This paper investigates human mobility patterns in an urban taxi transportation system. This work focuses on predicting human mobility from discovering patterns of in the number of passenger pick-ups quantity （PUQ） ...This paper investigates human mobility patterns in an urban taxi transportation system. This work focuses on predicting human mobility from discovering patterns of in the number of passenger pick-ups quantity （PUQ） from urban hotspots. This paper proposes an improved ARIMA based prediction method to forecast the spatial-temporal variation of passengers in a hotspot. Evaluation with a large-scale real- world data set of 4 000 taxis＇ GPS traces over one year shows a prediction error of only 5.8%. We also explore the applica- tion of the pl^di^fioti approach to help drivers find their next passetlgerS, The sinatllation results using historical real-world data demonstrate that, with our guidance, drivers can reduce the time taken and distance travelled, to find their next pas- senger＋ by 37.1% and 6.4% respectively,展开更多
Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful ...Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average（ARIMA） model for time series analysis was designed in this study. Eighty-four-month（from January 2009 to December 2015） retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination（R^2）, normalized Bayesian Information Criterion（BIC） and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as（1,0,1）（0,1,1）12, with the largest coefficient of determination（R^2=0.743） and lowest normalized BIC（BIC=3.645） value. The residuals of the model also showed non-significant autocorrelations（P_（Box-Ljung（Q））=0.299）. The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.展开更多
轨道几何尺寸数据是在对被测轨道进行检查时得到的,而不同时间的历史数据,由于检查环境和条件存在变动,其数据表现经常伴随着累积里程误差的存在,导致数据存在无法对齐的现象,从而不能精准预测轨道不平顺的发展。针对此问题,提出将多组...轨道几何尺寸数据是在对被测轨道进行检查时得到的,而不同时间的历史数据,由于检查环境和条件存在变动,其数据表现经常伴随着累积里程误差的存在,导致数据存在无法对齐的现象,从而不能精准预测轨道不平顺的发展。针对此问题,提出将多组原始数据依次以某一步长进行分段验证,以互相关函数相互进行评价,将各组原始数据的里程对齐之后得到有效的观测值。以广铁集团惠州工务段杭深线潮汕站4道K1317+150-K1317+350间的2013-2015年度的历史数据作为试验样本,通过建立自回归积分滑动平均模型(auto-regressive integrated moving average model,简称ARIMA)预测轨道不平顺。结果表明,将轨道几何尺寸原始数据对齐后再进行其不平顺状态的预测研究,可以达到更高的试验精度,其相对误差绝对值的最大值小于5%,样本中相对误差均值为1.75%,适用于工程。展开更多
文摘This paper investigates human mobility patterns in an urban taxi transportation system. This work focuses on predicting human mobility from discovering patterns of in the number of passenger pick-ups quantity （PUQ） from urban hotspots. This paper proposes an improved ARIMA based prediction method to forecast the spatial-temporal variation of passengers in a hotspot. Evaluation with a large-scale real- world data set of 4 000 taxis＇ GPS traces over one year shows a prediction error of only 5.8%. We also explore the applica- tion of the pl^di^fioti approach to help drivers find their next passetlgerS, The sinatllation results using historical real-world data demonstrate that, with our guidance, drivers can reduce the time taken and distance travelled, to find their next pas- senger＋ by 37.1% and 6.4% respectively,
基金financially supported by the Health and Family Planning Commission of Hubei Province(No.WJ2017F047)the Health and Family Planning Commission of Wuhan(No.WG17D05)
文摘Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average（ARIMA） model for time series analysis was designed in this study. Eighty-four-month（from January 2009 to December 2015） retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination（R^2）, normalized Bayesian Information Criterion（BIC） and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as（1,0,1）（0,1,1）12, with the largest coefficient of determination（R^2=0.743） and lowest normalized BIC（BIC=3.645） value. The residuals of the model also showed non-significant autocorrelations（P_（Box-Ljung（Q））=0.299）. The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.
文摘轨道几何尺寸数据是在对被测轨道进行检查时得到的,而不同时间的历史数据,由于检查环境和条件存在变动,其数据表现经常伴随着累积里程误差的存在,导致数据存在无法对齐的现象,从而不能精准预测轨道不平顺的发展。针对此问题,提出将多组原始数据依次以某一步长进行分段验证,以互相关函数相互进行评价,将各组原始数据的里程对齐之后得到有效的观测值。以广铁集团惠州工务段杭深线潮汕站4道K1317+150-K1317+350间的2013-2015年度的历史数据作为试验样本,通过建立自回归积分滑动平均模型(auto-regressive integrated moving average model,简称ARIMA)预测轨道不平顺。结果表明,将轨道几何尺寸原始数据对齐后再进行其不平顺状态的预测研究,可以达到更高的试验精度,其相对误差绝对值的最大值小于5%,样本中相对误差均值为1.75%,适用于工程。
