The American transportation system faces unprecedented challenges today and will face even more significant challenges in the near future. According to Texas A&M’s 2012 Urban Mobility Report, the combined effects of wasting 5.5 billion hours and 2.9 billion gallons of fuel annually, resulted in a total congestion cost of $121 billion in the United States. In addition, according to the National Highway Traffic Safety Administration (NHTSA), approximately 33,000 traffic fatalities occurred in 2014 with an additional estimated 53,000 annual deaths due to transportation-related emissions. Each of these challenges is compounded by the prospect of the U.S. population increasing 44% by 2050. The field of Intelligent Transportation Systems (ITS) can play a major role in meeting the aforementioned challenges and in creating a 21st century transportation system that is safer, more reliable, more efficient, and more sustainable than the existing transportation system, while remaining affordable. A total of three solutions are proposed in this dissertation with varying ability to improve the mobility and environmental sustainability of traffic. The first solution is the application of Eco-Cooperative Adaptive Cruise Control (Eco-CACC) and the associated lane-changing algorithms with Connected Vehicles (CVs) to freeway traffic systems. The second solution is the leveraging of CV technology to provide real-time adaptive signal control for arterial intersections. Finally, the third solution is the development of an eco-friendly city designed around a proposed novel continuous flow intersection.

For arterial roadways, most Active Traffic and Demand Management (ATDM) strategies focus on traffic signal timing optimization at signalized intersections. A critical drawback of conventional traffic signal control strategies is that they rely on measurements from point detection, and estimate traffic states such as queue length based on very limited information. The introduction of Connected Vehicle (CV) technology can potentially address the limitations of point detection via wireless communications to assist signal phase and timing optimization. In this project report, the authors present an agent-based online adaptive signal control (ASC) strategy based on real-time traffic information available from vehicles equipped with CV technology. The authors then evaluate the proposed strategy in terms of travel delay and fuel consumption, relative to a Highway Capacity Manual (HCM) based method in which hourly traffic demand is assumed to be known accurately a priori. The Connected Vehicle Adaptive Signal Control (CV-ASC) strategy is applied to an isolated traffic intersection as well as to a corridor of traffic intersections. The baseline signalization strategy for the corridor of traffic intersections is coordinated signal control. Study results indicate that for both the isolated intersection and corridor contexts, the proposed strategy outperforms the HCM based method and is very robust to traffic demand variations.

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Heavy-duty freight vehicles contribute a disproportionate amount of emissions relative to the national fleet percentage and the relative vehicle miles traveled by heavy-duty freight vehicles. Accordingly, an environmentally-friendly Intelligent Transportation System (ITS) application for improving arterial roadway performance is presented in this report. For arterial roadways, most Active Traffic and Demand Management (ATDM) strategies focus on traffic signal timing optimization at signalized intersections. A critical drawback of conventional traffic signal control strategies is that they rely on measurements from point detection, and estimate traffic states such as queue length based on very limited information. The introduction of Connected Vehicle (CV) technology can potentially address the limitations of point detection via wireless communications to assist signal phase and timing optimization. In this project report, the authors present an agent-based online adaptive signal control (ASC) strategy based on real-time traffic information available from vehicles equipped with CV technology. They then evaluate the proposed strategy in terms of travel delay and energy consumption, relative to a Highway Capacity Manual (HCM) based method in which hourly traffic demand is assumed to be known accurately a priori. This Connected Vehicle Adaptive Signal Control (CV-ASC) strategy has been applied to an isolated traffic intersection as well as to a corridor of traffic intersections. The baseline signalization strategy for the corridor of traffic intersections is coordinated signal control. Study results indicate that for both the isolated intersection and corridor contexts, the proposed strategy outperforms the HCM based method and is very robust to traffic demand variations. The proposed system also provides a framework to flexibly modify signal timing in order to serve evolving localities freight needs.

View the NCST Project Webpage