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.
For individuals that are visually impaired, access to safe and reliable transportation can be a significant challenge. The limited menu of mobility options can culminate in a reduced quality of life and more difficulty accessing housing and employment, relative to sighted individuals. Transportation network companies (TNCs, or ridesharing companies) have emerged as a new mode of travel that has the potential to increase access to transportation for the visually impaired. The opportunities and challenges for TNC use by individuals with blindness or visual impairment has not been widely studied. The goal of this research is to use both qualitative and quantitative methods to identify how this community perceives the safety of TNCs relative to other travel modes, and how they utilize TNCs for safe travel. The findings suggest that TNCs are used by a significant proportion of this population. The findings also suggest that one’s experience (or lack thereof) with TNC use has a strong influence on the safety perceptions of this new mode of travel. Finally, while TNCs present an opportunity for riders that are visually impaired to become more engaged in myriad activities, there are still areas in which ridesharing companies can make improvements.
According to the United States Department of Commerce, careers in science, technology, engineering, and mathematics (STEM) are growing faster than occupations in other areas. However, in-class academic concepts can seem abstract with little relevance to a student’s life. There is therefore a need for in-class curricula that links academic concepts with real-world STEM applications.
Over the past 10 years, Texas A&M Transportation Institute (TTI) researchers have developed many educational activities for elementary and middle school students (K–8)
that provide an opportunity to gain hands-on experience and insight into what transportation engineering and other STEM careers have to offer. In 2011, a TTI researcher taught approximately 300 fifth graders about the scientific principles of reflection, refraction, and retroreflectivity through a brief history of sign sheeting, handson activities, and a laboratory exercise. While these activities successfully engaged the students, it is not possible for one researcher to visit the numerous K–12 classrooms in their area, much less on a state- or nation-wide level. Therefore, TTI researchers created a curriculum and associated materials that can be used by teachers and other professionals to connect real-world applications in transportation to academic concepts to enhance the STEM learning experience for students.
