This study evaluates the accuracy of bus energy consumption estimates built on GPS data sampled at low frequencies and collected from standardized transit data feeds. It applies bus travel time forecast models to generate drive cycles for buses on any network reporting standardized data. An energy model is then applied to 33 international bus networks to assess the need to implement battery electric buses (BEBs). Previous work on this topic has focused on aggregating standardized bus GPS data directly to route segments. This study uses trained models to impute speed profiles for routes and networks without requiring the collection of network-specific data beyond that published in standard data feeds. This allows a wider-reaching comparison between agency electrification needs. Sensitivities are tested for key operational factors. Conclusions are then drawn on the adequacy of standardized bus data for energy analysis and practical findings across agencies. The findings suggest that low sampling frequency feed data do not significantly affect energy consumption estimates for BEBs, despite the challenges of modeling vehicle drive cycles. Current battery and charging technology is capable of supporting initial rollouts with unmanaged charging on low-energy scheduling blocks, but is incapable of supporting full electrification. To mitigate peak power costs and meet energy needs under full electrification scenarios, it is essential for agencies to adopt managed charging strategies.
Aemmer et al. (Tue,) studied this question.