During a signal malfunction, traffic signals are operated in the flash mode. During this event, drivers are presented with one of two possible scenarios: (1) flashing yellow on the major street and flashing red on the minor street or (2) flashing red on all approaches. Yellow/red flash is typically the default mode utilized based on the expectation that red/red flash would produce an intolerable amount of delay. However, little research has been conducted to date on flashing operations, with exception of low-volume nighttime conditions. A traffic signal malfunction can occur during any time of the day, potentially placing the signal into flash mode under moderate to peak traffic volume conditions. In order to assess the safety implications of these events and improve the process by which the mode of flash (yellow/red versus red/red) is selected, the research contained in this study evaluated driver behavior and the operational characteristics of intersections operating in malfunction flash mode under a wide spectrum of traffic demands. Analysis of field data collected at thirteen study intersections in the Atlanta, Georgia area found that confusion exists among drivers approaching a signal in flash mode. The analysis found that a significant percentage of vehicles stop on a yellow indication. It was seen that an intersection flashing yellow/red could operate as a two-way stop or four-way stop, potentially transitioning between these two alternatives on a minute-by-minute basis. This creates an increased potential for crashes and further compounds the problem of driver expectancy by creating a constantly changing control environment. The stopping on yellow also introduces additional delay, which reduces the operational benefit of utilizing the yellow/red flash mode. Furthermore, a high level of traffic violations was observed for the flashing red indications for both yellow/red and red/red flashing operation. Based upon the study results, providing one consistent mode of flashing operation may be a reasonable solution to improving driver expectancy and safety. Red/red flashing operation is the preferred mode as it reduces vehicle speeds and the variability in the number of vehicles stopping, while improving driver expectancy.

Intersection crashes accounted for 47 percent of the total number of crashes in the State of Georgia from 2000-2005, and as a location where crashes occur, represent the largest number of crash locations in the state. Federal legislation requires states to implement statewide safety plans to reduce fatalities, crashes, and improve safety. Intersections vary in different ways and there are individual factors that can cause an intersection to be safer or more dangerous than another. Acquiring better, uniform, and more updated information with regard to intersection crashes will enable transportation officials to prescribe policies for improving safety in an easier and more expedited manner. This thesis recommends a five-part program for intersection safety that will enable Georgia transportation officials to better analyze, identify, and implement countermeasures at intersections that are determined to be the most hazardous. The plan consists of: 1. Standardized Hazardous Intersection Identification Method 2. Statewide Public Involvement task force 3. Automated Police Crash Reporting Through Improved Technologies 4. Strategic Highway Safety Plan (SHSP) Intersection Safety Strategies (8 State Comparison) 5. Statewide Minimal Intersection Safety Equipment The five parts of this plan lead to a statewide standard method of analyzing intersections based on uniform collection methods and uniform equipment statewide.

Preferential treatments are employed to provide preemption for emergency vehicles (EV) and conditional priority for transit vehicles at signalized intersections. EV preemption employs technologies and signal control strategies seeking to reduce emergency vehicle crash potential and response times. Transit priority employs the same technologies with signal control strategies seeking to reduce travel time and travel time variability. Where both preemption and transit technologies are deployed, operational strategies deconflict simultaneous requests. Thus far, researchers have developed separate evaluation frameworks for preemption and priority. This research addresses the issue of preemption and priority signal control strategies in breadth and depth. In breadth, this research introduces a framework that reveals planning interdependence and operational interaction between preemption and priority from the controlling strategy down to roadway hardware operation under the inclusive title: preferential treatment. This fulfills a current gap in evaluation. In depth, this research focuses on evaluation of EV preemption. There are two major analytical contributions resulting from this research. The first is a method to evaluate the safety benefits of preemption based on conflict analysis. The second is an algorithm, suitable for use in future traffic simulation models, that incorporates the impact of auto driver behavior into the determination of travel time savings for emergency vehicles operating on signalized arterial roadways. These two analytical methods are a foundation for future research that seeks to overcome the principal weakness of current EV preemption evaluation. Current methods, which rely on modeling and simulation tools, do not consider the unique auto driver behaviors observed when emergency vehicles are present. This research capitalizes on data collected during a field operational test in Northern Virginia, which included field observations of emergency vehicles traversing signalized intersections under a wide variety of geometric, traffic flow, and signal operating conditions. The methods provide a means to quantify the role of EV preemption in reducing the number and severity of conflict points and the delay experienced at signalized intersections. This forms a critical basis for developing deployment and operational guidelines, and eventually, warrants. / Ph. D.

