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This study attempts to establish a quantitative linkage between deployment of dynamic wireless power transfer (DWPT) and the market adoption of plug-in electric vehicles (PEV). This linkage can be useful for analyzing the societal benefits of DWPT and justifying investments in its research, development, demonstration and deployment. Spatial relationships between charging opportunity and DWPT availability are estimated for four metropolitan areas. The consumer value of DWPT is formulated as a function of key DWPT deployment parameters and then integrated into an existing validated consumer choice model, where sales of PEVs are endogenous. Results indicate significant impacts on PEV sales of DWPT deployment, even only at 0.5% of road length by 2050. Significant impact heterogeneity is observed.

Driver is a key component in vehicle simulation. An ideal driver model simulates driving patterns a human driver may perform to negotiate road profiles. There are simulation packages having the capability to simulate driver behavior. However, it is rarely documented how they work with road profiles. This paper proposes a new truck driver model for vehicle simulation to imitate actual driving behavior in negotiating road grade and curvature. The proposed model is developed based upon Gipps' car-following model. Road grade and curvature were not considered in the original Gipps' model although it is based directly on driver behavior and expectancy for vehicles in a stream of traffic. New parameters are introduced to capture drivers' choice of desired speeds that they intend to use in order to negotiating road grade and curvature simultaneously. With the new parameters, the proposed model can emulate behaviors like uphill preparation for different truck drivers.

It is rare for an attempt towards optimization at the fleet-level when consideration is given to the sheer number of seemingly unpredictable interactions among vehicles and infrastructure in congested urban areas. To close the gap, we introduce a simulation based framework to explore the impact of speed synchronization on fuel economy improvement for fleets in traffic. The framework consists of traffic and vehicle modules. The traffic module is used to simulate driver behavior in urban traffic; and the vehicle module is employed to estimate fuel economy. Driving schedule is the linkage between these two modules. To explore the impact, a connected vehicle technology sharing vehicle speed information is used for better fuel economy of a fleet including six vehicles. In all scenarios analyzed, the leading vehicle operates under the EPA Urban Dynamometer Driving Schedule (UDDS), while the other five vehicles follow the leader consecutively.