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ÖgeA multi-objective optimization framework for trade-off among pedestrian delays and vehicular emissions at signal controlled intersections(Graduate School, 2021-12-14)Traffic congestion has numerous negative effects on urban life. Increased travel time and vehicular emissions are some of these negative effects. On one hand, the transportation sector is the leading factor in contributing to climate change air pollution based on the greenhouse gas emission of 29%. On the other hand, pedestrian traffic management requires extreme caution, especially in Central Business Districts. In classic traffic signal control applications, allocation of pedestrian green time is held at the minimum value mostly. However, in crowded intersections located in city centers, the number of pedestrians that need to be served can be excessive due to a number of reasons (gatherings, touristic, sport event, etc.). In this study, an integrated methodology for optimizing traffic signal control considering pedestrian delay and vehicular emissions is developed. VISSIM is used as the microscopic traffic simulator, the Non-dominated sorting genetic algorithm-II is adopted to solve the multi-objective optimization problem at hand, and MOVES3 is used to calculate vehicular emissions on a microscopic scale. To interfere with the traffic signal control settings, COM feature of VISSIM is used in conjunction with MATLAB. By using COM interface, one can change the signal control settings, vehicle and pedestrian inputs, routes of vehicles, and many other features that can be read and changed during simulations. To illustrate the trade-off between pedestrian delay and vehicular emissions, two objective functions are formulated. The input for these functions are obtained from VISSIM via COM interface. Since the objective functions are conflicting with each other, one tries to maximize the pedestrian green time while the other tries to maximize vehicle green time, a trade-off is observed between the objectives. In addition, a case study is conducted at Kadıköy, Istanbul to evaluate the proposed approach. Data is retrieved using camera recordings. Collected data involves the vehicle and pedestrian counts, and average crossing times of pedestrians. Calibration of the simulation model is done considering GEH statistics. After the calibration, two main scenarios are designed. The first main scenario involves a gradual change in vehicles loaded to the network. The second main scenario is produced to test the different prioritization approaches with changing vehicle demand. Three different sub-scenarios are generated in this manner. First, the sub-scenario is the situation where pedestrian movement is prioritized by giving more pedestrian time compared to vehicles. The second sub-scenario is created to achieve a balance between pedestrian and vehicle green times. The third sub-scenario is produced to prioritize vehicles over pedestrians. In the second scenario, all the signal timings are chosen from the Pareto front set acquired from the multi-objective optimization solved with MATLAB. Results acquired from simulations suggest a trade-off between pedestrian delay and vehicular emissions. In conclusion, a novel method is proposed in this study to assess through trade-off the signal control settings considering pedestrian delay and vehicular emissions. Despite the fact that an optimization problem is solved in the thesis, a unique global solution is not acquired. Because more than one objective is overlooked, multiple solutions are obtained after the optimization process. The multi-objective optimization problem is handled with a posteriori approach which enabled us to sense some intuition over the problem and its Pareto optimal solutions. By using this unique feature, scenarios are designed to test the solutions. In future research, the proposed framework can be applied to a variety of networks and traffic conditions. Safety measures can be added to the multi-objective optimization framework. 3-D Pareto fronts can be acquired for pedestrian delay, emissions, and safety in an optimization framework.