| dc.description.abstract | The volatile world economy has greatly affected fuel prices, while pollution and gas emissions are increasing to negatively impact global warming. Rising fuel costs have made drivers more concerned about how much of their monthly budgets are allocated for gasoline. In terms of the air pollution problem, greenhouse gas (GHG) emissions from vehicles are considered to be one of the main contributing sources. Carbon dioxide (CO₂) is the largest component of GHG emissions. As a result, it is important to develop and implement effective strategies to reduce fuel expenditure and prevent the expected increase of CO₂ emission from vehicles.
Vehicular networks offer a promising approach that can be applied in transportation systems to reduce fuel consumption and emissions. One of the major applications of vehicular networks is intelligent transportation systems (ITS). To exchange and distribute messages, geocast routing protocols have been proposed for ITS applications. Most of these protocols focus on improving network-centric performance measures (e.g., message delay, packet delivery ratio, etc.) instead of focusing on improving the performance measures that are meaningful to both the scientific community and the general public (e.g., fuel consumption and CO₂ emission).
Stop-and-go conditions, high acceleration, and unnecessary speed are uneconomical and environmentally unfriendly (UEU) actions that increase the amount of vehicle fuel consumed and the CO₂ emission. These actions can happen frequently for vehicles approaching a traffic light signal (TLS). This thesis proposes a new protocol named Economical and Environmentally Friendly Geocast (EEFG), which focuses on minimizing CO₂ emission and fuel consumption from vehicles approaching a TLS. The goal of this protocol is to deliver useful information to approaching vehicles inside the regions of interest (ROIs). Based on the information sent, the vehicle receiving the message adapts its speed to a recommended speed (Sʀ), which helps the vehicle reduce its UEU actions.
To determine the value of Sʀ, a comprehensive optimization model that is applicable in both vehicle-to-vehicle (V2V) communication and traffic light signal-to-vehicle (TLS2V) communication is developed. The objective function is to minimize fuel consumption by and emissions from vehicles. The speed that can achieve this goal is the optimum Sʀ
(Sʀ*). The thesis also proposes efficient heuristic expressions to compute the optimum or near-optimum value of Sʀ.
An extensive performance study of the EEFG protocol is performed. It shows the impact of using EEFG in a modeled real-world network for urban and suburban areas in the city of Waterloo, Ontario, Canada. Four case studies have been considered: (1) a suburban environment at the maximum traffic volume hour of the day; (2) a suburban environment at the minimum traffic volume hour of the day; (3) an urban environment at the maximum traffic volume hour of the day; (4) an urban environment at the minimum traffic volume hour of the day. The results show that EEFG saves fuel and CO₂ emission in all four cases. In addition, the thesis studies the effect of communication parameters (e.g., transmission range, packet delay, and packet dropping rate) on vehicle fuel consumption
and CO₂ emission. Having high transmission range, low packet delay, and low packet dropping rate, can save more fuel and CO₂ emission. | en |
