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Battery-powered electric buses are gaining popularity as an energy-efficient and emission-free alternative for bus fleets. However, battery electric buses continue to struggle with concerns related to their limited driving range and time-consuming recharging processes. Fast-charging technology, which utilizes dwelling time at bus stops or terminals to recharge buses in operation employing high power, can raise battery electric buses to the same level of capability as their diesel counterparts in terms of driving range and operating time. To develop an economical and effective battery electric bus system using fastcharging technology, fast-charging stations must be strategically deployed. Moreover, due to the instability of traffic conditions and travel demands, the energy consumption uncertainty of buses should also be considered. This study addresses the planning problem of fast-charging stations that is inherent in a battery electric bus system in light of the energy consumption uncertainty of buses. A robust optimization model that represents a mixed integer linear program is developed with the objective of minimizing the total implementation cost. The model is then demonstrated using a real-world bus system. The performances of deterministic solutions and robust solutions are compared under a worst-case scenario. The results demonstrate that the proposed robust model can provide an optimal plan for a fast-charging battery electric bus system that is robust against the energy consumption uncertainty of buses. The trade-off between system cost and system robustness is also addressed.

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In recent years, bike share programs have become more popular as they contribute to the move toward sustainable mobility in cities. Electric bike sharing, however, remains in the early stages of development. In contrast to traditional bikes, electric bicycles (e-bikes) provide an extra boost via an electric pedal-assist motor, thereby making it much easier to travel around a city with a hilly terrain, such as Park City, Utah. The Summit Bike Share system in Park City is the nation’s first fully electric bike share system (e-BSS). Based on an analysis of historical trip data of Summit Bike Share, this paper presents the system’s performance experience and evaluates user characteristics and travel behavior. A Poisson regression analysis is performed to investigate the factors that influence the e-bike share usage of this e-BSS. The regression results reveal that weather factors, including temperature and wind speed, significantly affect e-bike share usage. It was also found that weekends, summer months, high population density, proximity to public transit centers, recreational centers, and bike trails positively affect the demand for e-bikes. The findings of this paper can help the operators of Summit Bike Share to better understand their users and their e-BSS, while also providing a guide for other e-bike projects currently in the planning stages.

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