| Abstract |
The transportation sector is the largest producer of greenhouse gas (GHG) emissions in the United States. Energy-optimal algorithms are proposed to reduce the transportation sector's fuel consumption and emissions. These algorithms optimize vehicles' speed to lower energy consumption and emissions. However, recent studies argued that these algorithms could negatively impact traffic flow, create traffic congestions, and increase fuel consumption on the network-level. To overcome this problem, we propose a collective-energy-optimal adaptive cruise control (collective-ACC). Collective-ACC reduces fuel consumption and emissions by directly optimizing vehicles' trajectories and indirectly by improving traffic flow. Collective-ACC is a bi-objective non-linear integer optimization. This optimization was solved by the Non-dominated Sorting Genetic Algorithm (NSGA-II). Collective-ACC was compared with manual driving and self-centered adaptive cruise control (i.e., conventional energy-optimal adaptive cruise controls (self-centered-ACC)) in a traffic simulation. We found that collective-ACC reduced fuel consumption by up to 49% and 42% compared with manual driving and self-centered-ACC, respectively. Collective-ACC also lowered CO2, CO, NOX, and PMX by up to 54%, 70%, 58%, and 64% from manual driving, respectively. Game theory analyses were conducted to investigate how adopting collective-ACC could impact automakers, consumers, and government agencies. We propose policy and business recommendations to accelerate adopting collective-ACC and maximize its environmental benefits. |