To solve the shortest path planning problems on grid-based map efficiently,a novel heuristic path planning approach based on an intelligent swarm optimization method called Multivariant Optimization Algorithm( MOA) an...To solve the shortest path planning problems on grid-based map efficiently,a novel heuristic path planning approach based on an intelligent swarm optimization method called Multivariant Optimization Algorithm( MOA) and a modified indirect encoding scheme are proposed. In MOA,the solution space is iteratively searched through global exploration and local exploitation by intelligent searching individuals,who are named as atoms. MOA is employed to locate the shortest path through iterations of global path planning and local path refinements in the proposed path planning approach. In each iteration,a group of global atoms are employed to perform the global path planning aiming at finding some candidate paths rapidly and then a group of local atoms are allotted to each candidate path for refinement. Further,the traditional indirect encoding scheme is modified to reduce the possibility of constructing an infeasible path from an array. Comparative experiments against two other frequently use intelligent optimization approaches: Genetic Algorithm( GA) and Particle Swarm Optimization( PSO) are conducted on benchmark test problems of varying complexity to evaluate the performance of MOA. The results demonstrate that MOA outperforms GA and PSO in terms of optimality indicated by the length of the located path.展开更多
利用出行特征数据识别综合交通运输通道是合理布局城市群综合运输通道的关键技术。本文基于城市群手机信令数据,提出一种综合运输通道识别四阶段方法框架,即数据准备、运输方式划分、最短路径搜索和通道识别。在运输方式划分方面,提出...利用出行特征数据识别综合交通运输通道是合理布局城市群综合运输通道的关键技术。本文基于城市群手机信令数据,提出一种综合运输通道识别四阶段方法框架,即数据准备、运输方式划分、最短路径搜索和通道识别。在运输方式划分方面,提出一种以运输平均速度和站点POI (Point of Interest)位置为决策变量的高速铁路、普速铁路和公路多方式划分算法。在最短路搜索方面,设计一种基于双向A*算法的最短路径搜索算法。在通道识别方面,基于行政边界划分通道区段并以运输量为综合运输通道区段判别参数。以京津冀城市群为例进行实证分析,结果表明,本文方法能够有效处理城市群手机信令数据,并识别出6条综合运输通道,验证了方法的可行性和准确性。在案例数据下,京津冀城市群公路和铁路的运输量占比分别为81.87%和18.13%,公路的短程运输客流较铁路更多;节假日因素显著提高了综合运输通道的客流量,平均运输量增加62.6%,平均客流周转量提升61.2%。展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.61261007,61002049)the Key Program of Yunnan Natural Science Foundation(Grant No.2013FA008)
文摘To solve the shortest path planning problems on grid-based map efficiently,a novel heuristic path planning approach based on an intelligent swarm optimization method called Multivariant Optimization Algorithm( MOA) and a modified indirect encoding scheme are proposed. In MOA,the solution space is iteratively searched through global exploration and local exploitation by intelligent searching individuals,who are named as atoms. MOA is employed to locate the shortest path through iterations of global path planning and local path refinements in the proposed path planning approach. In each iteration,a group of global atoms are employed to perform the global path planning aiming at finding some candidate paths rapidly and then a group of local atoms are allotted to each candidate path for refinement. Further,the traditional indirect encoding scheme is modified to reduce the possibility of constructing an infeasible path from an array. Comparative experiments against two other frequently use intelligent optimization approaches: Genetic Algorithm( GA) and Particle Swarm Optimization( PSO) are conducted on benchmark test problems of varying complexity to evaluate the performance of MOA. The results demonstrate that MOA outperforms GA and PSO in terms of optimality indicated by the length of the located path.
文摘利用出行特征数据识别综合交通运输通道是合理布局城市群综合运输通道的关键技术。本文基于城市群手机信令数据,提出一种综合运输通道识别四阶段方法框架,即数据准备、运输方式划分、最短路径搜索和通道识别。在运输方式划分方面,提出一种以运输平均速度和站点POI (Point of Interest)位置为决策变量的高速铁路、普速铁路和公路多方式划分算法。在最短路搜索方面,设计一种基于双向A*算法的最短路径搜索算法。在通道识别方面,基于行政边界划分通道区段并以运输量为综合运输通道区段判别参数。以京津冀城市群为例进行实证分析,结果表明,本文方法能够有效处理城市群手机信令数据,并识别出6条综合运输通道,验证了方法的可行性和准确性。在案例数据下,京津冀城市群公路和铁路的运输量占比分别为81.87%和18.13%,公路的短程运输客流较铁路更多;节假日因素显著提高了综合运输通道的客流量,平均运输量增加62.6%,平均客流周转量提升61.2%。
