摘要
出租车乘车概率预测中存在数据量级大,底层属性类型多,预测信息不确定的问题。鉴于此,整合大规模轨迹数据范畴中现有的挖掘算法对出租车GPS数据和路网数据进行离线处理;将多类型的不确定性数据转换为具有置信结构的规则形式,并以此构建置信规则库;通过置信规则库推理方法(belief rule-base inference methodology using evidential reasoning,RIMER)在线预测路网道路上各个地点的乘车概率。以北京市2012年11月某天的出租车GPS数据为例说明该在线预测方法的应用。实验结果表明,该预测方法具有较高的实时性和准确性。
Large scale of data, various types of low-level attributes and uncertainty of prediction information exist in probability prediction of taking taxi. To solve these problems, this paper offline deals with the GPS data of taxi and road network data by using mining algorithms in the large-scale trajectory data domain, then builds a belief rule-base by transforming various types of information with uncertainty into rules which are in form of the belief structure, after that uses RIMER (belief rule-base inference methodology using evidential reasoning) to get the final probability of any points on the road network. Finally, the GPS data of Beijing' s taxi in November of 2012 are taken as an example to illustrate the usage of the online prediction method, and the results show the real-time and accuracy of the proposed method.
出处
《计算机科学与探索》
CSCD
北大核心
2015年第8期985-994,共10页
Journal of Frontiers of Computer Science and Technology
基金
国家自然科学基金Nos.71371053
61300026
61300104
福建省自然科学基金No.2015J01248
福建省教育厅科技项目No.JA13036
福州大学科技发展基金项目No.2014-XQ-26
国家级大学生创新创业训练计划项目No.201310386030~~
关键词
概率预测
GPS数据
路网数据
置信规则库
置信规则库推理方法(RIMER)
probability prediction
GPS data
road network data
belief rule-base
belief rule-base inference method- ology using evidential reasoning (RIMER)