DOI: 10.7910/dvn/xwcuij
title: Impact of Bicycle Rolling Stop Laws on Safety-Relevant Behaviors in the Pacific Northwest

description: Interview data obtained by Gonzaga University students takes the following two forms:
1) Raw interview data are provided in a zip file with the 17 text file transcripts of interviews with personally identifiable information removed. An interview script and a summary file indicating the stakeholder group and state of residence of the experts is also included in the zip file
2) Processed interview data includes a zip file with the output from the mixed method analysis of transcripts in Dedoose software. The excel files include number and percentage of interview text coded to the defined themes.
The survey data were compiled by the University of Idaho with assistance from Qualtrics, an experience management company. Prior to sharing these results, Qualtrics conducted a quality control check so it is assumed that any inconsistent data have already been removed. The data file provides the entirety of the survey responses in a spreadsheet format.
Four different data types were obtained from Oregon State Driving and Bicycling Simulator Laboratory for purpose of this report and they are as follow:
1) Speed data from the Passenger Car Driving Simulator consists of subject number, average speed, and all the independent variables. Speed data were collected based on the driver’s speed while driving through a certain scenario (total of 16). For each scenario, the average speed (mph) of 29 drivers were recorded at every intersection (10 ft upstream of the stop sign).
2) Speed data from the Bicycling Simulator consists of subject number, average speed, and all the independent variables. Speed data were collected based on the bicyclist’s speed while driving through a certain scenario (total of 16). For each scenario, the average speed (mph) of 30 bicyclists were recorded at every intersection (20 ft upstream of the stop sign).
3) Eye tracking data of bicyclists consists of subject number, total fixation duration (TFD) in milliseconds, area of interest (AOI), and all the independent variables. TFD data were collected while a bicyclist maneuvers through a certain scenario (total of 16). For each scenario, the TFD for each AOI was recorded for 21 subjects at every intersection (along 100 ft upstream of the stop sign). AOI represent the area of interest that a driver fixates for a certain of time to generate the total fixation duration.
4) Eye tracking data of bicyclists consists of subject number, GSR in peaks per minute, and all the independent variables. GSR data were collected while a bicyclist maneuvers through a certain scenario (total of 16). For each scenario, the peaks per minute data was recorded for 22 subjects at every intersection (along 100 ft upstream of the stop sign). Peaks per minute represents the emotional arousal (i.e., something is scary, threating, joyful, etc.) that a driver generates when reacting to a particular event.
80 participants were recruited, 40 for the bicycling simulator and 39 for the driving simulator. Simulator sickness affected 9 participants and equipment failure affected 11 participants, so they were excluded from the data and the analysis, bringing the bicycling and car simulator participant number to 59 for full analysis. There are no quality or consistency issues with this data set. The average values were calculated to apply robust statistical analysis for such data (speed). As the experiment consists of 4x2x2 factorial design, each participant had to driver or ride through 16 scenarios; therefore, approximately 480 scenario observations for each simulator were obtained and recorded in the excel files.

The interview data was collected between February 10th and March 17th, 2021.
The survey data were collected between July 2nd and July 8th, 2021. 
The heavy vehicle driving simulator data was collected between Mar 29th and April 14th, 2022.
description: http://hdl.handle.net/1773/49799