Development of prediction models for freeway incident durations using data mining techniques

The nation's freeway systems are becoming increasingly congested. A major contribution to traffic congestion on freeways is due to traffic incidents. Traffic incidents are non-recurring events such as accidents or stranded vehicles that cause a temporary roadway capacity reduction, and they can account for as much as 60 percent of all traffic congestion on freeways. One major freeway incident management strategy involves diverting traffic to avoid incident locations by relaying timely information through Intelligent Transportation Systems (ITS) devices such as dynamic message signs or real-time traveler information systems. The decision to divert traffic depends foremost on the expected duration of an incident, which is difficult to predict. In addition, the duration of an incident is affected by many contributing factors. Determining and understanding these factors can help the process of identifying and developing better strategies to reduce incident durations and alleviate traffic congestion. A number of research studies have attempted to develop models to predict incident durations, yet with limited success. ^ This dissertation research attempts to improve on this previous effort by applying data mining techniques to a comprehensive incident database maintained by the District 4 ITS Office of the Florida Department of Transportation (FDOT). Two categories of incident duration prediction models were developed: "offline" models designed for use in the performance evaluation of incident management programs, and "online" models for real-time prediction of incident duration to aid in the decision making of traffic diversion in the event of an ongoing incident. Multiple data mining analysis techniques were applied and evaluated in the research. The multiple linear regression analysis and decision tree based method were applied to develop the offline models, and the rule-based method and a tree algorithm called M5P were used to develop the online models. ^ The results show that the models in general can achieve high prediction accuracy within acceptable time intervals of the actual durations. The research also identifies some new contributing factors that have not been examined in past studies. As part of the research effort, software code was developed to implement the models in the existing software system of District 4 FDOT for actual applications. ^

A novel technique for analysing beat-to-beat dynamical changes of QT-RR distribution for arrhythmia prediction

Ventricular tachycardia (VT) leading to ventricular fibrillation (VF) is the major cause of sudden cardiac death (SCD) with subjects with or without any history of cardiac disease. Prediction of the initiation of ventricular fibrillation is crucial for both successful preventive measure and effective defibrillation therapy. A lot of studies have been done based on electrocardiogram (ECG) waveform analysis for VF detection but this field still needs more perfection. Both HRV and QTV related parameters were reported to be analysed for VT/VF detection and prediction with inconsistent results in different populations. In this study, we propose a novel time domain measurement tool to detect the pattern of dynamical changes of both RR and QT intervals in subjects having sustained VT/VF episodes form VFDB and AHA database (www.physionet.org). We also analyse the same pattern in some healthy subjects from Fantasia database and compare the distribution of patterns between healthy and VT/VF subjects. Our findings showed that the distribution of QT-RR dynamics are statistically significantly different (p<0.05) in healthy subjects from VT/VF in particular before the start of VF episode. Therefore, distribution of change in QT-RR dynamics may provide insight of the underlying instability before VF events and can be used for better prediction of arhythmogenesis.

Prediction of adult height in girls: the Beunen-MalinaFreitas method

The purpose of this study was to validate and cross-validate the Beunen-Malina-Freitas method for non-invasive prediction of adult height in girls. A sample of 420 girls aged 10–15 years from the Madeira Growth Study were measured at yearly intervals and then 8 years later. Anthropometric dimensions (lengths, breadths, circumferences, and skinfolds) were measured; skeletal age was assessed using the Tanner-Whitehouse 3 method and menarcheal status (present or absent) was recorded. Adult height was measured and predicted using stepwise, forward, and maximum R2 regression techniques. Multiple correlations, mean differences, standard errors of prediction, and error boundaries were calculated. A sample of the Leuven Longitudinal Twin Study was used to cross-validate the regressions. Age-specific coefficients of determination (R2) between predicted and measured adult height varied between 0.57 and 0.96, while standard errors of prediction varied between 1.1 and 3.9 cm. The cross-validation confirmed the validity of the Beunen-Malina-Freitas method in girls aged 12–15 years, but at lower ages the cross-validation was less consistent. We conclude that the Beunen-Malina-Freitas method is valid for the prediction of adult height in girls aged 12–15 years. It is applicable to European populations or populations of European ancestry.

Prediction of adult height in girls: the Beunen-MalinaFreitas method

The purpose of this study was to validate and cross-validate the Beunen-Malina-Freitas method for non-invasive prediction of adult height in girls. A sample of 420 girls aged 10–15 years from the Madeira Growth Study were measured at yearly intervals and then 8 years later. Anthropometric dimensions (lengths, breadths, circumferences, and skinfolds) were measured; skeletal age was assessed using the Tanner-Whitehouse 3 method and menarcheal status (present or absent) was recorded. Adult height was measured and predicted using stepwise, forward, and maximum R2 regression techniques. Multiple correlations, mean differences, standard errors of prediction, and error boundaries were calculated. A sample of the Leuven Longitudinal Twin Study was used to cross-validate the regressions. Age-specific coefficients of determination (R2) between predicted and measured adult height varied between 0.57 and 0.96, while standard errors of prediction varied between 1.1 and 3.9 cm. The cross-validation confirmed the validity of the Beunen-Malina-Freitas method in girls aged 12–15 years, but at lower ages the cross-validation was less consistent. We conclude that the Beunen-Malina-Freitas method is valid for the prediction of adult height in girls aged 12–15 years. It is applicable to European populations or populations of European ancestry.