Nighttime intersection crashes account for nearly half of all the intersection crashes, making them a major traffic safety concern. Although providing lighting at intersections has proven to be a successful countermeasure against these crashes, existing approaches to designing lighting at intersections are overly simplified. Current standards are based on recommending lighting levels, but do not account for the role of human vision or vehicle headlamps or the numerous pedestrian-vehicle conflict locations at intersections. For effective intersection lighting design, empirical evidence is required regarding the effects of lighting configuration (part of the intersection illuminated) and lighting levels on nighttime visibility. This research effort had three goals. The first was to identify an intersection lighting design that results in the best nighttime visibility. The second goal was to determine the effect of illuminance on visual performance at intersections. The third goal was to understand the relationships between object luminance, contrast, and visibility. To achieve these goals, three specific configurations were used, that illuminated the intersection approach (Approach), intersection box (Box), and both the intersection approach and box (Both). Each lighting configuration was evaluated under five levels of illumination. Visibility was assessed both objectively (visual performance) and subjectively (perceptions of visibility and glare). Illuminating the intersection box led to superior visual performance, higher perceived visibility, and lower perceived glare. For this same configuration, plateaus in visual performance and perceived visibility occurred between 8 and 12 lux illuminance levels. A photometric analysis revealed that the Box lighting configuration rendered targets in sufficient positive and negative contrasts to result in higher nighttime visibility. Negatively contrast targets aided visual performance, while for targets rendered in positive contrast visual performance was dependent on the magnitude of the contrast. The relationship between pedestrian contrast and perceived pedestrian visibility was more complex, as pedestrians were often rendered in multiple contrast polarities. These results indicate that Box illumination is an effective strategy to enhance nighttime visual performance and perceptions of visibility while reducing glare, and which may be an energy efficient solution as it requires fewer luminaires. / Ph. D.

Roadway safety continues to be a priority for the Utah Department of Transportation (UDOT) Traffic and Safety Division. UDOT has participated in and managed several research projects in recent years to determine the roadway segments of highest safety concern in the state. This research has provided UDOT with more tools to assist in safety project prioritization. Researchers in Department of Civil and Environmental Engineering at Brigham Young University (BYU) have worked with UDOT and the Statistics Department at BYU to create two network screening statistical tools called the Utah Crash Prediction Model (UCPM) and the Utah Crash Severity Model (UCSM) to analyze roadway segment safety. The Roadway Safety Analysis Methodology (RSAM) was developed as a process to run these segment models. Because a significant portion of crashes occur at intersections, there is a need to analyze roadway safety specifically at intersections. This research focuses on the development of the Utah Intersection Crash Prediction Model (UICPM) and the Intersection Safety Analysis Methodology (ISAM). The UICPM is a Bayesian generalized linear model that determines crash distributions for each intersection based on roadway characteristics and historical crash data. The observed number of crashes at each intersection is compared with the crash distribution, and a percentile value is calculated as the probability that the number of crashes occurring at an intersection in a particular year is less than or equal to the average annual number of crashes. A high percentile value indicates that more crashes were observed than expected and the intersection is a hot spot and should be considered for safety improvements. All intersections are ranked at the state, UDOT Region, and county levels based on the percentile value, the higher ranks having higher percentile values. The ISAM is the three-step process that was developed to execute the UICPM. The first step is to prepare the model input by formatting and combining the roadway characteristics and crash data files. Crashes are assigned to intersections if they fall with the functional area of an intersection. Due to data limitations, the ISAM is currently being used only for intersections of at least two state routes. It is anticipated that, as more data are made available, the ISAM will function properly for intersections of non-state routes as well. The second step is to execute the UICPM using the R GUI tool and R software. The third step is to create a two-page Intersection Safety Analysis Report (ISAR) for intersections of interest and maps of the state, UDOT Regions, and counties with the model results. Parts of the ISARs are auto-generated and the rest is entered manually by an analyst. The two-page ISARs will be used by UDOT Regions to prioritize intersection safety projects in their respective areas.

