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.

Bicycle use is increasing in many parts of the U.S. Local and regional governments have set ambitious bicycle mode share goals as part of their strategy to curb greenhouse gas emissions and relieve traffic congestion. In particular, Portland, Oregon has set a 25% mode share goal for 2030 (PBOT 2010). Currently bicycle mode share in Portland is 6.1% of all trips. Other cities and regional planning organizations are also setting ambitious bicycle mode share goals and increasing bicycle facilities and programs to encourage bicycling. Increases in bicycle mode share are being encouraged to increase. However, cities with higher-than-average bicycle mode share are beginning to experience locations with bicycle traffic congestion, especially during peak commute hours. Today, there are no established methods are used to describe or measure bicycle traffic flows. In the 1960s, the Highway Capacity Manual (HCM) introduced Level of Service (LOS) measurements to describe traffic flow and capacity of motor vehicles on highways using an A-to-F grading system; "A" describes free flow traffic with no maneuvering constraints for the driver and an "F" grade corresponds to over capacity situations in which traffic flow breaks down or becomes "jammed". LOS metrics were expanded to highway and road facilities, operations and design. In the 1990s, the HCM introduced LOS measurements for transit, pedestrians, and bicycles. Today, there are many well established and emerging bicycle level of service (BLOS) methods that measure the stress, comfort and perception of safety of bicycle facilities. However, it was been assumed that bicycle traffic volumes are low and do not warrant the use of a LOS measure for bicycle capacity and traffic flow. There are few BLOS methods that take bicycle flow into consideration, except for in the case of separated bicycle and bicycle-pedestrian paths. This thesis investigated the state of BLOS capacity methods that use bicycle volumes as a variable. The existing methods were applied to bicycle facility elements along a corridor that experiences high bicycle volumes in Portland, Oregon. Using data from the study corridor, BLOS was calculated and a sensitivity analysis was applied to each of the methods to determine how sensitive the models are to each of the variables used. An intercept survey was conducted to compare the BLOS capacity scores calculated for the corridor with the users' perception. In addition, 2030 bicycle mode share for the study corridor was estimated and the implications of increased future bicycle congestion were discussed. Gaps in the BLOS methods, limitations of the thesis study and future research were summarized. In general, the existing methods for BLOS capacity are intended for separated paths; they are not appropriate for existing high traffic flow facilities. Most of the BLOS traffic flow methods that have been developed are most sensitive to bicycle volumes. Some of these models may be a good starting point to improve BLOS capacity and traffic flow measures for high bicycle volume locations. Without the tools to measure and evaluate the patterns of bicycle capacity and traffic flow, it will be difficult to monitor and mitigate bicycle congestion and to plan for efficient bicycle facilities in the future. This report concludes that it is now time to develop new BLOS capacity measures that address bicycle traffic flow.