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.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/100681 |
| Date | 30 April 2018 |
| Creators | Abdelghaffar, Hossam Mohamed Abdelwahed |
| Contributors | Electrical Engineering, Rakha, Hesham A., Yang, Hao, Abbott, A. Lynn, Woolsey, Craig A., Zeng, Haibo |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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