1,721,007 research outputs found
Behavioral Measures for 48 Participants in Work-Zone Messaging Driving Simulation Study collected in 2016 at the University of Minnesota
No full textThe data files are comma separated values (.csv) files that can be opened in Excel and similar spreadhsheet programs. There are three files, comprising speed and speed variation data, lane deviation data, and eye tracking fixation counts on the messaging interface. For the driving performance variables, CN, CS, AN, AS, AVN, and AVS all refer to Control (PCMS) Northbound (Shoulder Work work zone), Control Southbound (Lane Closure work zone), Audio-only interface northbound, audio-only southbound, audio-visual interface northbound, and audiovisual southbound. Position indicates the placement of the smartphone interface, with 1 reflecting dashboard placement, and 2 reflecting passenger seat placement. Events reflect messaging events in the simulated work zone. Finally, in the eye-tracking dataset, position 1 and 2 is the same as the other data sets, while Interface 1 is control/PCMS, 2 is audio-only, 3 is audio-visual. DriveBound 1 is northbound/shoulderwork, and DriveBound 2 is southbound/laneclosure. TotalN is number of fixations on interface.This study explored the impact of in-vehicle messages to alert drivers to events within a simulated work zone. Participants used a partial motion-based driving simulator made by Realtime Technologies, Inc. Data variables include speed, speed deviation, lane standard deviation, and eye-tracking fixation counts. The data is provided here for reasons of transparency and verification.MnDOT Contract Number 99008, Work Order Number 184Craig, Curtis, M; Morris, Nichole L. (2018). Behavioral Measures for 48 Participants in Work-Zone Messaging Driving Simulation Study collected in 2016 at the University of Minnesota. Retrieved from the University Digital Conservancy, https://doi.org/10.13020/D6610M
Sound Localization Data of 29 Participants in Bicycle Alarm Development Study collected in 2018 at the University of Minnesota
No full textThe headings in the .csv data file represent:
participant: participant number
order: order of sound presentations
trial: trial number
SoundType: b (experimental sound) or c (car horn sound)
Degreeloc: with 0 degrees as directly in front, this represents the degree at which the sound was presented, on a circle. -45 is front left, -90 is left, -135 is left rear, 135 is right rear, 90 is right, 45 is front right.
Degreeresp_loc: this is the same metric as Degreeloc, but indicates the direction of the participant's response to where they thought the sound was coming from.
SourceLocation: The direction of the sound written in directional terms (e.g., front, left rear, etc.)
ResponseLocation: The direction of the participant's response to the sound direction, written in directional terms.
ResponseError: The difference between the degree of location of the sound source, and the degree of the location of the participant's response.
Corrected_ResponseError: This treats all differences reported in ResponseError as the minimum difference in degrees between sound source and response location, with a maximum possible difference of 180 degrees (i.e., opposite direction of sound and response).
ResponseConfidence: participant's reported confidence in the accuracy of their response, on a scale of 0 to 100.
Correct Response: Whether the participant correctly identified the location of the sound, with 0 being incorrect, and 1 being correct.
Incorrect Response: Whether the participant incorrectly identified the location of the sound, with 0 being correct, and 1 being incorrect.This data file represents de-identified raw data from a sound localization experiment with 29 participants. Participants heard an experimental sound (Sound A.wav) or a car horn sound (vehicle045.wav) and had to indicate which direction of the sound. Also included are the sound files used in the study.National Science Foundation (Grant/Award 1631133)Morris, Nichole L; Craig, Curtis M. (2020). Sound Localization Data of 29 Participants in Bicycle Alarm Development Study collected in 2018 at the University of Minnesota. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/a3n7-tz37
Simulated driver performance, error, and acceptance study of a J-turn intersection with 3 levels of signage
No full textCSV file containing study data; CSV file containing data dictionary; ZIP file with PNG files of participant visualizations. Note: This dataset is presented in long form. So, it repeats the J-turn attitude scores (pre_good; pre_willing; pre_community, pre_att_sum, post_good, post_willing, post_community, post_att_sum, att_change) and other demographic variables (age, gender (1F, 2M), city) across the three drives. Analyses of these variables should be collapsed so that the duplicate values are not included in analyses.
