Traffic congestion negatively affects traveler mobility and air quality. Stop and go vehicular movements associated with traffic jams typically result in higher fuel consumption levels compared to cruising at a constant speed. The first objective in the dissertation is to investigate the spatial relationship between air quality and traffic flow patterns. We developed and applied a recursive Bayesian estimation algorithm to estimate the source location (associated with traffic jam) of an airborne contaminant (aerosol) in a simulation environment. This algorithm was compared to the gradient descent algorithm and an extended Kalman filter algorithm. Results suggest that Bayesian estimation is less sensitive to the choice of the initial state and to the plume dispersion model. Consequently, Bayesian estimation was implemented to identify the location (correlated with traffic flows) of the aerosol (soot) that can be attributed to traffic in the vicinity of the Old Dominion University campus, using data collected from a remote sensing system. Results show that the source location of soot pollution is located at congested intersections, which demonstrate that air quality is correlated with traffic flows and congestion caused by signalized intersections. Sustainable mobility can help reduce traffic congestion and vehicle emissions, and thus, optimizing the performance of available infrastructure via advanced traffic signal controllers has become increasingly appealing. The second objective in the dissertation is to develop a novel de-centralized traffic signal controller, achieved using a Nash bargaining game-theoretic framework, that operates a flexible phasing sequence and free cycle length to adapt to dynamic changes in traffic demand levels. The developed controller was implemented and tested in the INTEGRATION microscopic traffic assignment and simulation software. The proposed controller was compared to the operation of an optimum fixed-time coordinated plan, an actuated controller, a centralized adaptive phase split controller, a decentralized phase split and cycle length controller, and a fully coordinated adaptive phase split, cycle length, and offset optimization controller to evaluate its performance. Testing was initially conducted on an isolated intersection, showing a 77% reduction in queue length, a 17% reduction in vehicle emission levels, and a 64% reduction in total delay. In addition, the developed controller was tested on an arterial network producing statistically significant reductions in total delay ranging between 36% and 67% and vehicle emissions reductions ranging between 6% and 13%. Analysis of variance, Tukey, and pairwise comparison tests were conducted to establish the significance of the proposed controller. Moreover, the controller was tested on a network of 38 intersections producing significant reduction in the travel time by 23.6%, a reduction in the queue length by 37.6%, and a reduction in CO2 emissions by 10.4%. Finally, the controller was tested on the Los Angeles downtown network composed of 457 signalized intersections, producing a 35% reduction in travel time, a 54.7% reduction in queue length, and a 10% reduction in the CO2 emissions. The results demonstrate that the proposed decentralized controller produces major improvements over other state-of-the-art centralized and de-centralized controllers. The proposed controller is capable of alleviating congestion as well as reducing emissions and enhancing air quality. / PHD / Traffic congestion affects traveler mobility and also has an impact on air quality, and consequently, on public health. Stop-and-go driving, which is typically associated with traffic jams, results in increased fuel consumption when compared to cruising at a constant speed. This in turn contributes to the amount of vehicle emissions that create air pollution, which contributes to global warming. Consequently, studying the spatial relationships between air quality and traffic flow patterns is directly related to enhancing air quality, as improving these patterns can reduce traffic congestion. The first objective in this dissertation is to investigate the spatial relationship between air quality and traffic flow patterns. We developed and applied a recursive Bayesian estimation algorithm to estimate the source location of an airborne contaminant (aerosol) in a simulation environment. This algorithm was compared to the gradient descent algorithm and the extended Kalman filter. Results suggest that Bayesian estimation is less sensitive to the choice of the initial state and to the plume dispersion model when compared to the other two approaches. Consequently, an experimental investigation using Bayesian estimation was conducted to identify the location (correlated with traffic flows) of the aerosol (soot) that can be attributed to traffic in the vicinity of the Old Dominion University campus, using data collected from a remote sensing system (a compact light detection and ranging [LiDAR] system). The results show that the location of soot pollution in the study area is located at congested intersections, which demonstrates that air quality is correlated with traffic flows and congestion caused by signalized intersections. Sustainable mobility could enhance air quality and alleviate congestion. Accordingly, optimizing the utilization of the available infrastructure using advanced traffic signal controllers has become necessary to mitigate traffic congestion in a world with growing pressure on financial and physical resources. The second objective in the dissertation is to develop a novel de-centralized traffic signal controller that is achieved using a Nash bargaining game-theoretic framework. This framework has a flexible phasing sequence and free cycle length, and thus can adapt to dynamic changes in traffic demand. The controller was implemented and evaluated using the INTEGRATION microscopic traffic assignment and simulation software. The proposed controller was tested and compared to state-of-the-art isolated and coordinated traffic signal controllers. The proposed controller was tested on an isolated intersection, producing a reduction in the queue length ranging from 58% to 77%, and a reduction in vehicle emission levels ranging from 6% to 17%. In the case of the arterial testing, the controller was compared to an optimum fixed-time