摘要
交通事件是引发道路交通拥堵的主要因素之一,通过实时交通诱导等手段可以降低其对交通运行造成的影响,而及时准确地预测事件持续时间则是实现有效管控的前提条件。基于MIT打分函数,融合自上而下的网络生长规则,引入蚁群算法寻找最优网络结构,即以S-ACOB算法为核心搭建最优贝叶斯网络模型。增加了节点随机选择机制及局部结构概率选择模式,降低局部最优结果生成概率,确保贝叶斯网络的健壮性。通过实例验证及对比分析,针对观测节点属性完备和缺失的情况,网络模型预测精度分别为76.97%和93.23%,平均预测精度可达87.82%,证明该模型可以有效地预测交通事件持续时间。
Traffic incident is one of the main factors that lead to traffic congestions.Through controlling methods such as real-time traffic guidance,its impacts on traffic operation can be reduced.Accurately prediction of traffic congestion duration is a prerequisite for effective traffic control.Based on MIT scoring functions,an S-ACOB algorithm as the core of the Bayesian network model is developed.The networks are generated from top to bottom with an ant colony algorithm searching for the optimal network structure.To increase the robustness of the proposed Bayesian network,a random selection mechanism for the nodes and a partial probabilistic selection model for the local structure are introduced.Through an empirical study and comparative analyses,the average precision is up to 87.82%,which is superior to the alternatives reported in the previous research.regarding those nods with the complete and incomplete node properties,the accuracy of the network prediction model is up to 76.97% and 93.23%.The results show that this model can effectively predict the duration of traffic congestions.
出处
《交通信息与安全》
2015年第6期65-71,共7页
Journal of Transport Information and Safety
基金
国家自然科学基金项目(批准号:71210001)资助
