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

Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service