摘要
针对现有路径诱导算法无法实现多点连续搜索的不足,提出了一种可以满足出行者一次出行中访问多个兴趣点(Point of interest,POI)的ASSA算法(A*-based sequenced search algorithm)。ASSA算法优化了路网搜索结构,大幅地减少了数据访问量,并通过兴趣点近邻区域的时空关联推理,得到最优出行路径。并基于城市POI兴趣点信息,对兴趣点数据进行了分类划分,设计了多规则下的兴趣点访问机制,并对其进行了试验验证。结果表明:相比于NS最近邻算法,提出的ASSA算法可以提高计算性能16%以上,并避免了非最优路径的出现,可以有效地满足出行者不同规则下的兴趣点访问需求。
To overcome the shortcoming that the existing route guidance algorithms can not execute sequenced route search, a new Artificial Searching Swarm Algorithm （ASSA） is developed that can query multi-Point of Interests （POIs） in travel processing. The ASSA can optimize search topological structure of the road-network, which greatly reduces the amount of data access. Thus the optimal path can be presented according to the spatial-time correlation reasoning. Further more, the POI data are categorized based on urban POIs and a method to go through the sequenced POIs under multi-rules is developed. Sensitive experiments were implemented to verify the proposed ASSA. Results show that, compared with NS algorithm, ASSA can improve the computing efficiency by at least 16%, and it can also avoid getting less-than-optimal path. It effectively meets the travelers＇ sequenced travel demand.
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2014年第3期631-636,共6页
Journal of Jilin University:Engineering and Technology Edition
基金
'863'国家高技术研究发展计划项目(2012AA112307)
关键词
交通运输系统工程
城市兴趣点信息
路径诱导
最短路径
出行信息
engineering of communication and transportation system
message of point of interest （POD
route guidance
shortest path
travel information
