetector is particularly useful in this situation. We can provide it with a Trajectory or TrajectoryCollection and let it detect all stops, that is, instances were the moving object stayed within a certain area (with a diameter of 1000m in this example) for a an extended duration (at least 3 hours).
The resulting stop segments include spatial and temporal information about the stop location and duration. To make this info more easily accessible, let’s turn the stop segment TrajectoryCollection into a point GeoDataFrame:
stop_pts = gpd.GeoDataFrame(columns=['geometry']).set_geometry('geometry')
stop_pts['stop_id'] = [track.id for track in stops.trajectories]
stop_pts= stop_pts.set_index('stop_id')
for stop in stops:
stop_pts.at[stop.id, 'ID'] = stop.df['ID'][0]
stop_pts.at[stop.id, 'datetime'] = stop.get_start_time()
stop_pts.at[stop.id, 'duration_h'] = stop.get_duration().total_seconds()/3600
stop_pts.at[stop.id, 'geometry'] = stop.get_start_location()
Indeed, I think the next version of MovingPandas should include a function that directly returns stops as points.
Now we can explore the stop information. For example, the map plot shows that stops are concentrated in three main areas: the northern and southern ends of the Canal, as well as the Great Bitter Lake in the middle. By looking at the timing of stops and their duration in a scatter plot, we can clearly see that the Ever Given stop (red) caused a chain reaction: the numerous points lining up on the diagonal of the scatter plot represent stops that very likely are results of the blockage:
Before the grounding, the stop distribution nicely illustrates the canal schedule. Vessels have to wait until it’s turn for their direction to go through:
You can see the full analysis workflow in the following video. Please turn on the captions for details.
Huge thanks to VesselsValue for supplying the data!
Free and Open Source GIS Ramblings 