This research explored (1) the relationship between suburban vehicle operating speed and roadway characteristics, especially the presence of bicyclists and (2) whether crowdsourced speed data could be used to estimate the unconstrained speed for a location. Both vehicle volume per lane and bicycle volume were found to be influential in affecting average speed on lower speed urban arterial roadways. For 40.3 km/hr (25 mph) sites, an increase of 19 vehicles per 15-min period would decrease average speed by 1.6 km/hr (1 mph), and an increase of more than 39 bicyclists per 15-min period would decrease average vehicle speed by a similar amount. Because of the limited number of 15-min periods with bicycle counts greater than 1, the research team also developed a model using all available 15-min periods with on-road speed data. Speed and volume data in 15-min increments for 2 weeks at nine sites were obtained using on-road tubes and via a vendor of crowdsourced speed data. The difference between the tube data and the crowdsourced data was calculated and called TMCS as a representation of tube (T) minus (M) crowdsourced (CS). The geometric variables that had the greatest influence on TMCS were the number of signals and the number of driveways within a corridor. When only including non-congested periods, weekends (Saturday or Sunday) were associated with the smallest TMCS.
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.
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.