固定路线混合公交系统能耗最小化研究
Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service
摘要：
负担得起的公共交通服务对社区至关重要，因为它们使居民能够获得就业，教育和其他服务。不幸的是，提供广泛覆盖的运输服务往往遭受相对低的利用率，这导致每英里每乘客的高燃料使用，导致高运营成本和环境影响。电动汽车（EV）可以降低能源成本和环境影响，但由于EV的前期成本很高，大多数公共交通机构不得不将其与传统的内燃机车辆结合使用。为了充分利用这种混合车队，公交机构需要优化路线分配和收费计划，这对大型公交网络来说是一个具有挑战性的问题。我们引入了一个新的问题配方，以尽量减少燃料和电力的使用，通过分配车辆过境旅行和调度他们充电，同时服务于现有的固定路线过境时间表。我们提出了一个整数规划的最优分配和调度，我们提出了多项式时间的启发式和元启发式算法的大型网络。我们评估我们的算法查塔努加，田纳西州的公共交通服务使用从运输车辆收集的运营数据。我们的研究结果表明，所提出的算法是可扩展的，可以减少能源的使用，因此，环境影响和运营成本。对于查塔努加，提出的算法每年可以节省145，635美元的能源成本和576.7公吨的二氧化碳排放量。
英文摘要：
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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