DOI: 10.5281/zenodo.15077435
title: Vehicle Trajectory Dataset from Drone-Collected Data at Three Swiss Roundabouts

description: Overview

This dataset provides high-resolution, georeferenced vehicle trajectories collected via drone footage at three roundabouts located in the municipalities of Frick and Laufenburg, Canton of Aargau, Switzerland. The data were collected as part of a collaborative drone campaign organized by the Urban Transport Systems Laboratory (LUTS), EPFL, within the framework of NCCR Automation, in cooperation with the cantonal traffic planning department of Aargau. The collection took place on Monday, 23rd October 2023, during peak morning and afternoon hours, resulting in nearly 11 hours of 4K video data.

Dataset Composition

This dataset contains CSV files structured with consistent data fields representing georeferenced trajectories, vehicle types (car, bus, truck), and timestamps, capturing detailed vehicle movements within roundabout environments.

File Organization

File names follow the convention:

D{X}_{TP}{N}_{S}.csv



D{X} — the drone identifier, where {X} is a number (e.g., 1, 2) indicating which drone captured the data.→ Example: D1 = data collected by Drone 1.

{TP}{N} — the time period and session number, where {TP} is either AM (morning) or PM (afternoon), and {N} is an integer indicating the session number.→ Example: AM2 = second morning session.

{S} — the site identifier, corresponding to one of the monitored sites:→ F1 = Roundabout F1 (Frick)→ F2 = Roundabout F2 (Frick)→ L1 = Roundabout L1 (Laufenburg)


CSV File Structure

Each CSV file includes:




Column Name
Description
Format / Units


track_id
Unique vehicle identifier (per file)
Integer


type
Vehicle type (Car, Bus, Truck)
Categorical


lon
WGS84 geographic longitude
Decimal degrees (15 d.p.)


lat
WGS84 geographic latitude
Decimal degrees (15 d.p.)


time
Local timestamp in ISO 8601 format
String (hh:mm:ss.ss)




Data Collection and Processing



Collection Method: Two drones flying at an altitude of 120 meters above ground level, capturing videos at 4K resolution (3840×2160 pixels) at 29.97 FPS.

Locations:



Roundabout F1 (Frick): Intersection of Bahnhofstrasse and Hauptstrasse 3 (Urban)

Roundabout F2 (Frick): Intersection of Hauptstrasse 3 with Gänsacker and Stöcklimattstrasse (Urban)

Roundabout L1 (Laufenburg): Intersection at Hauptstrasse 7 near the German border (Rural)



Data Processing: The detection, tracking, and trajectory stabilization were performed using the early version of the Geo-trax framework (v0.1.0), an advanced computer vision pipeline tailored for drone-captured traffic footage. The resulting trajectories are precisely represented in stabilized pixel coordinates, which are subsequently transformed into geographic coordinates (WGS84). This georeferencing process follows a procedure similar to that described in Espadaler-Clapés et al., 2023, and includes:



Identification and extraction of Ground Control Points (GCPs) in the first stabilized video frame using QGIS Georeferencer, linking pixel coordinates to UTM coordinates.

Linear regression modeling between stabilized pixel coordinates and corresponding UTM coordinates derived from GCPs to estimate transformation parameters.

Projection to WGS84, converting UTM coordinates into global geographic coordinates using a standard GIS transformation (EPSG:4326).




Dataset Statistics




Roundabout
Videos
Avg. Duration (min)
Total Duration (min)
Vehicles (total)
Cars
Buses
Trucks


F1
8
18.63
149.04
4,283
3,967
72
244


F2
6
19.24
115.44
2,528
2,205
26
297


L1
4
20.39
81.56
2,130
1,980
24
126




Potential Applications

This dataset is well-suited for:



Gap acceptance behavior studies at roundabouts (e.g., Pascual Anglès et al., 2025)

Traffic flow analysis and modeling

Safety assessments using surrogate safety measures (SSMs)

Validation of traffic simulation models