Urban vegetation has been an important indicator for the evaluation of eco-cities, which is of great significance to promote eeo-city construction. We study and discuss the commonly used urban vegetation extrac-tion m...Urban vegetation has been an important indicator for the evaluation of eco-cities, which is of great significance to promote eeo-city construction. We study and discuss the commonly used urban vegetation extrac-tion methods. The extraction of vegetation points in this study is completed through mathematical statistics, mean-square error, successive differences and iterative algorithm which are based on the analysis of different spatial morphological characteristics in urban point clouds. Linyi, a city of Shandong Province in China, is se-lected as the study area to test this method and the result shows that the proposed method has a strong practicali- ty in urban vegetation point cloud extraction. Only 3D coordinate properties of the LiDAR point clouds are used in this method and it does not require additional information, for instance, return intensity, which makes the method more applicable and operable.展开更多
针对外卖配送电动自行车换电柜布局不合理带来的部分换电柜利用率低与部分换电需求得不到及时满足的供需矛盾问题,本文通过聚类POI(Point of Interest)数据确定外卖配送起止点,并通过仿真模拟外卖骑手配送路径预测外卖配送电动自行车换...针对外卖配送电动自行车换电柜布局不合理带来的部分换电柜利用率低与部分换电需求得不到及时满足的供需矛盾问题,本文通过聚类POI(Point of Interest)数据确定外卖配送起止点,并通过仿真模拟外卖骑手配送路径预测外卖配送电动自行车换电需求时空分布,构建换电柜运营商总成本最低和用户满意度最高的多目标换电柜选址定容模型,并以新乡市主城区为例,采用NSGA-II(Non-dominated Sorting Genetic Algorithm II)算法得到换电柜选址定容方案。研究结果表明:仿真模拟得出的换电需求时间分布预测值与实际值基本吻合,换电需求在11:00,14:00,17:00和20:00左右急剧增长,且11:00和14:00左右的换电需求量显著高于17:00和20:00左右的换电需求量,外卖骑手配送路径仿真模拟方法在换电需求预测上具有较高的预测精度;换电柜选址方案不能同时满足运营商和用户利益均为最优,用户满意度的提高需以增加运营商总成本为代价;同时,兼顾运营商和用户利益的新乡市主城区外卖配送电动自行车换电柜最佳建设数量为26,其中,容量为11的换电柜11个,容量为22的换电柜8个,容量为33的换电柜7个;新乡市主城区应按照备选点编号15-7-19-17依次新增换电柜至30个,此时,用户满意度最大,若继续增加换电柜建设数量,只会增加运营商总成本。展开更多
文摘Urban vegetation has been an important indicator for the evaluation of eco-cities, which is of great significance to promote eeo-city construction. We study and discuss the commonly used urban vegetation extrac-tion methods. The extraction of vegetation points in this study is completed through mathematical statistics, mean-square error, successive differences and iterative algorithm which are based on the analysis of different spatial morphological characteristics in urban point clouds. Linyi, a city of Shandong Province in China, is se-lected as the study area to test this method and the result shows that the proposed method has a strong practicali- ty in urban vegetation point cloud extraction. Only 3D coordinate properties of the LiDAR point clouds are used in this method and it does not require additional information, for instance, return intensity, which makes the method more applicable and operable.