TRB Highlights: 3 PSU Student Presentations

Event Date: 
Friday, January 17, 2014, 12:00pm to 1:00pm PST
Katherine E. Bell, Adam Moore and Liang Ma, Portland State University

Watch video

View slides: Bell Presentation (PDF)

Moore Presentation (PDF)

Ma Presentation (PDF)

Identification and Characterization of PM2.5 and VOC Hot Spots on Arterial Corridor by Integrating Probe Vehicle, Traffic, and Land Use Data: The purpose of this study is to explore the use of integrated probe vehicle, traffic and land use data to identify and characterize fine particulate matter (PM2.5) and volatile organic compound (VOC) hot spot locations on urban arterial corridors. An emission hot spot is defined as a fixed location along a corridor in which the mean pollutant concentrations are consistently above the 85th percentile of pollutant concentrations when considering all other locations along the corridor during the same time period. In order to collect data for this study, an electric vehicle was equipped with instruments designed to measure PM2.5 and VOC concentrations. Second-by-second measurements were performed for each pollutant from both the right and left sides of the vehicle. Detailed meteorological, traffic and land use data is also available for this research. The results of a statistical analysis are used to better understand which data sources are most valuable in estimating PM2.5 and VOC hot spot locations consistent with empirical data, as well as which variables have the greatest impact on emissions and pollutant levels at a microscale level. This research highlights the importance of considering both consistency and peak emission levels when identifying hot spot locations. An objective of this research is to develop a method to identify urban arterial hot spot locations that provides a balance of efficiency (in terms of capital expenses, time, resources, expertise requirements, etc.) and accuracy.

Modeling Impact of Traffic Conditions on Variability of Midblock Roadside Fine Particulate Matter Concentrations on an Urban Arterial: This paper presents an innovative modeling of fine particulate matter (PM2.5) concentrations as a function of very high resolution meteorological and traffic data. Peak period measurements were taken at a mid-block roadside location on an urban arterial commuter roadway. To capture the impact of dynamic traffic conditions, data were analyzed at 10-second intervals, with substantially higher resolution than typical roadside air quality study designs. Particular attention was paid to changes in traffic conditions, including fleet mix, queuing and vehicle platooning over the course of the study period, and the effect of these changes on PM2.5. Significant correlations were observed between vehicle platoons and increases in PM2.5 concentrations. Traffic state analysis was employed to determine median PM2.5 levels before and after the onset of congestion. A multivariate regression model was estimated to determine significant PM2.5 predictors while controlling for autocorrelation. Significance was found not only in the simultaneous traffic variables but also in lagged traffic variables; additionally, the effects of vehicle types and wind direction were quantified. Modeling results indicate that traffic conditions and vehicle type do have a significant impact on roadside PM2.5 concentrations. For instance, the addition of one heavy vehicle was shown to increase PM2.5 concentrations by 2.45% when wind blew across the roadway before reaching the monitoring location. This study serves as a demonstration of the abilities of very high resolution data to identify the effects of relatively minute changes in traffic conditions on air pollutant concentrations.

Effects of the Objective and Perceived Built Environment on Bicycling for Transportation: 
This paper investigates the relative effects of the objectively-measured built environment versus stated perceptions of the built-environment on bicycling. Data are from a random phone survey conducted in the Portland, Oregon region. Binary logit and linear regression models, using objective measures, perceived measures, and both sets of measures, were estimated to predict odds of bicycling and frequency of bicycling separately. Results showed that the perceived environment and objective environment had independent effects on bicycling. This suggests that future bicycling research should include both perceived and objective measures of the built environment when possible. In addition, it indicates that interventions that focus on changing perceptions of the environment may be as important as actual changes in the built environment. The objective environment was necessary but not sufficient for bicycling. Intervention programs to improve people’s perceptions of the environment may be necessary to reap the full potential of planning and design policies.  The results also suggest that it is useful to predict odds of bicycling and bicycling frequency separately, as the predictors of each behavior do vary. Finally, the analysis confirms the importance of attitudes in predicting behavior.