To improve the forecasting reliability of travel time, the time-varying confidence interval of travel time on arterials is forecasted using an autoregressive integrated moving average and generalized autoregressive co...To improve the forecasting reliability of travel time, the time-varying confidence interval of travel time on arterials is forecasted using an autoregressive integrated moving average and generalized autoregressive conditional heteroskedasticity （ARIMA-GARCH） model. In which, the ARIMA model is used as the mean equation of the GARCH model to model the travel time levels and the GARCH model is used to model the conditional variances of travel time. The proposed method is validated and evaluated using actual traffic flow data collected from the traffic monitoring system of Kunshan city. The evaluation results show that, compared with the conventional ARIMA model, the proposed model cannot significantly improve the forecasting performance of travel time levels but has advantage in travel time volatility forecasting. The proposed model can well capture the travel time heteroskedasticity and forecast the time-varying confidence intervals of travel time which can better reflect the volatility of observed travel times than the fixed confidence interval provided by the ARIMA model.展开更多
Since the 20 thcentury,the time intervals of M ≥6.7 strong earthquakes in the SichuanYunnan region show obvious regularity.Using the years of the strong events,a twodimensional time coordinate system is generated,bas...Since the 20 thcentury,the time intervals of M ≥6.7 strong earthquakes in the SichuanYunnan region show obvious regularity.Using the years of the strong events,a twodimensional time coordinate system is generated,based on which,the time prediction model is constructed for strong earthquakes in the Sichuan-Yunnan region.Prediction analysis shows that there is risk of generating four earthquakes with M ≥ 6.7 in the Sichuan-Yunnan region in the future 16 years,and there are strong signals for M ≥6.7earthquakes for periods 2012-2021 and 2025-2029.The strong earthquakes may occur around 2014-2015,2019 and 2027.展开更多
Using 1200 CPUs of the National Supercomputer TH-A1 and a parallel integral algorithm based on the 3500th-order Taylor expansion and the 4180-digit multiple precision data,we have done a reliable simulation of chaotic...Using 1200 CPUs of the National Supercomputer TH-A1 and a parallel integral algorithm based on the 3500th-order Taylor expansion and the 4180-digit multiple precision data,we have done a reliable simulation of chaotic solution of Lorenz equation in a rather long interval 0 t 10000 LTU(Lorenz time unit).Such a kind of mathematically reliable chaotic simulation has never been reported.It provides us a numerical benchmark for mathematically reliable long-term prediction of chaos.Besides,it also proposes a safe method for mathematically reliable simulations of chaos in a finite but long enough interval.In addition,our very fine simulations suggest that such a kind of mathematically reliable long-term prediction of chaotic solution might have no physical meanings,because the inherent physical micro-level uncertainty due to thermal fluctuation might quickly transfer into macroscopic uncertainty so that trajectories for a long enough time would be essentially uncertain in physics.展开更多
基金The National Natural Science Foundation of China(No.51108079)
文摘To improve the forecasting reliability of travel time, the time-varying confidence interval of travel time on arterials is forecasted using an autoregressive integrated moving average and generalized autoregressive conditional heteroskedasticity （ARIMA-GARCH） model. In which, the ARIMA model is used as the mean equation of the GARCH model to model the travel time levels and the GARCH model is used to model the conditional variances of travel time. The proposed method is validated and evaluated using actual traffic flow data collected from the traffic monitoring system of Kunshan city. The evaluation results show that, compared with the conventional ARIMA model, the proposed model cannot significantly improve the forecasting performance of travel time levels but has advantage in travel time volatility forecasting. The proposed model can well capture the travel time heteroskedasticity and forecast the time-varying confidence intervals of travel time which can better reflect the volatility of observed travel times than the fixed confidence interval provided by the ARIMA model.
文摘Since the 20 thcentury,the time intervals of M ≥6.7 strong earthquakes in the SichuanYunnan region show obvious regularity.Using the years of the strong events,a twodimensional time coordinate system is generated,based on which,the time prediction model is constructed for strong earthquakes in the Sichuan-Yunnan region.Prediction analysis shows that there is risk of generating four earthquakes with M ≥ 6.7 in the Sichuan-Yunnan region in the future 16 years,and there are strong signals for M ≥6.7earthquakes for periods 2012-2021 and 2025-2029.The strong earthquakes may occur around 2014-2015,2019 and 2027.
基金partly supported by National Natural Science Foundation of China (Grant No. 11272209)National Basic Research Program of China (Grant No. 2011CB309704)State Key Laboratory of Ocean Engineering of China (Grant No. GKZD010056).
文摘Using 1200 CPUs of the National Supercomputer TH-A1 and a parallel integral algorithm based on the 3500th-order Taylor expansion and the 4180-digit multiple precision data,we have done a reliable simulation of chaotic solution of Lorenz equation in a rather long interval 0 t 10000 LTU(Lorenz time unit).Such a kind of mathematically reliable chaotic simulation has never been reported.It provides us a numerical benchmark for mathematically reliable long-term prediction of chaos.Besides,it also proposes a safe method for mathematically reliable simulations of chaos in a finite but long enough interval.In addition,our very fine simulations suggest that such a kind of mathematically reliable long-term prediction of chaotic solution might have no physical meanings,because the inherent physical micro-level uncertainty due to thermal fluctuation might quickly transfer into macroscopic uncertainty so that trajectories for a long enough time would be essentially uncertain in physics.
