Ride-hailing(e.g.,DiDi andUber)has become an important tool formodern urban mobility.To improve the utilization efficiency of ride-hailing vehicles,a novel query method,called Approachable k-nearest neighbor(A-kNN),ha...Ride-hailing(e.g.,DiDi andUber)has become an important tool formodern urban mobility.To improve the utilization efficiency of ride-hailing vehicles,a novel query method,called Approachable k-nearest neighbor(A-kNN),has recently been proposed in the industry.Unlike traditional kNN queries,A-kNN considers not only the road network distance but also the availability status of vehicles.In this context,even vehicles with passengers can still be considered potential candidates for dispatch if their destinations are near the requester’s location.The V-Treebased query method,due to its structural characteristics,is capable of efficiently finding k-nearest moving objects within a road network.It is a currently popular query solution in ride-hailing services.However,when vertices to be queried are close in the graph but distant in the index,the V-Tree-based method necessitates the traversal of numerous irrelevant subgraphs,which makes its processing of A-kNN queries less efficient.To address this issue,we optimize the V-Tree-based method and propose a novel index structure,the Path-Accelerated V-Tree(PAV-Tree),to improve query performance by introducing shortcuts.Leveraging this index,we introduce a novel query optimization algorithm,PAVA-kNN,specifically designed to processA-kNNqueries efficiently.Experimental results showthat PAV-A-kNNachieves query times up to 2.2–15 times faster than baseline methods,with microsecond-level latency.展开更多
应用经硅烷偶联处理后的纳米氧化镁(MgO)粉末与低密度聚乙烯(low density polyethylene,LDPE)共混,制得MgO/LDPE复合介质。高成分衬度扫描电镜(scanningelectron microscope,SEM)中图像表明,粒径为100 nm左右的MgO纳米粒子均匀的分散于...应用经硅烷偶联处理后的纳米氧化镁(MgO)粉末与低密度聚乙烯(low density polyethylene,LDPE)共混,制得MgO/LDPE复合介质。高成分衬度扫描电镜(scanningelectron microscope,SEM)中图像表明,粒径为100 nm左右的MgO纳米粒子均匀的分散于介质中。通过电声脉冲法(pulsed electro-acoustic,PEA)测试发现,当纳米MgO填料的质量分数为4%时,可以有效抑制空间电荷的注入,伏安特性的实验结果表明,复合介质拥有更高的空间电荷注入阈值场强。通过电树枝实验,发现复合介质可以抑制电树枝的引发和生长。最后,对实验结果进行了分析,探讨了纳米复合介质抑制空间电荷和树枝化生长的机制。纳米颗粒与基体材料界面电荷行为可能是复合介质电学性能改善的原因。展开更多
基金supported by the Special Project of Henan Provincial Key Research,Development and Promotion(Key Science and Technology Program)under Grant 252102210154in part by the National Natural Science Foundation of China under Grant 62403437.
文摘Ride-hailing(e.g.,DiDi andUber)has become an important tool formodern urban mobility.To improve the utilization efficiency of ride-hailing vehicles,a novel query method,called Approachable k-nearest neighbor(A-kNN),has recently been proposed in the industry.Unlike traditional kNN queries,A-kNN considers not only the road network distance but also the availability status of vehicles.In this context,even vehicles with passengers can still be considered potential candidates for dispatch if their destinations are near the requester’s location.The V-Treebased query method,due to its structural characteristics,is capable of efficiently finding k-nearest moving objects within a road network.It is a currently popular query solution in ride-hailing services.However,when vertices to be queried are close in the graph but distant in the index,the V-Tree-based method necessitates the traversal of numerous irrelevant subgraphs,which makes its processing of A-kNN queries less efficient.To address this issue,we optimize the V-Tree-based method and propose a novel index structure,the Path-Accelerated V-Tree(PAV-Tree),to improve query performance by introducing shortcuts.Leveraging this index,we introduce a novel query optimization algorithm,PAVA-kNN,specifically designed to processA-kNNqueries efficiently.Experimental results showthat PAV-A-kNNachieves query times up to 2.2–15 times faster than baseline methods,with microsecond-level latency.
