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Browsing by Author "Martin, Michael W."

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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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    Vehicle Occupants and Driver Behavior: A Novel Data Approach to Assessing Speeding
    Martin, Michael W.; Green, Lisa L.; Shipp, Eva; Chigoy, Byron; Mars, Rahul (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-11)
    The question of whether driver behavior, and speeding in particular, differs based on passenger(s) presence requires the use of large amounts of data, some of which may be difficult to accurately obtain. Traditional methods of obtaining driver behavior information result in datasets that either lack passenger information altogether (i.e., insurance companies using telematics) or rely on rough estimates of passenger age and gender obtained from blurred photos (i.e., naturalistic driving studies like the Second Strategic Highway Research Program). This research project represents a novel, data-driven approach to assessing passenger impact on speeding. Household travel survey demographic information and GPS traces were linked to HERE network speed limit to study the impact of vehicle occupancy on speeding. Survey responses from 11 study areas were cleaned, merged, and ultimately used in developing binomial logistic regression models. Of particular interest were the following driver groups: teenagers, adults driving with child passenger(s), and older drivers. The models suggest that drivers speed less when there is a passenger in the vehicle, particularly adult drivers with a child passenger(s).