计及碳减排的共享电动汽车网点选址优化  

Optimization of shared electric vehicle outlet location considering carbon emission reduction

作　　者：艾合太木·艾斯凯尔 孙小慧[1] 屈蕾 AHTAM·Askar;SUN Xiaohui;QU Lei(College of Civil Engineering and Architecture,Xinjiang University,Urumqi 830017,China;College of Traffic and Transportation Engineering,Xinjiang University,Urumqi 830017,China)

机构地区：[1]新疆大学建筑工程学院,乌鲁木齐830017 [2]新疆大学交通运输工程学院,乌鲁木齐830017

出　　处：《交通科技与经济》2023年第5期33-39,共7页Technology & Economy in Areas of Communications

基　　金：新疆维吾尔自治区自然科学基金项目(2021D01C104)。

摘　　要：为解决共享电动汽车网点选址布局不合理问题,从低碳角度出发,以建设共享电动汽车网点成本、用户出行时间成本及建设共享电动汽车网点碳排放和共享电动汽车路径碳排放最小为目标,以共享电动汽车网点覆盖程度为约束,构建计及碳减排的网点多目标选址规划模型,设计多目标优化算法NSGA-Ⅱ(非支配排序遗传算法),以Chicago Sketch(芝加哥写生)网络为算例求解模型得出网点选址结果,并将其与基于K-means(K均值)的聚类算法网点选址结果进行对比。结果表明:计及碳减排的共享电动汽车网点选址模型在车辆路径碳排放、聚类内部平均距离和聚类间平均距离等指标上表现更好。To solve the problem of unreasonable outlet location of shared electric vehicle,from the perspective of low carbon,a multi-objective outlet location planning model is constructed to minimize the cost of building electric vehicle outlets,users commute time expense,and carbon emissions from outlets and vehicle driving,in strict adherence to the outlet coverage range.The NSGA-Ⅱ(Non-dominated Sorting Genetic Algorithm-Ⅱ)is adopted herein to fulfill the calculation for the model and derive outlet location results based on the Chicago Sketch network as the exemplar,which are then compared to the results provided by the K-means clustering algorithm.The comparison shows that the proposed model outperforms the K-means algorithm in terms of carbon emission reduction,intra-cluster distance,and inter-cluster distance.

关 键 词：交通规划 网点选址 共享电动汽车 碳减排 NSGA-Ⅱ算法 K-MEANS算法 

分 类 号：U491[交通运输工程—交通运输规划与管理]