Drivers' mistakes in making immediate decision facing yellow signal interval to stop or go through the intersection is one of main factors contributing to intersection's safety. Incorrect decision might lead to a red light running and a right-angle Collison when passing through the intersection or a rear-end collision when failing to stop safely.Improperly timed traffic signal intervals result in the inability of the drivers to make the right decision and can place them in the dilemma zone. Advance warning systems (AWS) have been used to provide information about the downstream traffic signal change prior to approaching the intersection. On the other hand, advance warning systems increase drivers approach speed according to the literature. However, effect of AWS on dilemma zone has not been studied before. The goal of this thesis is to minimize the number of vehicles caught in dilemma zone by determining more precise boundaries for dilemma zone and to reduce the number of red light violations by predicting the red light runners before arriving to the intersection. Here, dilemma zone boundaries at the presence of AWS has been reexamined with the aid of a large dataset (more than 1870 hours of data for two different intersections). Upper dilemma zone boundaries found to be higher for the intersections with AWS. This is due to vehicles' increasing the speed at the flashing yellow sings to escape the dilemma zone.Moreover, an algorithm for predicting red light runners and distinguishing them from right turners is presented. / Master of Science

Driver violations at traffic signals are a major cause of intersection vehicle crashes. The yellow interval is used to inform approaching drivers of an upcoming change in the traffic signal indication from green to red. Current yellow-interval durations are currently calculated to accommodate for dilemma zone protection for passenger cars only. Buses with different vehicle, driver, and occupancy characteristics behave differently at the onset of a yellow indication. The research presented in this thesis characterizes the difference between bus and passenger car driver behavior at the onset of yellow-indication. A revised set of yellow timing procedures are presented to address the requirements for bus dilemma zone protection. A dataset of 864 stop-go records were collected as part of the research effort using a school bus approaching a traffic signal on the Virginia Smart Road facility. The experiment was conducted at an instructed speed limit of 57 km/h (35 mph) approach speed where participant drivers were presented with yellow indications. A total of 36 participating bus drivers were randomly selected from three age groups (under 40 years old, 40 to 64 years old and 65 and above) with equal number of male and female for each age group. Using the data collected as part of this research effort, statistical models were created to model bus driver perception-reaction times (PRTs) and deceleration levels considering driver attributes (age and gender), roadway grade, vehicle approach speed, and time to intersection (TTI) at the onset of the yellow indication. A Monte-Carlo simulation was conducted to develop appropriate yellow indication timings to provide adequate dilemma zone protection for buses. Lookup tables were then developed for different reliability levels to provide practical guidelines for the design of yellow signal timings to accommodate different bus percentages within the traffic stream. The recommended change durations can be integrated within the Vehicle Infrastructure Integration (VII) initiative to provide customizable driver warnings prior to a transition to a red indication. / Master of Science