coordinated plan, an actuated controller, a centralized adaptive phase split controller, a decentralized phase split and cycle length controller, and a fully coordinated adaptive phase split, cycle length, and offset optimization controller to evaluate its performance. On the arterial network, the proposed controller produced reductions in the total delay ranging from 36% to 67%, and a reduction in vehicle emissions ranging from 6% to 13%. Statistical tests show that the proposed controller produces major improvements over other state-of-the-art centralized and de-centralized controllers. In the domain of large scale networks, simulations were conducted on the town of Blacksburg, Virginia composed of 38 signalized intersections. The results show significant reductions on the intersection approaches with travel time savings of 23.6%, a reduction in the average queue length of 37.6%, a reduction in the average number of vehicle stops of 23.6%, a reduction in CO₂ emissions of 10.4%, a reduction in the fuel consumption of 9.8%, and a reduction in NO<sub>X<\sub> emissions of 5.4%. In addition, the proposed controller was tested on downtown Los Angles, California, including the most congested downtown area, which has 457 signalized intersections, and compared to the performance of a decentralized phase split and cycle length controller. The results show significant reductions on the intersections links in the average travel time of 35.1%, a reduction in the average queue length of 54.7%, a reduction in the average number of stops of 44%, a reduction in CO₂ emissions of 10%, a reduction in the fuel consumption of 10%, and a reduction in NO<sub>X<\sub> emissions of 11.7%. Furthermore, simulations were conducted at lower traffic flow levels and showed significant reductions on the network performance producing reductions in vehicle average total delay of 36.7%, a reduction in the stopped delay by 90.2%, and a reduction in the average number of stops by 35%, over a decentralized phase split and cycle length controller. The results demonstrate that the proposed decentralized controller reduces traffic congestion, fuel consumption and vehicle emission levels, and produces major improvements over other state-of-the-art centralized and de-centralized controllers.

Traffic signals are one of the vital control elements of traffic management and control systems under purview of Departments of Transportation (DOTs) nationwide. They directly affect mobility, safety, and environmental parameters of the transportation networks. Traffic engineers in DOTs often face pressure for extracting additional benefits from existing signal control equipment, influenced by evident increase in demand and changing traffic patterns. However, they often face difficulties, usually from the maturity of the field equipment, lack of understanding of currently available equipment capabilities, and multitude of market available equipment. Besides issues in everyday operation, the need for improved decision-making process appears during selection and implementation of the future signal-control subsystems. This thesis is focusing on the issues related with the need for extracting additional benefits and improved planning of signal-control equipment deployment. Presented are several methodologies and techniques for modeling and assessing traffic signal controllers and supporting communication infrastructure. Techniques presented in this thesis include Petri Net modeling language, Software-in-the-loop simulation, and Geographical Information Systems. Specific capabilities of listed techniques are coordinated for maximizing their benefits in addressing specific issues. The intended positive effects reflect in enhanced comprehension, numerical representation, and analysis of state-of-the-art signal control subsystems in focus. Frameworks, methodologies, and example cases are presented for each of the specific issues in identified traffic signal subsystems, along with recommendations for further research. / Master of Science

One of the most significant urban transport problems is traffic congestion. All major cities both in developed and developing countries are facing the problem due to increasing travel demand caused by increasing urbanization and the attendant economic and population growth. Recognizing the growing burden of traffic congestion, community leaders and transportation planners in Addis Ababa are still actively promoting large-scale road constructions to alleviate traffic congestion. Although Intelligent Transportation Systems(ITS) applications seem to have the potential to improve signalization performance, highly congested intersections in Addis Ababa are still controlled by a timed signal and manual operation. Moreover, these pre-timed signal controls are functioning sub-optimally as they are not being regularly monitored and updated to cope with varying traffic demands. Even though the benefits are well known theoretically, at the time of writing of this thesis, Adaptive Traffic Signal Controllers (ATSC) haven’t been deployed in Ethiopia and no research has been conducted to demonstrate and quantify their effectiveness. This master’s research thesis, therefore, intends to fill the identified gap, by undertaking a microscopic traffic simulation investigation, to evaluate the benefits of adopting a Traffic-responsive Urban Control (TUC) strategy and optimizing traffic signal timings. For the purpose of this study, an oversaturated three-intersection test corridor located in the heart of Addis Ababa city is modeled in VISSIM using real-world traffic data. After validating the calibrated model, the corridor was evaluated with the existing pre-timed, TRANSYT optimized pre-timed plan and TUC strategy. Multiple simulation runs were then made for each scenario alternatives and various measures of effectiveness were considered in the evaluation process. Simulation evaluation has demonstrated an average delay reduction of 24.17% when the existing pre-timed alternative is compared to TRANSYT optimized plan and 35% when compared to the TUC strategy. Overall evaluation results indicate that deploying the TUC strategy and optimizing the aging pre-timed signal plans exhibits a significant flow improvement. It is expected that the result of the thesis work will be an input for future comprehensive policy development processes.