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

Sivagnanam, Amutheezan; Ayman, Afiya; Wilbur, Michael; Pugliese, Philip; Dubey, Abhishek; Lászka, Áron

doi:10.48550/arxiv.2004.05146

Cited by 1 publication

(5 citation statements)

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“…We found these to be optimal configuration based on a grid search of the parameter space. Due to the lack of space, we include these search results in an online appendix (Sivagnanam et al 2020). Figure 2: Energy cost for assignments using simulated annealing ( x) and the greedy algorithm ( +) compared to optimal assignments (found using the integer program).…”

Section: Resultsmentioning

confidence: 99%

“…Solution Quality Next, we evaluate the performance of our algorithms with respect to solution quality, that is, energy cost. Note that we present CO 2 results in an online appendix (Sivagnanam et al 2020). We use the exact same setting as in the previous experiment (Figure 1).…”

Section: Resultsmentioning

confidence: 99%

“…Since this optimization problem is computationally hard (see proof sketch in an online appendix (Sivagnanam et al 2020)), we first present an integer program to find optimal solutions for smaller instances (Section 3.1). Then, we introduce efficient greedy (Section 3.2) and simulated annealing algorithms (Section 3.3), which scale well for larger instances.…”

Section: Algorithmsmentioning

confidence: 99%

“…Then, we introduce efficient greedy (Section 3.2) and simulated annealing algorithms (Section 3.3), which scale well for larger instances. Due to lack of space, we include less important subroutines in the online appendix (Sivagnanam et al 2020).…”

Section: Algorithmsmentioning

confidence: 99%

“…Starting with an empty set A = ∅, the algorithm then iteratively adds assignments (v, t) ∈ V × T to the set, always choosing an assignment with the lowest biased cost BiasedCost(A, v, t, α) (breaking ties arbitrarily). After each iteration, the biased costs E for the chosen vehicle v are updated by Update, which also adds charging slot assignments as necessary (see the online appendix (Sivagnanam et al 2020)). The algorithm terminates once all trips are assigned (or if it fails to find a solution).…”

Section: The Time Complexity Of This Algorithm Is O (|T ∪ C|)mentioning

confidence: 99%

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

Sivagnanam

Ayman

Wilbur

et al. 2021

AAAI

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Algorithmsmentioning

confidence: 99%

Section: Algorithmsmentioning

confidence: 99%

Section: The Time Complexity Of This Algorithm Is O (|T ∪ C|)mentioning

confidence: 99%

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