Highway traffic control systems have been deployed to provide emergency vehicle preemption (EVP) at signalized intersections. Industry and transportation researchers have worked to develop analytical methods to establish the degree of benefit of emergency vehicle preemption to the emergency vehicle (EV) community and the impact on other road user groups. This thesis report illustrates the use of an analytical method to evaluate the potential impacts of EVP related to EV safety, and the potential delay to EVs and vehicles on the side street. The method uses EV-specific conflict point and delay analysis with video and other data collected in a field study conducted in Northern Virginia at the intersection of Southgate Drive and U.S. 1. EV related conflict points are characterized in terms of the EV/auto interaction geometry, the signal display, and the severity of potential crashes. EV related delay is characterized in terms of the EV/auto interaction geometry, the signal display, the level of service and the amount of delay to the EV. The EV/auto interaction, the queue length and the signal display characterize increase in delay to vehicles on the side street. The analysis indicates that the severity of EV-specific conflict points is significantly reduced with EVP. The delay to EV does not change significantly and the delay to the vehicles on the side street auto traffic increases. / Master of Science

Emergency vehicle traffic signal priority systems allow emergency vehicles such as fire and emergency medical vehicles to request and receive a green traffic signal indication when approaching an intersection. Such systems have been around for a number of years, however, there is little understanding of the costs and benefits of such systems once they are deployed. This research develops an improved method to assess the travel time impacts of emergency vehicle traffic signal priority systems for transportation planning analyses. The research investigates the current state of available methodologies used in assessing the costs and benefits of emergency vehicle traffic signal priority systems. The ITS Deployment Analysis System (IDAS) software is identified as a recently developed transportation planning tool with cost and benefit assessment capabilities for emergency vehicle traffic signal priority systems. The IDAS emergency vehicle traffic signal priority methodology is reviewed and recommendations are made to incorporate the estimation of non-emergency vehicle travel time impacts into the current methodology. To develop these improvements, a simulation analysis was performed to model an emergency vehicle traffic signal priority system under a variety of conditions. The simulation analysis was implemented using the CORSIM traffic simulation software as the tool. Results from the simulation analysis were used to make recommendations for enhancements to the IDAS emergency vehicle traffic signal priority methodology. These enhancements include the addition of non-emergency vehicle travel time impacts as a function of traffic volume on the transportation network. These impacts were relatively small and ranged from a 1.1% to 3.3% travel time increase for a one-hour analysis period to a 0.6% to 1.7% travel time increase for a two-hour analysis period. The enhanced methodology and a sample application of the methodology are presented as the results of this research. In addition, future research activities are identified to further improve assessment capabilities for emergency vehicle traffic signal priority systems. / Ph. D.

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.

Motivated by recent advances in vehicle positioning and vehicle-to-infrastructure (V2I) communication, traffic signal controllers are able to make smarter decisions. Most of the current state-of-the-practice signal priority control systems aim to provide priority for only one mode or based on first-come-first-served logic. Consideration of priority control in a more general framework allows for several different modes of travelers to request priority at any time from any approach and for other traffic control operating principles, such as coordination, to be considered within an integrated signal timing framework. This leads to provision of priority to connected priority eligible vehicles with minimum negative impact on regular vehicles. This dissertation focuses on providing a real-time decision making framework for multi modal traffic signal control that considers several transportation modes in a unified framework using Connected Vehicle (CV) technologies. The unified framework is based on a systems architecture for CVs that is applicable in both simulated and real world (field) testing conditions. The system architecture is used to design both hardware-in-the-loop and software-in-the-loop CV simulation environment. A real-time priority control optimization model and an implementation algorithm are developed using priority eligible vehicles data. The optimization model is extended to include signal coordination concepts. As the penetration rate of the CVs increases, the ability to predict the queue more accurately increases. It is shown that accurate queue prediction improves the performance of the optimization model in reducing priority eligible vehicles delay. The model is generalized to consider regular CVs as well as priority vehicles and coordination priority requests in a unified mathematical model. It is shown than the model can react properly to the decision makers' modal preferences.

Advances in microprocessor and communications technologies are making it possible to deploy advanced traffic signal controllers capable of integrating emergency vehicle preemption and transit priority operations. However, investment planning for such an integrated system is not a trivial task. Investment planning for such a system requires a holistic approach that considers institutional, technical and financial issues from a systems perspective. Two distinct service providers, fire and rescue providers and transit operators, with separate operational functions, objectives, resources and constituents are involved. Performance parameters for the integrated system are not well defined and performance data are often imprecise in nature. Transportation planners and managers interested in deploying integrated emergency vehicle preemption and traffic priority systems do not have an evaluation approach or a common set of performance metrics to make an informed decision. There is a need for a simple structured analytical approach and tools to assess the impacts of an integrated emergency vehicle preemption and transit priority system as part of investment decision making processes. This need could be met with the assistance of a decision support system (DSS) developed to provide planners and managers a simple and intuitive analytical approach to assist in making investment decisions regarding emergency vehicle preemption and transit signal priority. This dissertation has two research goals: (1) to develop a decision support system framework to assess the impacts of advanced traffic signal control systems capable of integrating emergency vehicle preemption and transit signal priority operations for investment planning purposes; and (2) to develop selected analytical tools for incorporation into the decision support system framework. These analytical tools will employ fuzzy sets theory concepts, as well as cost and accident reduction factors. As part of this research, analytical tools to assess impacts on operating cost for transit and fire and rescue providers have been developed. In addition, an analytical tool was developed and employs fuzzy multi-attribute decision making methods to rank alternative transit priority strategies. These analytical tools are proposed for incorporation into the design of a decision support system in the future. / Ph. D.