With the number of vehicles increasing in the system every day, many statewide policies across the United States aim to increase the use of non- motorized transportation modes. This could have safety implications because the interaction between motorists and non-motorists could increase and potentially increasing pedestrian-vehicle crashes. Few models that predict the number of pedestrian crashes are not sensitive to site-specific conditions or intersection designs that may influence pedestrian crashes. Moreover, traditional statistical modeling techniques rely extensively on the sparsely available pedestrian crash database. This study focused on overcoming these limitations by developing models that quantify potential interactions between pedestrians and vehicles at various intersection designs using as surrogate safety measure the time to conflict. Several variables that capture volumes, intersection geometry, and operational performance were evaluated for developing pedestrian-vehicle conflict models for different intersection designs. Linear regression models were found to be best fit and potential conflict models were developed for signalized, unsignalized and roundabout intersections. Volume transformations were applied to signalized and unsignalized conditions to develop statistical models for unconventional intersections. The pedestrian-vehicle conflicting volumes, the number of lanes that pedestrians are exposed to vehicles, the percentage of turning vehicles, and the intersection conflict location (major or minor approach) were found to be significant predictors for estimating pedestrian safety at signalized and unsignalized intersections. For roundabouts, the pedestrian-vehicle conflicting volumes, the number of lanes that pedestrians have to cross, and the intersection conflict location (major or minor approach) were found to be significant predictors. Signalized intersection models were used for bowtie and median U-turn intersections using appropriate volume transformations. The combination of signalized intersection models for the intersection area and two-way unsignalized intersection models for the ramp area of the jughandle intersections were utilized with appropriate volume transformations. These models can be used to compare alternative intersection designs and provide designers and planners with a surrogate measure of pedestrian safety level for each intersection design examined.

Innerorts-Knotenpunkten gilt aufgrund der vielen Unfälle eine hohe Aufmerksamkeit in der Unfallforschung und -prävention. Die vorliegende Arbeit identifiziert und quantifiziert Einflüsse, welche die Komplexität von Knotenpunkten bestimmen und sich so auf Fehlerraten und somit Unfallzahlen auswirken. Dazu werden verallgemeinert lineare Modelle verwendet. Dabei erweisen sich neben der Verkehrsstärke vor allem die Anzahl der Konfliktpunkte an nichtsignalisierten Knotenpunkten sowie die Links- und Rechtsabbiegersignalisierungen an signalisierten Knotenpunkten als signifikante Einflüsse auf die Unfallzahlen. Entsprechend können komplexitätsverringernde Maßnahmen abgeleitet werden. / A great deal of research on road safety and accident prevention focuses on urban intersections due to high crash frequencies. In this paper urban intersection complexity is broken down into single quantifiable effects that determine crash counts using generalised linear models. Besides traffic volumes it can be shown that the number of conflict points explain crash counts at non-signalised intersections. Whereas crash counts at signalised intersections are effected by protected left- and right-turn signalling. Practical measures can be deduced from this work in order to manage intersection safety.

The focus of this dissertation is on safety improvement at intersections and presenting how Vehicle/Bicycle-to-Infrastructure Communications can be a potential countermeasure for crashes resulting from drivers' and cyclists' violations at intersections. The characteristics (e.g., acceleration capabilities, etc.) of transportation modes affect the violation behavior. Therefore, the first building block is to identify the users' transportation mode. Consequently, having the mode information, the second building block is to predict whether or not the user is going to violate. This step focuses on two different modes (i.e., driver violation prediction and cyclist violation prediction). Warnings can then be issued for users in potential danger to react or for the infrastructure and vehicles so they can take appropriate actions to avoid or mitigate crashes. A smartphone application was developed to collect sensor data used to conduct the transportation mode recognition task. Driver violation prediction task at signalized intersections was conducted using observational and simulator data. Also, a naturalistic cycling experiment was designed for cyclist violation prediction task. Subsequently, cyclist violation behavior was investigated at both signalized and stop-controlled intersections. To build the prediction models in all the aforementioned tasks, various Artificial Intelligence techniques were adopted. K-fold Cross-Validation as well as Out-of-Bag error was used for model selection and validation. Transportation mode recognition models contributed to high classification accuracies (e.g., up to 98%). Thus, data obtained from the smartphone sensors were found to provide important information to distinguish between transportation modes. Driver violation (i.e., red light running) prediction models were resulted in high accuracies (i.e., up to 99.9%). Time to intersection (TTI), distance to intersection (DTI), the required deceleration parameter (RDP), and velocity at the onset of a yellow light were among the most important factors in violation prediction. Based on logistic regression analysis, movement type and presence of other users were found as significant factors affecting the probability of red light violations by cyclists at signalized intersections. Also, presence of other road users and age were the significant factors affecting violations at stop-controlled intersections. In case of stop-controlled intersections, violation prediction models resulted in error rates of 0 to 10 percent depending on how far from the intersection the prediction task is conducted. / Ph. D.

