为提高城市公交网络(Urban Public Transit Network,UPTN)的抗毁性,基于复杂网络理论系统性研究了UPTN级联失效。以厦门市公交网络为例,利用改进的Space-L方法构建UPTN,设定网络效率、网络最大连通率和故障站点比率作为网络抗毁性的度...为提高城市公交网络(Urban Public Transit Network,UPTN)的抗毁性,基于复杂网络理论系统性研究了UPTN级联失效。以厦门市公交网络为例,利用改进的Space-L方法构建UPTN,设定网络效率、网络最大连通率和故障站点比率作为网络抗毁性的度量指标,基于非线性负荷-容量的UPTN级联失效模型研究了网络在级联失效下的抗毁性,并对比无级联失效下的网络抗毁性。结果表明:容量参数α的减少或β的增加,对网络抗毁性有着显著的提升;当β处于临界阈值时,小幅增加β,网络抗毁性呈现出突增现象;当α=0.2、β=0.7时,网络具有较强的抗毁性,网络在级联失效下可抵挡283次蓄意攻击、575次随机攻击;有、无级联失效下,网络均对蓄意攻击具有脆弱性,对随机攻击具有稳定性,而考虑级联失效情况下的网络,其脆弱性更为明显。展开更多
To improve the level of active traffic management,a short-term traffic flow prediction model is proposed by combining phase space reconstruction(PSR)and extreme gradient boosting(XGBoost)algorithms.Firstly,the traditi...To improve the level of active traffic management,a short-term traffic flow prediction model is proposed by combining phase space reconstruction(PSR)and extreme gradient boosting(XGBoost)algorithms.Firstly,the traditional data preprocessing method is improved.The new method uses hierarchical clustering to determine the traffic flow state and fills in missing and abnormal data according to different traffic flow states.Secondly,one-dimensional data are mapped into a multidimensional data matrix through PSR,and the time series complex network is used to verify the data reconstruction effect.Finally,the multidimensional data matrix is inputted into the XGBoost model to predict future traffic flow parameters.The experimental results show that the mean square error,average absolute error,and average absolute percentage error of the prediction results of the PSR-XGBoost model are 5.399%,1.632%,and 6.278%,respectively,and the required running time is 17.35 s.Compared with mathematical-statistical models and other machine learning models,the PSR-XGBoost model has clear advantages in multiple predictive indicators,proving its feasibility and superiority in short-term traffic flow prediction.展开更多
文摘为提高城市公交网络(Urban Public Transit Network,UPTN)的抗毁性,基于复杂网络理论系统性研究了UPTN级联失效。以厦门市公交网络为例,利用改进的Space-L方法构建UPTN,设定网络效率、网络最大连通率和故障站点比率作为网络抗毁性的度量指标,基于非线性负荷-容量的UPTN级联失效模型研究了网络在级联失效下的抗毁性,并对比无级联失效下的网络抗毁性。结果表明:容量参数α的减少或β的增加,对网络抗毁性有着显著的提升;当β处于临界阈值时,小幅增加β,网络抗毁性呈现出突增现象;当α=0.2、β=0.7时,网络具有较强的抗毁性,网络在级联失效下可抵挡283次蓄意攻击、575次随机攻击;有、无级联失效下,网络均对蓄意攻击具有脆弱性,对随机攻击具有稳定性,而考虑级联失效情况下的网络,其脆弱性更为明显。
基金The National Natural Science Foundation of China (No.71771019, 71871130, 71971125)the Science and Technology Special Project of Shandong Provincial Public Security Department (No. 37000000015900920210010001,37000000015900920210012001)。
文摘To improve the level of active traffic management,a short-term traffic flow prediction model is proposed by combining phase space reconstruction(PSR)and extreme gradient boosting(XGBoost)algorithms.Firstly,the traditional data preprocessing method is improved.The new method uses hierarchical clustering to determine the traffic flow state and fills in missing and abnormal data according to different traffic flow states.Secondly,one-dimensional data are mapped into a multidimensional data matrix through PSR,and the time series complex network is used to verify the data reconstruction effect.Finally,the multidimensional data matrix is inputted into the XGBoost model to predict future traffic flow parameters.The experimental results show that the mean square error,average absolute error,and average absolute percentage error of the prediction results of the PSR-XGBoost model are 5.399%,1.632%,and 6.278%,respectively,and the required running time is 17.35 s.Compared with mathematical-statistical models and other machine learning models,the PSR-XGBoost model has clear advantages in multiple predictive indicators,proving its feasibility and superiority in short-term traffic flow prediction.
