Anticipatory planning for equitable and productive curbside electric vehicle charging stations

被引:4
作者
Guo, Ziyi [1 ,2 ]
Wang, Yan [3 ,4 ]
机构
[1] Univ Florida, Dept Urban & Reg Planning, Coll Design Construct & Planning, 1480 Inner Rd, Gainesville, FL 32601 USA
[2] Univ Florida, Florida Inst Built Environm Resilience, Coll Design Construct & Planning, 1480 Inner Rd, Gainesville, FL 32611 USA
[3] Univ Florida, Dept Urban & Reg Planning, POB 115706, Gainesville, FL 32611 USA
[4] Univ Florida, Florida Inst Built Environm Resilience, POB 115706, Gainesville, FL 32611 USA
基金
美国国家科学基金会;
关键词
Electric vehicle charging station; Access equity; Multi-family housing; Scenario planning; Curbside; LOCATION; INFRASTRUCTURE; OPTIMIZATION; SIMULATION; SELECTION; ADOPTION; MODEL; GIS;
D O I
10.1016/j.scs.2023.104962
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Two challenges to planning public electric vehicle (EV) charging networks remain in U.S. cities, including uneven productivity of charging stations and inequitable charging access across user groups. The unpredictable EV market penetration over the long term further complicates the relevant infrastructure planning. However, the extant planning approaches are limited in addressing both challenges simultaneously when considering future uncertainties. Therefore, we propose a data-driven anticipatory framework to plan for EV charging station allocation near urban amenities based on charging-while-parking behavioral patterns. We focus on two user groups, i.e., multi-family and single-family residents. We compare the productivity-equity outcomes of allocation scenarios under three planning strategies and four possible ratios between both user groups. The framework addresses the incremental charging demands at different market levels for each scenario. An in-depth case study of Alachua County, FL, shows that over-emphasizing multi-family charging demands when placing EV charging stations may undermine their overall productivity. We then suggest three pathways to balance equitable access and optimized productivity for the community based on the comparison of planning scenarios. The proposed framework is generalizable to other EV-initiating communities. This study sheds light on future-oriented adaptive planning for transportation infrastructure during the energy transition.
引用
收藏
页数:13
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