A Bayesian analysis of the minimal model was proposed where both glucose and insulin were analyzed simultaneously under the insulin-modified intravenous glucose tolerance test (IVGTT). The resulting model was implemen...A Bayesian analysis of the minimal model was proposed where both glucose and insulin were analyzed simultaneously under the insulin-modified intravenous glucose tolerance test (IVGTT). The resulting model was implemented with a nonlinear mixed-effects modeling setup using ordinary differential equations (ODEs), which leads to precise estimation of population parameters by separating the inter- and intra-individual variability. The results indicated that the Bayesian method applied to the glucose-insulin minimal model provided a satisfactory solution with accurate parameter estimates which were numerically stable since the Bayesian method did not require approximation by linearization.展开更多
Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human activity.Taxi-GPS traces,acted as one kind of typical crowd sensed data,it can be used ...Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human activity.Taxi-GPS traces,acted as one kind of typical crowd sensed data,it can be used to mine the semantics of OD flows.In this paper,we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China.The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows.Then based on a novel complex network model,a semantics mining method of OD flows is proposed through compounding Points of Interests(POI)network and public transport network to the OD flows network.The propose method would offer a novel way to predict the location characteristic and future traffic conditions accurately.展开更多
交通智能(IC)卡可以记录居民的移动出行,反映居民的源-目的地(OD)信息;但智能卡记录的OD流数据规模大,直接可视化空间分布容易导致视觉杂乱,并且多元数据类型多,更难以和流数据结合对比分析。首先,针对直接可视化大规模OD数据的空间分...交通智能(IC)卡可以记录居民的移动出行,反映居民的源-目的地(OD)信息;但智能卡记录的OD流数据规模大,直接可视化空间分布容易导致视觉杂乱,并且多元数据类型多,更难以和流数据结合对比分析。首先,针对直接可视化大规模OD数据的空间分布容易视觉遮挡的问题,提出基于正交非负矩阵分解(ONMF)的流聚类方法。所提方法对源-目的地数据聚类后再可视化,可以减少不必要的遮挡。然后,针对多元时空数据类型多难以结合对比分析的问题,设计了公交站点多元时序数据视图。该可视化方法将公交站点的流量大小和空气质量、空气温度、相对湿度、降雨量这四类多元数据在同一时间序列上编码,提高了视图的空间利用率并且可以对比分析。再次,为了辅助用户探索分析,开发了基于OD流和多元数据的交互式可视分析系统,并设计了多种交互操作提升用户探索效率。最后,基于新加坡交通智能卡数据集,从聚类效果和运行时间对该聚类方法评估。结果显示,在用轮廓系数评估聚类效果上,所提方法比原始方法提升了0.028,比用K均值聚类方法提升了0.253;在运行时间上比聚类效果较好的ONMFS(ONMF through Subspace exploration)方法少了254 s。通过案例分析和系统功能对比验证了系统的有效性。展开更多
Accurate demand forecasting for online ride-hailing contributes to balancing traffic supply and demand,and improving the service level of ride-hailing platforms.In contrast to previous studies,which have primarily foc...Accurate demand forecasting for online ride-hailing contributes to balancing traffic supply and demand,and improving the service level of ride-hailing platforms.In contrast to previous studies,which have primarily focused on the inflow or outflow demands of each zone,this study proposes a conditional generative adversarial network with a Wasserstein divergence objective(CWGAN-div)to predict ride-hailing origin-destination(OD)demand matrices.Residual blocks and refined loss functions help to enhance the stability of model training.Interpretable conditional information is employed to capture external spatiotemporal dependencies and guide the model towards generating more precise results.Empirical analysis using ride-hailing data from Manhattan,New York City,demon-strates that our proposed CWGAN-div model can effectively predict the network-wide OD matrix and exhibits strong convergence performance.Comparative experiments also show that the CWGAN-div outperforms other benchmarking methods.Consequently,the proposed model displays potential for network-wide ride-hailing OD demand prediction.展开更多
文摘A Bayesian analysis of the minimal model was proposed where both glucose and insulin were analyzed simultaneously under the insulin-modified intravenous glucose tolerance test (IVGTT). The resulting model was implemented with a nonlinear mixed-effects modeling setup using ordinary differential equations (ODEs), which leads to precise estimation of population parameters by separating the inter- and intra-individual variability. The results indicated that the Bayesian method applied to the glucose-insulin minimal model provided a satisfactory solution with accurate parameter estimates which were numerically stable since the Bayesian method did not require approximation by linearization.
