Transportation related activities has substantially increased the mobility of people and goods, but also brings impacts to the environment, such as greenhouse gas (GHG) emissions, air pollution, energy consumption, and health problems. This growing energy consumption and emission crisis has drawn tremendous attention from the public agencies, industry and researchers. Different solutions have been proposed to reduce the energy consumption from the transportation sector. For example, alternative energy sources, such as electricity and hydrogen, have been proposed as more energy-efficient and environmental-friendly fuel sources. In addition to eco-friendlier fuel sources, wireless communication and automated driving technology can also be applied to develop connected eco-friendly transportation systems. Connected and automated vehicles (CAVs) have shown the capability to improve traffic mobility and energy efficiency via vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication. Among all CAV applications, Eco Approach and Departure (EAD) at Signalized Intersections is particularly promising for fuel saving and emission reduction in urban area, as drivers would effectively reduce stops and idling and avoid unnecessary acceleration and deceleration by receiving signal timing information in advance. In real-world traffic, signal timing and traffic conditions are quite dynamic and uncertain in the scope of different road networks. Due to the existence of different varieties of vehicle classes and vehicle engine types, the algorithm developed for eco-driving also needs to be powertrain-specific. In this dissertation, advanced eco-trajectory planning algorithms have been developed to address four challenges: 1) Driving in urban areas with uncertain queue conditions due to the limitation of the communication and sensing range; 2) Developing an adaptive Eco-Approach and Departure (EAD) strategy to minimize the expected energy consumption when passing an actuated signalized intersection; 3) Extending the intersection-based eco-driving algorithm to corridor level for global optimal performance; and 4) Customizing eco-driving algorithms to other vehicles, such as electric trucks, under different traffic volumes and vehicle fleet mix.
