Portland State University

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Abstract: Traffic counts are an important piece of information used by transportation planners; however, while count programs are common for motor vehicles most efforts at counting non-motorized traffic – cyclists and pedestrians – are minimal. Long-term, continuous counts of non-motorized traffic can be used to estimate month of year and day of week adjustment factors that can be used to scale short-duration counts to estimates of annual average daily traffic. Here we present results from continuous counts of non-motorized traffic at 6 locations on off-street trails in Minneapolis, MN using two types of automated counters (active infrared and inductive loop detectors). We found that traffic volumes varied significantly by location, but the month of year and day of week patterns were mostly consistent across locations and mode (i.e., cycling, walking, or mixed mode). We give examples of how this information could be used to extrapolate short-duration counts to estimates of annual average daily traffic as well as Bicycle Miles Traveled (BMT) and Pedestrian Miles Traveled (PMT) for defined lengths of off-street trails. More research is needed to determine if non-motorized traffic patterns (and subsequently our adjustment factors) for off-street...

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ORcycle is a new smartphone application (for both Android and iOS) developed by Transportation, Technology, and People (TTP) lab researchers at Portland State University as part of an Oregon Department of Transportation (ODOT) research project. ORcycle collects user, route, infrastructure, crash, and safety data. ORcycle was successfully launched in early November 2014 and presents many improvements over existing or similar apps. Initial data findings and insights will be presented. Lessons learned as well as opportunities and challenges associated with smartphone data collection methods will be discussed. More information about the app can be found here: http://www.pdx.edu/transportation-lab/orcycle

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Summary: Researchers from the transportation, planning and health fields share the common goal of promoting physically active lifestyle. One challenge that researchers often face is the measurement of physical activity, particularly among children. This is because the sporadic nature of children’s physical activity patterns makes it difficult to recall and quantify such activities. Additionally, children’s lower cognitive functioning compared to adults prevents them from accurately recalling their activities. This presentation will describe the design and application of a novel self-report instrument - the Graphs for Recalling Activity Time (GReAT) - for measuring children’s activity time use patterns. The instrument was applied in a study of children’s risk for obesity and diabetes in a predominately Hispanic community in Milwaukee, WI. Time-use data for two weekdays and one weekend day were collected for various physical and sedentary activities. The data was then assessed against measurements of the children’s cardiovascular fitness, weight status and insulin resistance through exploratory analysis and structured equation modeling. Findings on GReAT’s reliability and new evidence on the impacts of time-use in different activities on children’s risk for...