Browsing by Author "Griffin, Greg P."

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    Exploring Crowdsourced Monitoring Data for Safety
    Turner, Shawn M.; Martin, Michael W.; Griffin, Greg P.; Le, Minh; Das, Subasish; Wang, Ruihong; Dadashova, Bahar; Li, Xiao (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-03)
    This project included four distinct but related exploratory studies of data sources that could improve roadway safety analysis. The first effort evaluated passively gathered crowdsourced bicyclist activity data from StreetLight Data and found promising correlations (R2 of 62% and 69% for monthly weekday and weekend daily averages) when the StreetLight data were compared to bicyclist counts from 32 locations in eight Texas cities, and even better correlation (R2 of 94%) when compared with countywide Strava data expanded to represent total bicycling activity. The second effort evaluated the pedestrian counting accuracy of the Miovision system and found 15% error for daytime and 24% error for nighttime conditions. The third effort used INRIX trip trace data to determine origin-destination patterns and developed 40 decision rules to define the origin-destination patterns. The fourth effort analyzed crowdsourced Waze data (i.e., traffic incidents) and found it to be a reliable alternative to observed and predicted crashes, with the ability to identify high-risk locations: 77% of high-risk locations identified from police-reported crashes were also identified as high-risk in Waze data. The researchers propose a method to treat the redundant Waze reports and to match the unique Waze incidents with police crash reports.
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    Sources and Mitigation of Bias in Big Data for Transportation Safety
    Griffin, Greg P.; Mulhall, Meg; Simek, Christopher L. (SAFE-D: Safety Through Disruption National University Transportation Center, 2018-11)
    Emerging big data resources and practices provide opportunities to improve transportation safety planning and outcomes. However, researchers and practitioners recognize that big data includes biases in who the data represents and accuracy related to transportation safety statistics. This study systematically reviews both the sources of bias and approaches to mitigate bias through review of published studies and interviews with experts. The study includes quantified analysis of topic frequency and evaluation of the reliability of concepts by using two independent trained coders. Results show a need to keep transportation experts and the public central in determining the right goals and metrics to evaluate transportation safety, in the development of new methods to relate big data to the total population’s transportation safety needs, in the use of big data to solve difficult problems, and to work ahead of emerging trends and technologies.
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    Street Noise Relationship to Bicycling Road User Safety
    Griffin, Greg P.; Hankey, Steven C.; Simek, Christopher L.; Le, Huyen; Buehler, Ralph (SAFE-D: Safety Through Disruption National University Transportation Center, 2018-12)
    Vulnerable road users, such as bicyclists, experience road noise directly. This study explored the relationship between bicycle crash risk and street-level road noise as measured in Austin, Texas and the Washington, D.C. metropolitan area, in addition to other factors. Construction and validation of a method to measure noise directly using consumer-accessible tools supports additional studies as well as potential public crowdsourcing applications for urban planning. Results from the two case sites were mixed. Street noise, as measured on our chosen routes, was not a consistent predictor of bicycle crash risk. However, our model explained over 87% of the variation in crash risk in the Washington, D.C. metropolitan area route, considering infrastructure, nearby bicycle commute mode share, and street noise. Findings from the two routes using our modeling approaches are not exhaustive, but rather an initial exploration of these relationships to support further work on the role of street noise in planning for safety.