Modelling Driver Behaviour at Urban Signalised Intersections Using Logistic Regression and Machine Learning

This study investigated several factors that may influence driver actions throughout the yellow interval at urban signalised intersections. The selected samples include 2,168 observations. Almost 33% of drivers stopped ahead of the stop line, 60% passed the intersection through the yellow interval, and 7% passed after the yellow interval was complete (red light running, RLR violations). Binary logistic regression models showed that the chance of passing went up as vehicle speed went up and down as the gap between the vehicle and the traffic light and green interval went up. The movement type and vehicle position influenced the passing probability, but the vehicle type did not. Moreover, multinomial logistic regression models showed that the legal passing probability declined with the growth in the green time and vehicle distance to the traffic signal. It also increased with the growth in the speed of approaching vehicles. Also, movement type directly affected the chance of legally passing, but vehicle position and type did not. Furthermore, the driver’s performance during the yellow phase was studied using the k-nearest neighbours algorithm (KNN), support vector machines (SVM), random forest (RF) and AdaBoost machine learning techniques. The driver’s action run prediction was the most accurate, and the run-on-red camera was the least accurate

The role of self-efficacy as an attribute of principals’ leadership effectiveness in K-12 private and public institutions in Lebanon

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. The aim of this study is to explore the role of K-12 school principals’ self-efficacy as an attribute for their leadership effectiveness in Lebanon. The Norwegian principal self-efficacy scale (NPSES) instrument was translated into Arabic and used to collect quantitative data from participants. Internal consistency of factors within this study was checked (24 items; α = 0.73). By comparing private and public schools in Lebanon, all located in the governorate of Mount Lebanon, the researchers revealed the extent to which principals’ self-efficacy plays a role in their leadership. In addition, while no statistical difference was found between self-efficacy levels of private and public principals, females reported higher scores on the majority of the dimensions than their male counterparts in both types of schools. This study highlights the importance of the interaction effect of age and gender on self-efficacy levels. Moreover, it offers knowledge and practice to policy makers when recruiting principals or designing training programs. It also suggests the implementation of an in-house mentoring program to create school-school partnerships. Finally, this paper offers a platform for future researchers interested in principal self-efficacy in similar conflict-affected places with high economic depression. Limitations are further mentioned

Lumos: a statewide linkage programme in Australia integrating general practice data to guide system redesign

ObjectiveWith ageing of the Australian population, more people are living longer and experiencing chronic or complex health conditions. The challenge is to have information that supports the integration of services across the continuum of settings and providers, to deliver person-centred, seamless, efficient and effective healthcare. However, in Australia, data are typically siloed within health settings, precluding a comprehensive view of patient journeys. Here, we describe the establishment of the Lumos programme—the first statewide linked data asset across primary care and other settings in Australia and evaluate its representativeness to the census population.Methods and analysisRecords extracted from general practices throughout New South Wales (NSW), Australia’s most populous state, were linked to patient records from acute and other settings. Innovative privacy and security technologies were employed to facilitate ongoing and regular updates. The marginal demographic distributions of the Lumos cohort were compared with the NSW census population by calculating multiple measures of representation to evaluate its generalisability.ResultsThe first Lumos programme data extraction linked 1.3 million patients’ general practice records to other NSW health system data. This represented 16% of the NSW population. The demographic distribution of patients in Lumos was &gt;95% aligned to that of the NSW population in the calculated measures of representativeness.ConclusionThe Lumos programme delivers an enduring, regularly updated data resource, providing unique insights about statewide, cross-setting healthcare utilisation. General practice patients represented in the Lumos data asset are representative of the NSW population overall. Lumos data can reliably be used to identify at-risk regions and groups, to guide the planning and design of health services and to monitor their impact throughout NSW.</jats:sec