基金Acknowledgment The research work was financially supported both by the Natural Science Foundation of China （51178164） and the Priority Discipline Foundation of Henan Province （507909）.
基金This work is supported by Shandong Provincial Natural Science Foundation,China under Grant No.ZR2017MG011This work is also supported by Key Research and Development Program in Shandong Provincial(2017GGX90103).
文摘Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human activity.Taxi-GPS traces,acted as one kind of typical crowd sensed data,it can be used to mine the semantics of OD flows.In this paper,we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China.The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows.Then based on a novel complex network model,a semantics mining method of OD flows is proposed through compounding Points of Interests(POI)network and public transport network to the OD flows network.The propose method would offer a novel way to predict the location characteristic and future traffic conditions accurately.
文摘交通智能(IC)卡可以记录居民的移动出行,反映居民的源-目的地(OD)信息;但智能卡记录的OD流数据规模大,直接可视化空间分布容易导致视觉杂乱,并且多元数据类型多,更难以和流数据结合对比分析。首先,针对直接可视化大规模OD数据的空间分布容易视觉遮挡的问题,提出基于正交非负矩阵分解(ONMF)的流聚类方法。所提方法对源-目的地数据聚类后再可视化,可以减少不必要的遮挡。然后,针对多元时空数据类型多难以结合对比分析的问题,设计了公交站点多元时序数据视图。该可视化方法将公交站点的流量大小和空气质量、空气温度、相对湿度、降雨量这四类多元数据在同一时间序列上编码,提高了视图的空间利用率并且可以对比分析。再次,为了辅助用户探索分析,开发了基于OD流和多元数据的交互式可视分析系统,并设计了多种交互操作提升用户探索效率。最后,基于新加坡交通智能卡数据集,从聚类效果和运行时间对该聚类方法评估。结果显示,在用轮廓系数评估聚类效果上,所提方法比原始方法提升了0.028,比用K均值聚类方法提升了0.253;在运行时间上比聚类效果较好的ONMFS(ONMF through Subspace exploration)方法少了254 s。通过案例分析和系统功能对比验证了系统的有效性。
基金supported by the National Natural Science Foundation of China(Grant No.72371251)the National Science Foundation for Distinguished Young Scholars of Hunan Province(Grant No.2024JJ2080)+1 种基金the Excellent Youth Foundation of Hunan Education Department(Grant No.21B0015)the State Key Lab-oratory of Rail Traffic Control and Safety of Beijing Jiaotong Uni-v ersity,China(Gr ant No.RCS2022K004).
文摘Accurate demand forecasting for online ride-hailing contributes to balancing traffic supply and demand,and improving the service level of ride-hailing platforms.In contrast to previous studies,which have primarily focused on the inflow or outflow demands of each zone,this study proposes a conditional generative adversarial network with a Wasserstein divergence objective(CWGAN-div)to predict ride-hailing origin-destination(OD)demand matrices.Residual blocks and refined loss functions help to enhance the stability of model training.Interpretable conditional information is employed to capture external spatiotemporal dependencies and guide the model towards generating more precise results.Empirical analysis using ride-hailing data from Manhattan,New York City,demon-strates that our proposed CWGAN-div model can effectively predict the network-wide OD matrix and exhibits strong convergence performance.Comparative experiments also show that the CWGAN-div outperforms other benchmarking methods.Consequently,the proposed model displays potential for network-wide ride-hailing OD demand prediction.
