Bicycle transportation has become a central priority of urban areas invested in improving sustainability, livability, and public health outcomes. Transportation agencies are striving to increase the comfort of their bicycle networks to improve the experience of existing cyclists and to attract new cyclists. The Oregon Department of Transportation sponsored the development of ORcycle, a smartphone application designed to collect cyclist travel, comfort, and safety information throughout Oregon. The sample resulting from the initial deployment of the application between November 2014 and March 2015 is described and analyzed within this thesis. 616 bicycle trips from 148 unique users were geo-matched to the Portland metropolitan area bicycle and street network, and the self-reported comfort level of these trips was modeled as a function of user supplied survey responses, temporal characteristics, bicycle facility/street typology, traffic volume, traffic speed, topography, and weather. Cumulative logistic regression models were utilized to quantify how these variables were related to route comfort level within separate variable groups, and then the variables were used in a pooled regression model specified by backwards stepwise selection. The results of these analyses indicated that many of the supplied predictors had significant relationships with route comfort. In particular, bicycle miles traveled on facilities with higher traffic volumes, higher posted speeds, steep grades, and less separation between bicycles and motor vehicles coincided with lower cyclist comfort ratings. User supplied survey responses were also significant, and had a greater overall model variance contribution than objectively measured facility variables. These results align with literature that indicates that built environment variables are important in predicting bicyclist comfort, but user variables may be more important in terms of the variance accounted for. This research outlines unique analysis methods by which future researchers and transportation planners may explore crowdsourced data, and presents the first exploration of bicyclist comfort perception data crowdsourced using a smartphone application.

This study aims to explore the relationship between the objective (actual) environment and people's perceptions of the environment, and their relative effects on active travel behavior, particularly bicycling behavior. This is an important research gap in the current literature linking the built environment and active travel. Better understanding this relationship will help to explore the mechanism underlying the built environment- behavior relationship and identify potential interventions to promote active travel. Relying on the data from Portland, OR, this study investigated the following four research questions: (1) How does the objectively measured environment correspond to the perceived environment? And what factors contribute to the mismatch between the objective and perceived environment? (2) What are the different effects of the perceived and objective environment on active travel behavior? (3) Do perceptions mediate the effects of the objective environment on active travel behavior? (4) Do changes in the built environment change perceptions, and in turn change travel behavior? Through various statistical methods, this study found that there was a mismatch between perceptions and objectively measured environment, and such factors as socio-demographics, attitudes, social environment, and behavior could contribute to this mismatch. This study also found the perceived environment and objective environment had independent effects on bicycling. Further, this study found the objectively measured bicycling environment had only an indirect effect on bicycling behavior through influencing one's perceptions of the environment. Finally, this study found changes in the actual built environment may change the perceptions of the walking environment, but not the perceptions of the bicycling environment, at least in the short term.

While bicyclists and other active travelers obtain health benefits from increased physical activity, they also risk uptake of traffic-related air pollution. But pollution uptake by urban bicyclists is not well understood due to a lack of direct measurements and insufficient analysis of the determinants of exposure and ventilation (breathing). This knowledge gap impedes pollution-conscious transportation planning, design, and health impact assessment. The research presented in this dissertation generates new connections between transportation system characteristics and pollution uptake by bicyclists. The primary research questions are: 1) how do urban bicyclists' intake and uptake of air pollution vary with roadway and travel characteristics and 2) to what extent can transportation-related strategies reduce uptake. Breath biomarkers are used to measure absorbed doses of volatile organic compounds (VOCs). This research is the first application of breath biomarkers to travelers and the first uptake measurements of any pollutant to include roadway-level covariates. Novel methods to collect and integrate bicycle, rider, traffic, and environmental data are also introduced. Bicyclist exposure concentrations, exhaled breath concentrations, respiratory physiology, and travel characteristics were collected on a wide range of facilities in Portland, Oregon. High-resolution trajectory and pollution data were then integrated with roadway and traffic data. Models of exposure, ventilation, and uptake of VOCs were estimated from the on-road data. Important new quantifications in the models include the effects of average daily traffic (ADT) on multi-pollutant exposure, the lagged effect of on-road workload on ventilation, and the effects of exposure and ventilation on absorbed VOCs. Estimated models are applied to situations of interest to travelers and transportation professionals. Sample applications include the inhalation dose effects of road grade, cruising speed choice, stops, and detouring to parallel low-traffic facilities. In addition, dose-minimizing routing behavior is compared with revealed routing preferences in the literature. Finally, findings from this research and the literature are distilled so that they can be incorporated into bicycle network design guidelines.