The onset of a yellow indication is typically associated with the risk of vehicle crashes resulting from dilemma-zone and red-light-running problems. Such risk of vehicle crashes is greater for high-speed signalized intersection approaches. The research presented in this dissertation develops statistical as well as behavioral frameworks for modeling driver behavior while approaching high-speed signalized intersection approaches at the onset of a yellow indication. The analysis in this dissertation utilizes two sources of data. The main source is a new dataset that was collected as part of this research effort during the summer of 2008. This experiment includes two instructed speeds; 72.4 km/h (45 mph) with 1727 approaching trials (687 running and 1040 stopping), and 88.5 km/h (55 mph) with 1727 approaching trials (625 running and 1102 stopping). The complementary source is an existing dataset that was collected earlier in the spring of 2005 on the Virginia Smart Road facility. This dataset includes a total of 1186 yellow approaching trials (441 running and 745 stopping). The adopted analysis approach comprises four major parts that fulfill the objectives of this dissertation. The first part is concerned with the characterization of different driver behavioral attributes, including driver yellow/red light running behavior, driver stop-run decisions, driver perception-reaction times (PRT), and driver deceleration levels. The characterization of these attributes involves analysis of variance (ANOVA) and frequency distribution analyses, as well as the calibration of statistical models. The second part of the dissertation introduces a novel approach for computing the clearance interval duration that explicitly accounts for the reliability of the design (probability that drivers do not encounter a dilemma zone). Lookup tables are developed to assist practitioners in the design of yellow timings that reflects the stochastic nature of driver PRT and deceleration levels. An extension of the proposed approach is presented that can be integrated with the IntelliDriveSM initiative. Furthermore, the third part of the dissertation develops an agent-based Bayesian statistics approach to capture the stochastic nature of the driver stop-run decision. The Bayesian model parameters are calibrated using the Markov Chain Monte Carlo (MCMC) slice procedure implemented within the MATLAB® software. In addition, two procedures for the Bayesian model application are illustrated; namely Cascaded regression and Cholesky decomposition. Both procedures are demonstrated to produce replications that are consistent with the Bayesian model realizations, and capture the parameter correlations without the need to store the set of parameter realizations. The proposed Bayesian approach is ideal for modeling multi-agent systems in which each agent has its own unique set of parameters. Finally, the fourth part of the dissertation introduces and validates a state-of-the-art behavioral modeling framework that can be used as a tool to simulate driver behavior after the onset of a yellow indication until he/she reaches the intersection stop line. The behavioral model is able to track dilemma zone drivers and update the information available to them every time step until they reach a final decision. It is anticipated that this behavioral model will be implemented in microscopic traffic simulation software to enhance the modeling of driver behavior as they approach signalized intersections. / Ph. D.

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In the United States, intersections are among the most frequent locations for crashes. One of the major problems at signalized intersection is the dilemma zone, which is caused by false driver behavior during the yellow interval. This research evaluated driver behavior during the yellow interval at signalized intersections and compared different dilemma zone countermeasures. The study was conducted through four stages. First, the driver behavior during the yellow interval were collected and analyzed. Eight variables, which are related to risky situations, are considered. The impact factors of drivers' stop/go decisions and the presence of the red-light running (RLR) violations were also analyzed. Second, based on the field data, a logistic model, which is a function of speed, distance to the stop line and the lead/follow position of the vehicle, was developed to predict drivers' stop/go decisions. Meanwhile, Cellular Automata (CA) models for the movement at the signalized intersection were developed. In this study, four different simulation scenarios were established, including the typical intersection signal, signal with flashing green phases, the intersection with pavement marking upstream of the approach, and the intersection with a new countermeasure: adding an auxiliary flashing indication next to the pavement marking. When vehicles are approaching the intersection with a speed lower than the speed limit of the intersection approach, the auxiliary flashing yellow indication will begin flashing before the yellow phase. If the vehicle that has not passed the pavement marking before the onset of the auxiliary flashing yellow indication and can see the flashing indication, the driver should choose to stop during the yellow interval. Otherwise, the driver should choose to go at the yellow duration. The CA model was employed to simulate the traffic flow, and the logistic model was applied as the stop/go decision rule. Dilemma situations that lead to rear-end crash risks and potential RLR risks were used to evaluate the different scenarios. According to the simulation results, the mean and standard deviation of the speed of the traffic flow play a significant role in rear-end crash risk situations, where a lower speed and standard deviation could lead to less rear-end risk situations at the same intersection. High difference in speed are more prone to cause rear-end crashes. With Respect to the RLR violations, the RLR risk analysis showed that the mean speed of the leading vehicle has important influence on the RLR risk in the typical intersection simulation scenarios as well as intersections with the flashing green phases' simulation scenario. Moreover, the findings indicated that the flashing green could not effectively reduce the risk probabilities. The pavement marking countermeasure had positive effects on reducing the risk probabilities if a platoon's mean speed was not under the speed used for designing the pavement marking. Otherwise, the risk probabilities for the intersection would not be reduced because of the increase in the RLR rate. The simulation results showed that the scenario with the pavement marking and an auxiliary indication countermeasure, which adds a flashing indication next to the pavement marking, had less risky situations than the other scenarios with the same speed distribution. These findings suggested the effectiveness of the pavement marking and an auxiliary indication countermeasure to reduce both rear-end collisions and RLR violations than other countermeasures.

