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首页> 外文期刊>Transportation Research >Reducing transit fleet emissions through vehicle retrofits, replacements, and usage changes over multiple time periods
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Reducing transit fleet emissions through vehicle retrofits, replacements, and usage changes over multiple time periods

机译：通过在多个时间段内进行车辆改装，更换和使用变化来减少过境车队的排放

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摘要

Bus transit is often promoted as a green form of transportation, but surprisingly little research has been done on how to run transit systems in a green manner. Both vehicle task assignment and purchase models are generally constructed to minimize financial costs. Integrating vehicle task assignment with purchase decisions is made challenging by the different time scales involved. An integer programming approach is used to combine vehicle purchase, retrofit and aggregated task assignment decisions. The formulation is designed to operate in sequence with traditional vehicle task assignment models, to add emissions and long term financial cost elements to the objective, while maintaining computational tractability and feasible input data requirements. In a case study, a transit agency saves money in the long term by using stimulus money to buy CNG infrastructure instead of purchasing only new buses. Carbon prices up to $400/(ton CO_2 equivalent) do not change vehicle purchase decisions, but higher carbon prices can cause more diesel hybrid purchases, at a high marginal cost. Although the motivation and numerical case study are from the US transit industry, the model is formulated to be widely applicable to green fleet management in multiple contexts.
机译：公共汽车过境通常被提倡为绿色运输方式，但是令人惊讶的是，很少有关于如何以绿色方式运行运输系统的研究。车辆任务分配和购买模型通常都构造为使财务成本最小化。将车辆任务分配与购买决策相集成会因所涉及的不同时标而具有挑战性。整数编程方法用于组合车辆购买，改造和汇总任务分配决策。该公式旨在与传统的车辆任务分配模型按顺序进行操作，为目标增加排放量和长期财务成本要素，同时保持计算的可处理性和可行的输入数据要求。在一个案例研究中，公交机构通过使用刺激性资金购买CNG基础设施而不是仅购买新公交车来长期节省资金。高达$ 400 /（吨CO_2当量）的碳价不会改变车辆的购买决定，但更高的碳价会导致更多的柴油混合动力车购买，而边际成本很高。尽管动机和数值案例研究来自美国的过境行业，但该模型被制定为可广泛应用于多种情况下的绿色车队管理。

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