发表机构
Argonne National Laboratory; The University of Tennessee, Knoxville; Texas A&M University（阿贡国家实验室; 田纳西大学诺克斯维尔分校; 德克萨斯农工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究以五家公交机构为案例，采用混合车队优化模型，发现电动公交替代柴油公交多非一对一，线路密度高的机构替代比例更低，建议用替代比例预测车队容量需求以降低成本。
AI 中文摘要
当公交机构考虑公交车队电气化时，了解用电池电动公交车（BEB）替代柴油公交车（DB）的效率与成本至关重要。为评估这一点，本研究采用混合车队优化模型，整合调度、充电及车队配置决策，应用于五个机构：圣莫尼卡的大蓝巴士（BBB）、芝加哥交通局（CTA）、诺克斯维尔地区交通局（KAT）、亚特兰大都会快速交通管理局（MARTA）及曼哈顿大都会运输署（MTA）巴士服务。通过计算电动车队占比、BEB/DB替代比例、公交线路密度及车辆活动时间分配，研究发现优化后的车队仍以电动为主，但车辆替代极少为一对一。平均替代比例范围为CTA的1.101至KAT的1.245，CTA与MTA等公交线路密度较高的网络，每替换一辆柴油巴士所需的替代巴士数量少于MARTA与KAT等低密度网络。尽管这些关系为描述性而非因果性，但非营收车辆活动或可解释差异。本多机构比较将重点从简单电动车队占比转向柴油替代效率，表明公交机构应使用替代比例准确预测额外车队容量需求，避免严格一对一车辆替代的高成本假设。
英文摘要
As transit agencies consider bus fleet electrification, understanding the efficiency and cost of replacing diesel buses (DBs) with battery electric buses (BEBs) is critical. To evaluate this, this study applies a mixed-fleet optimization model, integrating scheduling, charging, and fleet composition decisions, across five agencies: Santa Monica's Big Blue Bus (BBB), the Chicago Transit Authority (CTA), Knoxville Area Transit (KAT), the Metropolitan Atlanta Rapid Transit Authority (MARTA), and Manhattan's Metropolitan Transportation Authority (MTA) bus service. By calculating electric fleet share, the BEB/DB replacement ratio, transit-link density, and vehicle activity-time allocation, the study finds that while optimized fleets remain majority-electric, vehicle substitution is rarely one-to-one. Average replacement ratios range from 1.101 for CTA to 1.245 for KAT, with higher transit-link density networks like CTA and MTA requiring fewer replacement buses per diesel bus displaced than lower-density networks like MARTA and KAT. While these relationships are descriptive rather than causal, non-revenue vehicle activity may help explain the differences. By shifting the focus from simple electric fleet share to diesel replacement efficiency, this multi-agency comparison demonstrates that transit agencies should use the replacement ratio to accurately forecast additional fleet capacity requirements and avoid the costly assumption of strict one-to-one vehicle substitution.