This research explores the red light running phenomena and offer a better understanding of the factors associated with it. The red light running is a type of traffic violation that can lead to angle crash and the most common counter measure is installing a red light running cameras. Red light running cameras some time can reduce the rates of red light running but because of the increased worry of the public towards crossing the intersection it can cause an increase in rear end crashes. Also the public opinion of the red light running cameras is that they are a revenue generator for the local counties and not a concern of public safety. Further more, they consider this type of enforcement as violation of privacy. There was two ways to collect the data needed for the research. One way is through a tripod cameras setup temporarily placed at the intersection. This setup can collect individual vehicles caught in the change phase with specific information about their reactions and conditions. This required extensive manual analysis for the recorded videos plus data could not be collected during adverse weather conditions. The second way was using traffic monitoring cameras permanently located at the site to collect red light running information and the simultaneous traffic conditions. This system offered more extensive information since the cameras monitor the traffic 24/7 collecting data directly. On the other hand this system lacked the ability to identify the circumstances associated with individual red light running incidents. The research team finally decided to use the two methods to study the red light running phenomena aiming to combine the benefits of the two systems. During the research the team conducted an experiment to test a red light running countermeasure in the field and evaluate the public reaction and usage of this countermeasure. The marking was previously tested in a driving simulator and proved to be successful in helping the drivers make better stop/go decisions thus reducing red light running rates without increasing the rear-end crashes. The experiment was divided into three phases; before marking installation called "before", after marking installation called "after", and following a media campaign designed to inform the public about the use of the marking the third phase called "after media" The behavior study that aimed at analyzing the motorist reactions toward the signal change interval identified factors which contributed to red light running. There important factors were: distance from the stop bar, speed of traffic, leading or following in the traffic, vehicle type. It was found that a driver is more likely to run red light following another vehicle in the intersection. Also the speeding vehicles can clear the intersection faster thus got less involved in red light running violations. The proposed "Signal Ahead" marking was found to have a very good potential as a red light running counter measure. The red light running rates in the test intersection dropped from 53 RLR/hr/1000veh for the "before" phase, to 24 RLR/hr/1000veh for the "after media" phase. The marking after media analysis period found that the marking can help the driver make stop/go decision as the dilemma zone decreased by 50 ft between the "before" and the "after media" periods. Analysis of the traffic condition associated with the red light running it revealed that relation between the traffic conditions and the red light running is non-linear, with some interactions between factors. The most important factors included in the model were: traffic volume, average speed of traffic, the percentage of green time, the percentage of heavy vehicles, the interaction between traffic volume and percentage of heavy vehicles. The most interesting finding was the interaction between the volume and the percent of heavy vehicles. As the volume increased the effect of the heavy vehicles reversed from reducing the red light running to increasing the red light. This finding may be attributed to the sight blocking that happens when a driver of a passenger car follows a larger heavy vehicle, and can be also explained by the potential frustration experienced by the motorist resulting from driving behind a bigger vehicle.

Among the most critical elements at signalized intersections are the design of vehicle detection equipment and the timing of change and clearance intervals. Improperly timed clearance intervals or improperly placed detection equipment can potentially place drivers in a Type I dilemma zone, where approaching motorists can neither proceed through the intersection before opposing traffic is released nor safely stop in advance of the stop bar. Type II dilemma zones are not necessarily tied to failures in design, but are more readily tied to difficulties in driver decision making associated with comprehension and behavior. The Type II dilemma zone issues become even more prevalent at high-speed intersections where there is greater potential for serious crashes and more variability in vehicle operating speeds. This research initiative attempts to further describe the impact of driver behavior and comprehension on dilemma zones. To address this notion several experiments are proposed. First, a large empirical observation of high-speed signalized intersections is undertaken at 10 intersection approaches in Vermont. This resulted in the collection of video and speed data as well as full intersection inventories and signal timings. These observations are reduced and analyzed for the purpose of reexamining the boundaries of a Type II dilemma zone. Second, a comparison of point and space sensors for the purpose of dilemma zone mitigation was conducted. This experiment provides evidence supporting the notion that space sensors have the potential for providing superior dilemma zone protection. Third, a computer based survey is conducted to identify if drivers comprehend the correct meaning of the solid yellow indication and how this relates to their predicted behavior. Lastly, a regression model is developed drawing on the data collected from the field observation as well as the static survey to determine how characteristics such as the speed and position of the vehicle as well as driver age and experience influence driver behavior in the Type II dilemma zone. Cumulatively, these experiments will shed additional light on the influence of driver behavior and comprehension on the Type II dilemma zone.