Data contains only one drive for Participant 8 and participant 53, and two drives for Participant 65 and Participant 77.Thirty-six participants with limited previous experience and knowledge of J-turn intersections participated in a simulation study to examine their acceptance of J-turns and left turning navigational performance at three simulated J-turn intersections in counterbalanced order, each featuring one of three signage levels (minimum, intermediate, and full). Participants navigational path was visualized and scored for error occurrence by 3 raters/coders. Eleven different error types occurred and they were classified as minor, moderate, or major severity errors. Participants provided demographic information, crash history, and acceptance of J-turn intersections (across three scales) before and after driving through the simulated J-turn intersections. The data has been deidentified and is available to provide a better understanding of common errors from drivers who are experiencing J-turn intersections for the first time and the resultant influence that their error experiences have on their acceptance of the novel intersection design.Minnesota Department of Transportation/Local Road Research Board, Award # 1003325 WO 98Morris, Nichole L; Schwieters, Katelyn R; Tian, Disi; Craig, Curtis M. (2024). Simulated driver performance, error, and acceptance study of a J-turn intersection with 3 levels of signage. Retrieved from the University Digital Conservancy, https://doi.org/10.13020/DNZ4-S946
Identifying and Reconciling Stakeholder Perspectives in Deploying Automated Speed Enforcement
Full text linkSpeeding is a public-health crisis, making up approximately a third of roadway deaths each year in the United States. One countermeasure with clearly documented efficacy to reduce speed is automated speed enforcement (ASE). Public acceptance of ASE, however, has been marginal with many drivers calling into question its need and legality. This project used surveys and interviews to better understand public rejection of ASE and to strategically provide individualized information to determine whether opinions can be shifted toward a more favorable view of ASE. Statistically significant movement on ASE opinion was achieved after respondents engaged with a tailored survey addressing their particular ASE concerns. Those who changed their opinion were more engaged (e.g., considered the opposite of their current stance more fully) and were persuaded by evidence of safety benefits resulting from reduced speeds and effective speed reduction with ASE deployment.Peterson, Colleen; Douma, Frank; Morris, Nichole L.. (2017). Identifying and Reconciling Stakeholder Perspectives in Deploying Automated Speed Enforcement. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/189297
A Pilot Study on Mitigating Run-Off-Road Crashes
No full textRun off the road crashes account for approximately 50% of motor vehicle related fatalities on a national and on a state level. To address this unacceptably high rate of fatalities this pilot project first sought to identify the primary factors associated with run off the road crashes and identify limitations and shortcomings of existing countermeasures. This was accomplished through the development of a taxonomy that summarized existing engineering related and human factors related literature according to infrastructure, environment, and driver related factors that have been found to be most associated with run off the road crash-related fatalities. Based on the taxonomy results a new potentially useful countermeasure was identified that consisted of a haptic and auditory feedback. The pilot project then sought to develop and then evaluate a series of prototype countermeasure systems based on haptic and auditory feedback presented either individually or in parallel. The primary results of the driving environment simulator study in which participants drove through a series of realistic worlds experiencing the countermeasures in response to lane departure events found that the presentation of multiple countermeasure systems can provide increased user satisfaction but can also increase driver response times to critical situations. Secondary results of the study suggest that the haptic countermeasures can provide additional information to drivers but that it may not be interpreted by drivers as expected by designers.Intelligent Transportation Systems Institute
Center for Transportation StudiesEdwards, Christopher; Morris, Nichole L.; Manser, Michael. (2013). A Pilot Study on Mitigating Run-Off-Road Crashes. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/155993
Risk Evaluation for In-Vehicle Sign Information
No full textThe goal of the study was to examine the influence of in-vehicle signing (IVS) pertaining to four types of changing driving conditions and determine the utility and potential safety costs associated with the IVS information. Signage displayed on a personal navigation device was presented for specific zones within the simulation to assist drivers’ preparation for transitioning to new driving conditions ahead. These zones included: speed zone changes within the same roadway, notification of school zones, notification of work zones, and notification of curves. Driving performance measures known to be related to distraction as well as subjective usability and workload measures were used to help identify potential distraction associated with the IVS information. Moreover, risk analysis was conducted to evaluate the safety associated with IVS technology compared to the known safety levels with standard roadside signage. The objective measures collected in this study (both driving performance and risk analysis) indicated that implementing IVS technology would impact driving performance in the following manner:
When IVS was deployed in the absence of external signs, speeding behavior significantly increased relative to baseline levels. IVS technology was not observed to impact speeding behavior when external signs were also present.
