  Affordable public transit services are crucial for communities since they
enable residents to access employment, education, and other services.
Unfortunately, transit services that provide wide coverage tend to suffer from
relatively low utilization, which results in high fuel usage per passenger per
mile, leading to high operating costs and environmental impact. Electric
vehicles (EVs) can reduce energy costs and environmental impact, but most
public transit agencies have to employ them in combination with conventional,
internal-combustion engine vehicles due to the high upfront costs of EVs. To
make the best use of such a mixed fleet of vehicles, transit agencies need to
optimize route assignments and charging schedules, which presents a challenging
problem for large transit networks. We introduce a novel problem formulation to
minimize fuel and electricity use by assigning vehicles to transit trips and
scheduling them for charging, while serving an existing fixed-route transit
schedule. We present an integer program for optimal assignment and scheduling,
and we propose polynomial-time heuristic and meta-heuristic algorithms for
larger networks. We evaluate our algorithms on the public transit service of
Chattanooga, TN using operational data collected from transit vehicles. Our
results show that the proposed algorithms are scalable and can reduce energy
use and, hence, environmental impact and operational costs. For Chattanooga,
the proposed algorithms can save $145,635 in energy costs and 576.7 metric tons
of CO2 emission annually.