Traffic conflicts associated to signalized intersections are one of the major contributing factors to crash occurrences. Driver behavior plays an important role in the safety concerns related to signalized intersections. In this research effort, dynamics of driver behavior in relation to the traffic conflicts occurring at the onset of yellow is investigated. The area ahead of intersections in which drivers encounter a dilemma to pass through or stop when the yellow light commences is called Dilemma Zone (DZ). Several DZ-protection algorithms and advance signal settings have been developed to accommodate the DZ-related safety concerns. The focus of this study is on drivers' decision dynamics, human learning, and choice behavior in DZ, and DZ-related safety measures. First, influential factors to drivers' decision in DZ were determined using a driver behavior survey. This information was applied to design an adaptive experiment in a driving simulator study. Scenarios in the experimental design are aimed at capturing drivers learning process while experiencing safe and unsafe signal settings. The result of the experiment revealed that drivers do learn from some of their experience. However, this learning process led into a higher level of risk aversion behavior. Therefore, DZ-protection algorithms, independent of their approach, should not have any concerns regarding drivers learning effect on their protection procedure. Next, the possibility of predicting drivers' decision in different time frames using different datasets was examined. The results showed a promising prediction model if the data collection period is assumed 3 seconds after yellow. The prediction model serves advance signal protection algorithms to make more intelligent decisions. In the next step, a novel Surrogate Safety Number (SSN) was introduced based on the concept of time to collision. This measure is applicable to evaluate different DZ-protection algorithms regardless of their embedded methodology, and it has the potential to be used in developing new DZ-protection algorithms. Last, an agent-based human learning model was developed integrating machine learning and human learning techniques. An abstracted model of human memory and cognitive structure was used to model agent's behavior and learning. The model was applied to DZ decision making process, and agents were trained using the driver simulator data. The human learning model resulted in lower and faster-merging errors in mimicking drivers' behavior comparing to a pure machine learning technique. / Ph. D.

Dilemma zone (DZ), also called decision zone in other literature, is an area where drivers face an indecisiveness of stopping or crossing at the yellow onset. The DZ issue is a major reason for the crashes at high-speed signalized intersections. As a result, how to prevent approaching vehicles from being caught in the DZ is a widely concerning issue. In this dissertation, the author addressed several DZ-associated issues, including the new stochastic safety measure, namely dilemma hazard, that indicates the vehicles' changing unsafe levels when they are approaching intersections, the optimal advance detector configurations for the multi-detector green extension systems, the new dilemma zone protection algorithm based on the Markov process, and the simulation-based optimization of traffic signal systems with the retrospective approximation concept. The findings include: the dilemma hazard reaches the maximum when a vehicle moves in the dilemma zone and it can be calculated according the caught vehicles' time to the intersection; the new (optimized) GES design can significantly improve the safety, but slightly improve the efficiency; the Markov process can be used in the dilemma zone protection, and the Markov-process-based dilemma zone protection system can outperform the prevailing dilemma zone protection system, the detection-control system (D-CS). When the data collection has higher fidelity, the new system will have an even better performance. The retrospective approximation technique can identify the sufficient, but not excessive, simulation efforts to model the true system and the new optimization algorithm can converge fast, as well as accommodate the requirements by the RA technique. / Ph. D.