Driver violations of traffic signals are a major cause of intersection vehicle crashes. The duration of yellow intervals is highly associated with driver yellow/red time stopping behavior. Rainy weather and wet pavement surface conditions may result in changes in both driver behavior and vehicle performance. The research presented in this thesis quantifies the impact of wet pavement surface and rainy weather conditions on driver perception-reaction times (PRTs) and deceleration levels, which are used in statistical models for the design of yellow intervals. A new dataset with a total of 648 stop-run records were collected as part of the research effort during rainy weather and wet pavement surface conditions at the Virginia Department of Transportation's Smart Road facility. This experiment was conducted at a 72.4 km/h (45 mi/h) approach speed where participant drivers encountered a yellow indication initiation. The participant drivers were randomly selected in different age groups (under 40 years old, 40 to 59 years old, and 60 years of age or older) and genders (female and male). Combined with an existing dataset that was collected by the same research group under clear weather conditions during the summer of 2008, statistical models for driver PRT and deceleration levels are developed, considering roadway surface and environmental parameters, driver attributes (age and gender), roadway grade, and time to the intersection at the onset of yellow. Using the state-of-the-practice procedures with the modeled PRT and deceleration levels, inclement weather yellow timings are then developed as a function of different factors (e.g., driver age/gender, roadway grade, speed limits, and precipitation levels). The results indicate that an increase in the duration of change interval is required for wet roadway surface and rainy weather conditions. Lookup tables are developed with different reliability levels to provide practical guidelines for the design of yellow signal timings in wet and rainy weather conditions. These 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

Traffic simulation and signal timing optimization are classified in structure into two main categories: (i) Macroscopic or Microscopic; (ii) Deterministic or Stochastic. Performance of the optimized signal timing derived by any tool is influenced by the methodology used in how calculations are executed in a particular tool. In this study, the performance of the optimal signal timing plans developed by two of the most popular traffic analysis tools, HCS and Tru-Traffic, each of them has its inbuilt objective function(s) to optimize signal timing for intersection, is compared with an ideal and an existing timing plans (base case) for the area of study using the microsimulation software VISSIM. An urban arterial with 29 intersections and high traffic in Fort Lauderdale, Florida serves as the test bed. To eliminate unfair superiority in the results, all experiments were performed under identical geometry and traffic conditions in each tool. Comparison of the optimized plans is conducted on the basis of average delay, average stopped delay, average number of stops, number of vehicles completed trips, latent delay, and latent demand from the simulated vehicle network performance evaluation results in VISSIM. The results indicate that, overall, HCS with its overall delay objective and the Tru-Traffic programs produce signal timing with comparable quality that performed similar to the un-optimized base case for most of the performance measures. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Signal retiming, or signal optimization process, has not changed much over the last few decades. Traditional procedures rely on low-resolution data and a low-fidelity modeling approach. Such developed signal timing plans always require a fine-tuning process for deployed signal plans in field, thus questioning the very benefits of signal optimization. New trends suggest the use of high-resolution data, which are not easily available. At the same time, many improvements could be made if the traditional signal retiming process was modified to include the use of medium-resolution data and high-fidelity modeling. This study covers such an approach, where a traditional retiming procedure is modified to utilize large medium-resolution data sets, high-fidelity simulation models, and powerful stochastic optimization to develop robust signal timing plans. The study covers a 28-intersection urban corridor in Southeastern Florida. Medium-resolution data are used to identify peak-hour, Day-Of-Year (DOY) representative volumes for major seasons. Both low-fidelity and high-fidelity models are developed and calibrated with high precision to match the field signal operations. Then, by using traditional and stochastic optimization tools, signal timing plans are developed and tested in microsimulation. The findings reveal shortcomings of the traditional approach. Signal timing plans developed from medium-resolution data and high-fidelity modeling approach reduce average delay by 5%-26%. Travel times on the corridor are usually reduced by up to 10.5%, and the final solution does not transfer delay on the other neighboring streets (illustrated through latent delay), which is also decreased by 10%-49% when compared with the traditional results. In general, the novel approach has shown a great potential. The next step should be field testing and validation. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1996. / Vita. Abstract. Includes bibliographical references (leaves 161-166). Also available via the Internet.

Enabling vehicles to preempt the normal operation of traffic signals has the potential to improve the safety and efficiency of both the requesting vehicle and all of the other vehicles. Little is known about which strategy is the most effective to exit from preemption control and transition back to the traffic signals normal timing plan. Common among these traffic signal transition strategies is the method of either increasing or decreasing the cycle length of the signal timing plan, as the process followed to return to the coordination point of the effected signal timing plan, to coordinate its operation with adjacent traffic signals. This research evaluates commonly available transition strategies: best way, long, short, and hold strategies. The major contribution of this research is enhancing the methodology to evaluate the impacts of using these alternative transition strategies. Part of this methodology consists of the "software-in-the-loop" simulation tool which replicates the stochastic characteristics of traffic flow under different traffic volume levels. This tool combines the software from a traffic signal controller (Gardner NextPhase Suitcase Tester, version 1.4B) with a microscopic traffic simulation model (CORSIM, TSIS 5.2 beta version). The research concludes that a statistically significant interaction exists between traffic volume levels and traffic signal transition strategies. This interaction eliminates the ability to determine the isolated effects of either the transition strategies on average travel delay and average travel time, or the effects of changes in traffic volume levels on average travel delay and average travel time. Conclusions, however, could be drawn on the performance of different transition strategies for specific traffic volume levels. As a result, selecting the most effective transition strategy needs to be based on the traffic volume levels and conditions specific to each traffic signal or series of coordinated traffic signals. The research also concludes that for the base traffic volume and a 40% increase in traffic volume, the most effective transition strategies are the best way, long or hold alternatives. The best way was the most effective transition strategy for a 20% increase in traffic volume. The least effective strategy is the short transition strategy for both the base and 40% increase in traffic volume, and the long and short for a 20% increase in traffic volume. Further research needs to be conducted to assess the performance of different transition strategies in returning to coordinated operation under higher levels of traffic volume (e.g., approaching or exceeding congested flow regime), with varying cycle lengths, with different signal timing plans, and when different roadway geometric configurations (e.g., turn lanes, length of turn lanes, number of lanes, spacing between intersections) are present. / Ph. D.