TY - DATA
T1 - Data underlying the master thesis: Predicting cycling risk at intersections with natural cycling data for speed-controlled e-bikes
PY - 2022/12/16
AU - Daniël Landré
AU - Jason Moore
UR - https://data.4tu.nl/articles/dataset/Data_underlying_the_master_thesis_Predicting_cycling_risk_at_intersections_with_natural_cycling_data_for_speed-controlled_e-bikes/21736967/1
DO - 10.4121/21736967.v1
KW - E-bikes
KW - Cyclist behavior
KW - Traffic accidents
KW - natural data set
KW - IoT data
KW - intersection crashes
N2 - <p>This dataset contains data that was gathered during a master's thesis project. </p>
<p>During the project, e-bike cycling data was gathered during a natural cycling experiment. Five women and five men aged 20 to 30 years were asked to participate in the experiment. Participants are asked to cycle according to how they would cycle in their daily life.</p>
<p>The experiment consists of two phases. First, participants are instructed to cycle to a random destination in Delft. In the second phase, participants are instructed to cycle two predetermined routes, each time returning to the starting location of the experiment. This results in around 40-45 minutes of cycling data per participant.</p>
<p>The sensor setup of the experiment is tailored toward collecting data that is similar to the data collection capabilities of modern IoT modules carried by e-bikes. </p>
<p>Data was recorded by various sensors (a high-frequency GNSS antenna, IMU data and power pedals) and correlated against detailed open-access traffic accident data.  </p>
<p>In this dataset, you will find the raw sensor data, processed sensor data, and script used to process the cycling data.</p>
ER -