%0 Generic %A Reddy, Nagarjun %A Farah, Haneen %D 2025 %T Driving simulator dataset on human driven vehicles' speed compliance under variable speed limits in mixed traffic with automated vehicles %U %R 10.4121/91308654-6467-49c5-be0f-12b056e42ec7.v1 %K Automated vehicles %K Mixed traffic %K Dynamic speed limits %K Connected vehicles %K Variable message signs %X <p>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 connected and automated vehicles (CAVs).</p><p><br></p><p>The driving simulator used is located at the Transport & Planning department of Delft University in the Netherlands. It operates using the SCANeR (v1.9) software by AV Simulation. It is a fixed base driving simulator equipped with a Fanatec steering wheel and pedals, a dashboard mock-up, and three 4 K high-resolution screens which approximately provide 180° vision.</p><p><br></p><p>The dataset consists of 36 participants, over different ages and both males and females.</p><p><br></p><p>The route in the driving simulator consisted of a motorway with a defined speed limit of 100 km/h. On the motorway, drivers also experienced dynamic speed limit sections, where a decreasing speed limit from 100 to 70 to 50, and again back to 100 was applied. A depiction of the route is attached in this dataset information.</p><p><br></p><p>Each driver drove 7 scenarios. The scenarios were defined using two variables: the penetration rate of CAVs (0%, 10% and 40%) and the distance of speed limit information provision to CAVs (300 m, 600 m, and 900 m).</p><p><br></p><p>More details about the experiment set-up itself can be found in the master thesis: http://resolver.tudelft.nl/uuid:b2bf818d-1cc9-40fb-8515-eef9b8eb348a</p><p><br></p><p><strong>Attached files:</strong></p><p>Processed dataset</p><p>ReadMe file</p><p>Data processing scripts</p><p><br></p><p><strong>File formats:</strong></p><p>Data /.csv</p><p>Data /.xlsx</p><p>Jupyternotebooks /.ipynb</p> %I 4TU.ResearchData