22 & 23 May 2025: Mini-conference on Open and FAIR in Natural and Engineering Sciences. Register to attend.

Driving simulator dataset on human driven vehicles' gap acceptance behaviour in mixed traffic with automated vehicles

DOI:10.4121/28149b45-62fd-44f6-9c8a-fbc14a5d645c.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/28149b45-62fd-44f6-9c8a-fbc14a5d645c

Datacite citation style

Reddy, Nagarjun; Haneen Farah; Hoogendoorn, S.P.(Serge) (2025): Driving simulator dataset on human driven vehicles' gap acceptance behaviour in mixed traffic with automated vehicles. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/28149b45-62fd-44f6-9c8a-fbc14a5d645c.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This research is based on data gathered in 2020 at Delft University of Technology. This dataset is from a driving simulator experiment, whose goal was to study human drivers' behaviour in mixed traffic, which has both human-driven vehicles and automated vehicles (AVs).


The dataset consists of 95 participants of which 71 (74.7 %) were male and 24 females.


The route in the driving simulator consisted of several motorway sections, provincial (regional) road sections, and three priority T-intersections. Each T-intersection consisted of an urban road (the minor road) intersecting with a provincial road (the major road). The defined speed limit was 100 km/h on the motorway, 80 km/h on the provincial roads, and 50 km/h on urban roads. On the motorway, drivers also experienced dynamic speed limit sections. A depiction of the route is attached in this dataset information.


The experiment design aimed to separately observe the effects of AVs’ recognizability and their driving style on human driving behavior as well as their combined effects. Each participant drove four scenarios, excluding a familiarization drive. The scenarios differed in two aspects: the recognizability and the driving style of AVs.

Two variables primarily varied in the experiment: the driving style of AVs, and their recognizability. The participants were assigned randomly to one of three groups: Defensive AVs, Aggressive AVs, and Mixed AVs. The group determined the driving style of AVs that a participant encountered in the experiment.


More details about the experiment set-up itself can be found in our paper published: https://doi.org/10.1016/j.trf.2022.09.018


Attached files:

Processed dataset

ReadMe file

Data processing scripts


File formats:

Data /.csv

Data /.xlsx

Jupyternotebooks /.ipynb

History

  • 2025-03-13 first online, published, posted

Publisher

4TU.ResearchData

Format

Data /.csv Data /.xlsx Jupyternotebooks /.ipynb

Funding

  • Safe and efficient operation of AutoMated and human drivEN vehicles in mixed traffic (grant code 17187) [more info...] Applied and Technical Sciences (TTW), a subdomain of the Dutch Institute for Scientific Research (NWO)

Organizations

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Transport and Planning, Traffic Systems Engineering

DATA

Files (2)