This thesis presents the results of an investigation into traffic operations and driver behaviour at traffic circles under South African conditions. The scarcity of local traffic circles necessitated the development of a simulation program (TRACSIM) to assist in the research process. This microscopic program for single lane circles is based on event updates and was calibrated and validated based on local data. Because the acceptance of gaps is such a vital part of the operation of a traffic circle, it was examined in detail. Specific attention was given to the possible use of a gap acceptance model based on variables other than time. Since the gap acceptance process also depends on the gap distribution in the circulating stream, the effect of the origin-destination pattern was also investigated. Two existing analysis techniques are evaluated and verified for local conditions, improving them where possible. Generally these techniques under-estimate traffic delay at local circles. Observations indicate a difference between the acceptance of gaps/lags in the entering and circulating stream of conflicting traffic as well as a difference between critical gaps and critical lags. The mean observed critical gaps/lags are larger than in other countries, which indicates that delays at local circles will be greater. Gap/lag acceptance based on critical distances rather than critical times was applied successfully in the simulation program TRACSIM. A method is proposed to estimate critical distances from the geometric layout of the circle. Critical gaps are not fixed, but should vary with at least the conflicting flows. The investigation of the effect of unbalanced flows on delay, showed that the variability in drivers' critical gaps is more a function of delay than of conflicting flow. Entry delays increase because of an increase in conflicting flows or because of an unfavourable imbalance of conflicting flows. In both instances the drivers' critical gaps will decrease. A variable critical gap model only based on conflicting flows will show no change in the drivers' critical gaps if the conflicting volumes remain constant, even though the actual average delay might increase because of an unfavourable imbalance in conflicting flows. / Thesis (Ph.D.)-University of Natal, Durban, 1997.

Thesis (M.S.)--Florida State University, 2006. / Advisor: James R. Simpson, Florida State University, College of Engineering, Dept. of Industrial Engineering. Title and description from dissertation home page (viewed Sept. 22, 2006). Document formatted into pages; contains ix, 95 pages. Includes bibliographical references.

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.

The safety at half-signalized intersections in Portland, Oregon is analyzed in this thesis using 10 years of crash history and analysis of video that was collected at a subset of intersections. A half-signalized intersection has a standard red-yellow-green traffic signal for automobiles on the major road, a stop sign for motorists on the minor road, and a pedestrian signal with actuation for pedestrians and/or bicyclists on the minor road. Although prevalent in Canada, this type of intersection control is not typically found in the United States because the MUTCD explicitly prohibits its use. Half-signal use is limited mostly to two cities in the Pacific Northwest. In Portland, Oregon there are forty-seven intersections where half-signals are used but the last installation was in 1986; Seattle has over 100 intersections with half-signals and installs these in new locations where warranted. To explore the safety records of these intersections in Portland, crash data from 2002-2011 was analyzed. A total of 442 crashes over the ten-year period at half-signals were observed. Sixteen of these 442 crashes involved pedestrians. In the crashes involving pedestrians, significant differences were found between the approach street of the vehicle and whether the pedestrian or driver was at fault. In the crash error reports, it was found that significantly more of the crashes involving pedestrians were the fault of motorists departing from the minor road who collided with pedestrians crossing the major street. Further crash analysis at half-signals was performed by developing matched comparison groups of minor stop controlled and fully signalized intersections. Crash rates were 0.158 and 0.178 crashes per million entering vehicles for 3-leg and 4-leg half-signals and these rates did not differ significantly from the minor street stop controlled and signalized comparison groups. Results from the matched comparison showed that the half-signalized group had more rear-end crashes when compared with the minor stop controlled group. This was the only result that held significance when crash rates were considered. It was also observed that the minor stop controlled group had a higher proportion of angle crashes when compared with the half-signal group but this did not influence the crash severity. Pedestrian crashes were more prevalent in the half-signal group when compared with the fully-signalized group. Pedestrian volumes were not available which would be used to determine if this significant measure is a result of higher pedestrian use at half-signals. In addition to crash analysis, video was captured at five half-signalized intersections totaling 180 hours. Traffic volumes, pedestrian and bicycle volumes, and signal actuations were collected over a twenty-four hour period. Over this twenty-four hour period the five intersections averaged daily counts of 18613 vehicles on the major street, 591 vehicles on the minor street, 263 pedestrians crossing the major street, 285 pedestrians crossing the minor street, 52 bicycles on the major street, 37 bicycles on the minor street, and 126 signal actuations. Twenty-four hour observations from each of the intersections were used to study conflicts and compliance. No conflicts were observed that reflect the left-turning from the minor street pedestrian crashes that were identified in the crash history. Compliance of the half-signal by vehicles and pedestrians was comparable to compliance at fully-signalized intersections found in other studies with one exception. Across the intersections where video was collected, consisting of four 4-leg intersections and one 3-leg intersection, seven left turn on red violations were observed which had a significant impact on the time after red that red light violations were made. It is hypothesized that at half-signals vehicles on the major street make a left turn on the red signal very late into the red phase because there is not a risk of colliding with a vehicle traveling on the minor street since traffic volumes on the minor street are comparably low. The observed left turn on red violations did not put pedestrians at risk since by that point into the signal pedestrians were already clear of the intersection. Finally, a stop compliance logistic regression model was developed at four four-leg intersections to see what factors had an effect on minor street vehicle stop compliance. All 166 hours of video were used to observe vehicles that arrived at the half-signal during the pedestrian phase. The dependent variable collected was whether a vehicle came to an acceptable stop. Independent variables collected included the vehicle's queue position, if it was the peak school period, if there was a vehicle across the street on the minor road, if a vehicle was stopped at the signal on the major street, if a pedestrian was present when the vehicle arrived, and the movement that the vehicle made from the minor street. Independent variables used in the model included the vehicle's queue position, if a vehicle was stopped at the signal on the major street, if a pedestrian was present, and if the vehicle made a right turn at the signal. Pedestrian presence and right turning vehicles had a positive impact on stop compliance. Vehicles being further back in the queue and cars stopped at the signal on the major street had a negative impact on stop sign compliance. In the model, pedestrian presence had the largest positive impact on stop compliance. When pedestrians were present, a motorist on the minor street was four times more likely to stop at the sign.