For a number of reasons--congestion, public health, greenhouse gas emissions, energy use, demographic shifts, and community livability to name a few--the importance of walking and bicycling as transportation options will only continue to increase. Currently, policy interest and infrastructure funding for nonmotorized modes far outstrip our ability to model bike and walk travel. To ensure scarce resources are used most effectively, accurate models sensitive to key policy variables are needed to support long-range planning and project evaluation, and to continue adding to our growing understanding of key factors driving walk and bike behavior. This research attempts to synthesize and advance the state of the art in trip-based, nonmotorized mode choice modeling. Over the past fifteen years, efforts to model the decision to walk or bike on a given trip have been hampered by the lack of a comprehensive behavioral framework and inconsistency in measurement scales and model specification. This project develops a mode choice behavioral framework that acknowledges the importance of attributes along the specific walk and bike routes that travelers are likely to consider, in addition to more traditional area-based measures of travel environments. The proposed framework is applied to a revealed preference, GPS-based travel dataset collected from 2010-2013 in Portland, Oregon. Measurement of nonmotorized trip distance, built environment, tour-level variables, and attitudinal attributes as well as mode availability are explicitly addressed. Route and mode choice models are specified using discrete choice techniques, and predicted walking and bicycling routes are tested as inputs to various mode choice models. Results suggest strong potential for predicted route measures to enhance walk and bicycle mode choice modeling. Findings also support the specific notion that bicycle and pedestrian infrastructure contribute not only to route choice but also to the choice of whether to bike or walk. For decisions to bicycle, availability of low-traffic routes may be particularly important to women. Model results further indicate that land use and built environments around trip ends and a person’s home still have important effects on nonmotorized travel when controlling for route quality. Both route and area travel environment impacts are mostly robust to the inclusion of residential self-selection variables, consistent with the idea that built environment differences matter even for households that choose to live in a walkable or bikeable neighborhood. The combination of area and route-based built environment measures alongside trip context, sociodemographic, and attitudinal attributes provides a new perspective on nonmotorized travel behavior relevant to both policy and practice.

This project examined cyclist red light running behavior using two data sets. Previous studies of cyclist compliance have investigated the tendencies of cyclists to run red lights on the whole by generalizing different maneuvers to their end outcome, running a red light. This project differentiates between the different types of red light running and focuses on the most egregious case, gap acceptance, which is when a cyclist runs a red light by accepting a gap in opposing traffic. Using video data, a mathematical model of cyclist red light running was developed for gap acceptance. Similar to other studies, this analysis utilized only information about the cyclist, intersection, and scenario that can be outwardly observed. This analysis found that the number of cyclists already waiting at the signal, the presence of a vehicle in the adjacent lane, and female sex were deterrents to red light running. Conversely, certain types of signal phasing, witnessing a violation, and lack of helmet increased the odds that a cyclist would run the red light. Interestingly, while women in general are less likely to run a red light, those who witnessed a violation were even more prone that men who had witnessed a violation to follow suit and run the red light themselves. It is likely that the differing socialization of women and men leads to different effects of witnessing a previous violator. The analysis also confirmed that a small subset of cyclists, similar to that found in the general population, are more prone to traffic violations. These cyclists are more willing to engage in multiple biking-related risk factors that include not wearing a helmet and running red lights. Although the model has definite explanatory power regarding decisions of cyclist compliance, much of the variance in the compliance choices of the sample is left unexplained. This points toward the influence of other, not outwardly observable variables on the decision to run a red light. Analysis of survey data from cyclists further confirms that individual characteristics not visible to the observer interact with intersection, scenario, and visible cyclist characteristics to result in a decision to comply (or not) with a traffic signal. Furthermore, cyclist characteristics, in general, and unobservable individual characteristics, specifically, play a larger role in compliance decisions as the number of compliance-inducing intersection traits (e.g. conflicting traffic volume) decrease. One such unobservable trait is the regard for the law by some cyclists, which becomes a more important determinant of compliance at simpler intersections. Cyclists were also shown to choose non-compliance if they questioned the validity of the red indication for them, as cyclists. The video and survey data have some comparable findings. For instance, the relationship of age to compliance was explored in both data analyses. Age was not found to be a significant predictor of non-compliance in the video data analysis while it was negatively correlated with stated non-compliance for two of the survey intersections. Gender, while having significant effects on non-compliance in the video dataset, did not emerge as an important factor in the stated non-compliance of survey takers. Helmet use had a consistent relationship with compliance between the video and survey datasets. Helmet use was positively associated with compliance in the video data and negatively associated with revealed non-compliance at two of the survey intersections. When coupled with the positive association between normlessness and stated willingness to run a red light, the relationship between helmet use and compliance solidifies the notion that a class of cyclists is more likely to consistently violate signals. It points towards a link between red light running and individuals who do not adhere to social norms and policies as strictly as others. Variables representing cyclists and motorists waiting at the signal were positively related to signal compliance in the video data. While an increased number of cyclists may be a physical deterrent to red light running, part of the influence on compliance that this variable and the variable representing the presence of a vehicle may be due to accountability of cyclists to other road users. This relationship, however, was not revealed in the stated non-compliance data from the survey. Efforts to increase cyclist compliance may not be worth a jurisdiction's resources since nearly 90% of cyclists in the video data were already compliant. If a problem intersection does warrant intervention, different methods of ensuring bicyclist compliance are warranted depending on the intersection characteristics. An alternative solution is to consider the applicability of traffic laws (originally designed for cars) to bicyclists. Creating separation in how laws affect motorists and cyclists might be a better solution for overly simple types of intersections where cyclists have fewer conflicts, better visibility, etc. than motorists. Education or other messaging aimed at cyclists about compliance is another strategy to increase compliance. Since cyclists appear to feel more justified in running red lights at low-volume, simple-looking intersections, it would probably be prudent to target messaging at these types of intersections. Many cyclists are deterred by high-volume and/or complicated looking intersections for safety reasons. Reminding cyclists of the potential dangers at other intersections may be a successful messaging strategy. Alternatively, reminding cyclists that it is still illegal to run a red light even if they feel safe doing so may be prudent. Additionally, messaging about the purpose of infrastructure such as bicycle-specific signals or lights that indicate detection at a signal may convince cyclists that stopping at the signal is in their best interest and that the wait will be minimal and/or warranted.