出租车乘车概率预测的置信规则库推理方法
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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 rulebase 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.
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 rulebase 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.
引文
[1]Arel I,Liu C,Urbanik T,et al.Reinforcement learning-based multi-agent system for network traffic signal control[J].IET Intelligent Transport Systems,2010,4(2):128-135.
[2]Zhang Hesheng,Zhang Yi,Wen Huimin,et al.Estimation approaches of average link travel time using GPS data[J].Journal of Jilin University:Engineering and Technology Edition,2007,37(3):533-537.
[3]Yuan Jing,Zheng Yu,Zhang Liuhang,et al.T-Finder:a recommender system for finding passengers and vacant taxis[J].IEEE Transactions on Knowledge and Data Engineering,2013,25(10):2390-2403.
[4]Jiang Guiyan,Chang Ande,Li Qi,et al.Estimation models for average speed of traffic flow based on GPS data of taxi[J].Journal of Southwest Jiaotong University,2011,46(4):638-644.
[5]Yang Jianbo,Liu Jun,Wang Jin,et al.Belief rule-base inference methodology using the evidential reasoning approach—RIMER[J].IEEE Transaction on Systems,Man,and Cybernetics:Part A Systems and Humans,2006,37(4):569-585.
[6]Cheng Ben,Jiang Jiang,Tan Yuejin,et al.A novel approach for WSo S capability requirement satisfactory degree evaluation using evidential reasoning[J].System Engineering Theory&Practice.2011,31(11):2210-2216.
[7]Yang Longhao,Fu Yanggeng,Wu Yingjie.Structure learning approach of belief rule base for best decision structure[J].Journal of Frontiers of Computer Science and Technology,2014,8(10):1216-1230.
[8]Wu Weikun,Yang Longhao,Fu Yanggeng,et al.Parameters training for belief rule-base using the accelerating of gradient algorithm[J].Journal of Frontiers of Computer Science and Technology,2014,8(8):989-1001.
[9]Yang Jianbo,Xu Dongling.On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty[J].IEEE Transactions on Systems,Man,and Cybernetics:Part A Systems and Humans,2002,32(3):289-304.
[10]Wang Yingming,Yang Jianbo,Xu Dongling.Environmental impact assessment using the evidential reasoning approach[J].European Journal of Operational Research,2006,174(3):1885-1913.
[11]Fu Yanggeng,Yang Longhao,Wu Yingjie.Evidential reasoning approach for solving complex evaluation models[J].Pattern Recognition and Artificial Intelligence,2014,27(4):313-326.
[12]Estkowski R.No Steiner point subdivision simplification is NP-complete[C]//Proceedings of the 10th Canadian Conference Computational Geometry,1998:76-77.
[13]Bresenham J E.Algorithm for computer control of a digital potter[J].IBM Systems Journal,1965,4(1):25-30.
[14]Zhang T Y,Suen C Y.A fast parallel algorithm for thinning digital patterns[J].Communications of the ACM,1984,27(3):236-239.
[15]Yang Jianbo.Rule and utility based evidential reasoning approach for multi-attribute decision analysis under uncertainties[J].European Journal of Operational Research,2001,131(1):31-61.
[2]张和生,张毅,温惠敏,等.利用GPS数据估计路段的平均行程时间[J].吉林大学学报:工学版,2007,37(3):533-537.
[4]姜桂艳,常安德,李琦,等.基于出租车GPS数据的路段平均速度估计模型[J].西南交通大学学报,2011,46(4):638-644.
[6]程贲,姜江,谭跃进,等.基于证据推理的武器装备体系能力需求满足度评估方法[J].系统工程理论与实践,2011,31(11):2210-2216.
[7]杨隆浩,傅仰耿,吴英杰.面向最佳决策结构的置信规则库结构学习方法[J].计算机科学与探索,2014,8(10):1216-1230.
[8]吴伟昆,杨隆浩,傅仰耿,等.基于加速梯度求法的置信规则库参数训练方法[J].计算机科学与探索,2014,8(8):989-1001.
