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.
| Identifer | oai:union.ndltd.org:pdx.edu/oai:pdxscholar.library.pdx.edu:open_access_etds-3223 |
| Date | 30 March 2015 |
| Creators | Thompson, Samson Ray Riley |
| Publisher | PDXScholar |
| Source Sets | Portland State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations and Theses |
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