![]() ![]() The final testing of the algorithm was done in an enhanced Traffic Experimental Simulation tool (eTEXAS) that incorporates the conventional TEXAS model with a new web-service interface as well as connected vehicle message set dictionary. The fuel reduction was high for low volumes and decreased as the traffic volumes increased. Simulation of a calibrated real intersection showed average fuel savings of nearly 30 percent for peak volumes. The results showed how multi-vehicle interaction enhances usability of the system. The proposed system was tested in an agent-based environment developed in MATLAB using the RPA car-following model as well as the Society of Automobile Engineers (SAE) J2735 message set standards for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. The results demonstrated savings of up to 30 percent in fuel consumption within the traffic signalized intersection vicinity. The modeling of the system constitutes a modified state-of-the-art path-finding algorithm within a dynamic programming framework to find near-optimal and near-real-time solutions to a complex non-linear programming problem that involves minimizing vehicle fuel consumption in the vicinity of signalized intersections. The dissertation then presents the algorithmic development of an Eco-Cooperative Adaptive Cruise Control system. Driver–willingness to use advanced in-vehicle technology and cellphone applications is also found to be subjective on what benefits it has to offer and safety and efficiency are found to be in the top list. The results of the survey indicate that user-acceptance to systems that enhance safety and efficiency is ranked high. The dissertation first presents the results from an on-line survey soliciting driver input on public perceptions of in-vehicle assistive devices. In addition to the ECACC presented here, the research also expands on some of the key eco-driving concepts such as fuel-optimizing acceleration models, which could be used in conjunction with conventional vehicles and even autonomous vehicles, or assistive systems that are being implemented in vehicles. This fuel-efficient cruise control system is known as an Eco-Cooperative Adaptive Cruise Control (ECACC) system. The research proposed in this dissertation uses this information and advanced computational models to develop fuel-efficient vehicle trajectories, which can either be used as guidance for drivers or can be attached to an electronic throttle controlled cruise control system. For example, information on traffic signal changes, traffic slow-downs and even headway and speed of lead vehicles can be shared. The connectivity between vehicles and infrastructure, as achieved through Connected Vehicles technology, can provide a vehicle with information that was not possible before. Furthermore, the majority of these models do not capture vehicle acceleration and deceleration limitations in addition to vehicle-to-vehicle interactions as constraints within the mathematical program. number of vehicle stops, time spent accelerating and decelerating, and/or acceleration or deceleration levels) in the objective function and fail to explicitly optimize vehicle fuel consumption levels. ![]() Existing state-of-the-art approaches, however, only consider surrogate measures (e.g. Recently, researchers have attempted to develop tools that reduce these losses by capitalizing on traffic signal information received via vehicle connectivity with traffic signal controllers. Vehicle stops and speed variations account for a large percentage of vehicle fuel losses especially at signalized intersections.
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