Posting advisory speed signs at sharp horizontal curve sites is a practice well established in the United States. The purpose of these signs is to provide the driving public with a safe speed to negotiate such curves; however, the link between these signs and safety has not yet been clearly established. The first manuscript in this dissertation presents an effort to model safety as it relates to curve advisory speed signs. It proposes a statistical model relating crash frequency at 2-lane rural highways in Oregon to curve advisory speed signs and other influential factors. The Advisory Speed Crash Factor (ASCF) emerges as a sub-model that characterizes the safety effect of advisory speed signs. Results indicate that safety may be compromised if the advisory speed is either excessively prohibitive or excessively permissive. The second manuscript extends the use of the proposed ASCF to develop the OSU posting method, a new procedure that procures the "optimal" advisory speed derived from the ASCF. A field validation analysis, also presented in this manuscript, verified the meaningfulness of the proposed ASCF sub-model. The third manuscript outlines another methodology, named 'the Hybrid OSU Posting Method' in an effort to mitigate the well documented variability associated with using the Ball Bank Indicator (BBI). This method determines the advisory speed using the BBI in combination with the ASCF. Though benefits in safety performance and consistency resulted from using the Hybrid OSU method, this method is still outperformed by the computational OSU method. / Graduation date: 2012 / Access restricted to the OSU Community at author's request from May 25, 2012 - May 25, 2013

Transit travel time is affected by many factors including traffic signals and traffic condition. Transit agencies have implemented strategies such as transit signal priority (TSP) to reduce transit travel time and improve service reliability. However, due to the lack of empirical data, the joint impact of these factors and improvement strategies on bus travel time has not been studied at the stop-to-stop segment level. This study utilizes and integrates three databases available along an urban arterial corridor in Portland, Oregon. Data sources include stop-level bus automatic vehicle location (AVL) and automatic passenger count (APC) data provided by the Tri-County Metropolitan Transportation District of Oregon (TriMet), the Sydney Coordinated Adaptive Traffic System (SCATS) signal phase log data, and intersection vehicle count data provided by the City of Portland. Based on the unique collection and integration of these fine granularity empirical data, this research utilizes multiple linear regression models to understand and quantify the joint impact of intersection signal delay, traffic conditions and bus stop location on bus travel time and its variability at stop-to-stop segments. Results indicate that intersection signal delay is the key factor that affects bus travel time variability. The amount of signal delay is nearly linearly associated with intersection red phase duration. Results show that the effect of traffic conditions (volumes) on bus travel time varies significantly by intersection and time of day. This study also proposed new and useful performance measures for evaluating the effectiveness of TSP systems. Relationships between TSP requests (when buses are late) and TSP phases were studied by comparing TSP phase start and end times with bus arrival times at intersections. Results show that green extension phases were rarely used by buses that requested TSP and that most green extension phases were granted too late. Early green effectiveness (percent of effective early green phases) is much higher than green extension effectiveness. The estimated average bus and passenger time savings from an early green phase are also greater compared to the average time savings from a green extension phase. On average, the estimated delay for vehicles on the side street due to a TSP phase is less than the time saved for buses and automobiles on the major street. Results from this study can be used to inform cities and transit agencies on how to improve transit operations. Developing appropriate strategies, such as adjusting bus stop consolidation near intersections and optimizing bus operating schedules according to intersection signal timing characteristics, can further reduce bus travel time delay and improve TSP effectiveness.