[11]傅仰耿,杨隆浩,吴英杰.面向复杂评价模型的证据推理方法[J].模式识别与人工智能,2014,27(4):313-326.
[2]Zhang Hesheng,Zhang Yi,Wen Huimin,et al.Estimation approaches of average link travel time using GPS data[J].Journal of Jilin University:Engineering and Technology Edition,2007,37(3):533-537.
[3]Yuan Jing,Zheng Yu,Zhang Liuhang,et al.T-Finder:a recommender system for finding passengers and vacant taxis[J].IEEE Transactions on Knowledge and Data Engineering,2013,25(10):2390-2403.
[4]Jiang Guiyan,Chang Ande,Li Qi,et al.Estimation models for average speed of traffic flow based on GPS data of taxi[J].Journal of Southwest Jiaotong University,2011,46(4):638-644.
[5]Yang Jianbo,Liu Jun,Wang Jin,et al.Belief rule-base inference methodology using the evidential reasoning approach—RIMER[J].IEEE Transaction on Systems,Man,and Cybernetics:Part A Systems and Humans,2006,37(4):569-585.
[6]Cheng Ben,Jiang Jiang,Tan Yuejin,et al.A novel approach for WSo S capability requirement satisfactory degree evaluation using evidential reasoning[J].System Engineering Theory&Practice.2011,31(11):2210-2216.
[7]Yang Longhao,Fu Yanggeng,Wu Yingjie.Structure learning approach of belief rule base for best decision structure[J].Journal of Frontiers of Computer Science and Technology,2014,8(10):1216-1230.
[8]Wu Weikun,Yang Longhao,Fu Yanggeng,et al.Parameters training for belief rule-base using the accelerating of gradient algorithm[J].Journal of Frontiers of Computer Science and Technology,2014,8(8):989-1001.
[9]Yang Jianbo,Xu Dongling.On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty[J].IEEE Transactions on Systems,Man,and Cybernetics:Part A Systems and Humans,2002,32(3):289-304.
[10]Wang Yingming,Yang Jianbo,Xu Dongling.Environmental impact assessment using the evidential reasoning approach[J].European Journal of Operational Research,2006,174(3):1885-1913.
[11]Fu Yanggeng,Yang Longhao,Wu Yingjie.Evidential reasoning approach for solving complex evaluation models[J].Pattern Recognition and Artificial Intelligence,2014,27(4):313-326.
[12]Estkowski R.No Steiner point subdivision simplification is NP-complete[C]//Proceedings of the 10th Canadian Conference Computational Geometry,1998:76-77.
[13]Bresenham J E.Algorithm for computer control of a digital potter[J].IBM Systems Journal,1965,4(1):25-30.
[14]Zhang T Y,Suen C Y.A fast parallel algorithm for thinning digital patterns[J].Communications of the ACM,1984,27(3):236-239.
[15]Yang Jianbo.Rule and utility based evidential reasoning approach for multi-attribute decision analysis under uncertainties[J].European Journal of Operational Research,2001,131(1):31-61.
[2]张和生,张毅,温惠敏,等.利用GPS数据估计路段的平均行程时间[J].吉林大学学报:工学版,2007,37(3):533-537.
[4]姜桂艳,常安德,李琦,等.基于出租车GPS数据的路段平均速度估计模型[J].西南交通大学学报,2011,46(4):638-644.
[6]程贲,姜江,谭跃进,等.基于证据推理的武器装备体系能力需求满足度评估方法[J].系统工程理论与实践,2011,31(11):2210-2216.
[7]杨隆浩,傅仰耿,吴英杰.面向最佳决策结构的置信规则库结构学习方法[J].计算机科学与探索,2014,8(10):1216-1230.
[8]吴伟昆,杨隆浩,傅仰耿,等.基于加速梯度求法的置信规则库参数训练方法[J].计算机科学与探索,2014,8(8):989-1001.
[11]傅仰耿,杨隆浩,吴英杰.面向复杂评价模型的证据推理方法[J].模式识别与人工智能,2014,27(4):313-326.