  Due to increasing concerns about environmental impact, operating costs, and
energy security, public transit agencies are seeking to reduce their fuel use
by employing electric vehicles (EVs). However, because of the high upfront cost
of EVs, most agencies can afford only mixed fleets of internal-combustion and
electric vehicles. Making the best use of these mixed fleets presents a
challenge for agencies since optimizing the assignment of vehicles to transit
routes, scheduling charging, etc. require accurate predictions of electricity
and fuel use. Recent advances in sensor-based technologies, data analytics, and
machine learning enable remedying this situation; however, to the best of our
knowledge, there exists no framework that would integrate all relevant data
into a route-level prediction model for public transit. In this paper, we
present a novel framework for the data-driven prediction of route-level energy
use for mixed-vehicle transit fleets, which we evaluate using data collected
from the bus fleet of CARTA, the public transit authority of Chattanooga, TN.
We present a data collection and storage framework, which we use to capture
system-level data, including traffic and weather conditions, and high-frequency
vehicle-level data, including location traces, fuel or electricity use, etc. We
present domain-specific methods and algorithms for integrating and cleansing
data from various sources, including street and elevation maps. Finally, we
train and evaluate machine learning models, including deep neural networks,
decision trees, and linear regression, on our integrated dataset. Our results
show that neural networks provide accurate estimates, while other models can
help us discover relations between energy use and factors such as road and
weather conditions.