Risk analysis suggested that IVS technology (when used in conjunction with external signs) can improve the safety associated with frontal-impact crashes; however, risk analysis proved that safety across all crash types was significantly reduced below baseline levels when IVS was used without external signs. Moreover, subjective usability results reinforced the driving performance findings.Schlicht, Erik J.; Morris, Nichole L.. (2016). Risk Evaluation for In-Vehicle Sign Information. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/182436
Multi-Method Investigation of Pedestrian Safety Impacts of Right-Turn Lanes
No full textThe impact of dedicated right turn lanes at signalized intersections on pedestrian safety has been relatively understudied, particularly for urban areas. The authors reviewed the research literature and pedestrian crash data analysis on right-turning vehicles, performed a field study with both in-person observation and video recordings of sites with dedicated right-turn lanes and right-turn through lanes in Saint Paul, MN, and conducted an urban driving simulation study with participants driving and turning through both simulated lane types. The results indicated that (1) lane count and traffic volume were significantly associated with risk to pedestrians from right-turning vehicles, (2) higher-volume intersections with dedicated right-turn lanes were riskier to pedestrians in terms of yielding rates, (3) the dedicated right-turn lanes at lower-volume sites were safer than their right-turn through-lane counterparts in terms of yielding likelihood, (4) dedicated right-turn lanes were associated with fewer high-speed turns, and (5) right-turn through lanes were associated with wider turns in both the field data and simulation data. Taken together, intersections with dedicated right-turn lanes could pose some risk to pedestrians at higher-volume intersections for stopping rates, while dedicated right-turn lanes were likely safer than right-turn through-lane counterparts at lower-volume intersections in terms of pedestrian safety. Future research should examine these findings with a wider range of traffic volumes and intersection types.Craig, Curtis M.; Morris, Nichole L.. (2025). Multi-Method Investigation of Pedestrian Safety Impacts of Right-Turn Lanes. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/278958
Older Driver Support System Field Operational Test
No full textOlder drivers represent the highest injury and fatality rate per 100 million miles driven. The disproportionate fatality risk is linked to several known factors, ranging from failure to yield to cognitive and visual limitations to seatbelt use abstention to fragility. Through a series of focus groups, usability tests, and a controlled field test, a universally designed smartphone app (called RoadCoach) designed to reduce risky driving behaviors, such as speeding and hard braking, was previously found to have high usability among older drivers. The current research consisted of a field operational test of the app, which examined the baseline driving behavior (3 weeks) of 28 older drivers in Minnesota and Kansas, their driving behavior with RoadCoach feedback (6 weeks), and their driving behavior during a follow-up, no-feedback period (3 weeks). The results demonstrated marginal reductions in speeding behaviors while the app was functioning, but speed behaviors significantly increased after the feedback was discontinued compared to when it was active. Hard braking and stop sign violations were significantly reduced during feedback and post feedback. Finally, satisfaction and trust were high among users, with drivers reporting that the app helped improve their attention and focus on the task of driving.Libby, David A.; Morris, Nichole L.; Craig, Curtis M.. (2019). Older Driver Support System Field Operational Test. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/203524
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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