The Fukushima Daiichi Accident

The Fukushima Daiichi Accident consists of a Report by the IAEA Director General and five technical volumes. It is the result of an extensive international collaborative effort involving five working groups with about 180 experts from 42 Member States with and without nuclear power programmes and several international bodies. It provides a description of the accident and its causes, evolution and consequences, based on the evaluation of data and information from a large number of sources available at the time of writing. The set contains six printed parts and five supplementary CD-ROMs. Contents: Report by the Director General; Technical Volume 1/5, Description and Context of the Accident; Technical Volume 2/5, Safety Assessment; Technical Volume 3/5, Emergency Preparedness and Response; Technical Volume 4/5, Radiological Consequences; Technical Volume 5/5, Post-accident Recovery; Annexes. The JRC contributed to volumes 1,2 and 3, which are attached.JRC.F.5-Nuclear Reactor Safety Assessmen

Modelling Driver Behaviour at Urban Signalised Intersections Using Logistic Regression and Machine Learning

This study investigated several factors that may influence driver actions throughout the yellow interval at urban signalised intersections. The selected samples include 2,168 observations. Almost 33% of drivers stopped ahead of the stop line, 60% passed the intersection through the yellow interval, and 7% passed after the yellow interval was complete (red light running, RLR violations). Binary logistic regression models showed that the chance of passing went up as vehicle speed went up and down as the gap between the vehicle and the traffic light and green interval went up. The movement type and vehicle position influenced the passing probability, but the vehicle type did not. Moreover, multinomial logistic regression models showed that the legal passing probability declined with the growth in the green time and vehicle distance to the traffic signal. It also increased with the growth in the speed of approaching vehicles. Also, movement type directly affected the chance of legally passing, but vehicle position and type did not. Furthermore, the driver’s performance during the yellow phase was studied using the k-nearest neighbours algorithm (KNN), support vector machines (SVM), random forest (RF) and AdaBoost machine learning techniques. The driver’s action run prediction was the most accurate, and the run-on-red camera was the least accurate

Driver performance through the yellow phase using video cameras at urban signalized intersections

The main objective of this research is to examine the influencing parameters of driver performance through the yellow phase at urban signalized intersections with and without red-light running (RLR) cameras. Data were collected to include the intersection type, vehicle type, turning movement type, whether the vehicle position is in a platoon or not, presence of RLR cameras, green light flash devices, pedestrians, and pavement markings. A total of 2168 driver observations were extracted. Only 33.3% of the drivers stopped before the stop line, 59% of the drivers passed the intersection through the yellow phase, and 7% of the drivers committed RLR violations. The results showed that drivers were more likely to stop before the stop line through the yellow phase at locations with RLR cameras, green light flash devices, pavement markings, where pedestrians were present, and at a four-leg intersection. Chi-square tests indicated that all parameters had a significant impact on driver performance, except for the type of turning movement

Assessing the effectiveness of decision aids for decisionmaking in prostate cancer testing: a systematic review

Background Prostate cancer is a leading disease affecting men worldwide. Conflicting evidence within the literature provides little guidance to men contemplating whether or not to be screened for prostate cancer. This systematic review aimed to determine whether decision aids about early detection of prostate cancer improve patient knowledge and decision making about whether to undergo prostate‐specific antigen testing. Methods Medline, EMBASE, CINAHL, PsychINFO, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects and Health Technology Assessment databases up until March 2014 were searched. All included randomised controlled trials were assessed for methodological quality. Clinical selection and assessment heterogeneity among studies prevented the pooling of data for meta‐analyses. Descriptive analyses of all included studies and comparison were performed. Results A total of 13 randomised controlled trials met the inclusion criteria. Significant heterogeneity was present for the design and implementation of decision aids including comparative interventions and outcomes. Eight studies were of a low methodological quality, with the remaining five of medium quality. Improvements in patient knowledge following use of a decision aid were demonstrated by 11 of the 13 included studies. Seven of 10 studies demonstrated a reduction in decisional conflict/distress. Three of four studies demonstrated no difference between a decision aid and information only in reducing decisional uncertainty. Three of five studies demonstrated an increase in decisional satisfaction with use of a decision aid. Conclusions Decision aids increase patient knowledge and confidence in decision making about prostate cancer testing. Further research into effective methods of implementation is needed