Mobility has a vital impact on the quality of life in a city. Yet, traditional modes of car-centric transportation models generate large externalities that must be tackled by cities - such as congestion, noise and air pollution. The Free and Hanseatic City of Hamburg in Germany is striving for a mobility transition, making mobility more sustainable and environmentally friendly. The city wants to change the mobility behaviour by strengthening the means of transport that are causing less impact on the environment and climate. By 2030, the goal is to increase the share of cycling, walking and public transport to a total of 80 per cent of all routes travelled. Cycling, which is especially cost- and space-efficient, plays a crucial role here. More specifically, the share of all joumeys made by bike should be increased by 25-30 per cent within this decade. Within the framework of Hamburg's strategy of lntelligent Transport Systems (ITS), the city fosters, develops and conducts ITS-projects that focus, amongst others, on cycling. In order to increase the proportion of cycling, it is essential to promote its attractiveness. A cycling infrastructure that ensures smooth and easy cycling within the city is vital for a competitive alternative to motorised private transport. Furthermore, people enjoy cycling when they feel comfortable and safe [e.g. 3]. The ITS-project PrioBilce-HH follows this approach and addresses both topics: cycling comfort and safety. This abstract focuses on the aspect of safety.

The purpose of this project is to evaluate the safety and operational differences between three left-turn treatments: permitted, protected, and protected-permitted left-turn phasing. Permitted phasing allows vehicles to turn left after yielding to any opposing vehicles; protected phasing provides an exclusive phase for vehicles to turn left that does not allow opposing vehicles; and protected-permitted phasing combines the previous phasing alternatives, allowing vehicles to turn after yielding while also providing some green time for protected left-turns.As part of evaluating the differences between these left-turn treatments, crashes before and after the change at intersections that had experienced a permanent change from one phase alternative to another were compared. The crashes that took place at these intersections were compared with the number of crashes experienced at a baseline set of intersections. A general increase in total crashes was observed for most intersections, and an increase in left-turn crashes per million entering vehicles was also observed in intersections that had experienced a change from protected to protected-permitted phasing; no other clear trends were observed.The research team also gathered simulated data using VISSIM traffic modeling software and safety data were extracted from these simulations using the Surrogate Safety Assessment Model (SSAM) created by the Federal Highway Administration to identify decision boundaries between each left-turn treatment. The simulations modeled intersections with 1-, 2-, and 3-opposing-lane configurations with permitted and protected-permitted models (split into green times of 10-, 15-, and 20-seconds) for a total of 12 different simulation models. Each model was divided into 100-225 different volume scenarios, with incremental increases in left-turn vs. opposing volumes. By exporting trajectory files from VISSIM and importing these files into SSAM, crossing conflicts for each volume combination in each model were identified and extracted. These were then entered into MATLAB to create contour maps; the contours of these maps represent the number of crossing conflicts per hour associated with different combinations of left-turn and opposing volume. Basic decision boundaries were observed in the contour maps for each model. To extract an equation to estimate each boundary, JMP Pro statistical analysis software was used to perform a linear regression analysis and develop natural log-based equations estimating the decision boundaries for each configuration and phase alternative. These equations were then charted using Excel and final decision boundaries were developed for the 1-, 2-, and 3-lane configurations between permitted and protected-permitted phasing as well as between protected-permitted and protected phasing.