168 resultados para motor skill


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The current state of the practice in Blackspot Identification (BSI) utilizes safety performance functions based on total crash counts to identify transport system sites with potentially high crash risk. This paper postulates that total crash count variation over a transport network is a result of multiple distinct crash generating processes including geometric characteristics of the road, spatial features of the surrounding environment, and driver behaviour factors. However, these multiple sources are ignored in current modelling methodologies in both trying to explain or predict crash frequencies across sites. Instead, current practice employs models that imply that a single underlying crash generating process exists. The model mis-specification may lead to correlating crashes with the incorrect sources of contributing factors (e.g. concluding a crash is predominately caused by a geometric feature when it is a behavioural issue), which may ultimately lead to inefficient use of public funds and misidentification of true blackspots. This study aims to propose a latent class model consistent with a multiple crash process theory, and to investigate the influence this model has on correctly identifying crash blackspots. We first present the theoretical and corresponding methodological approach in which a Bayesian Latent Class (BLC) model is estimated assuming that crashes arise from two distinct risk generating processes including engineering and unobserved spatial factors. The Bayesian model is used to incorporate prior information about the contribution of each underlying process to the total crash count. The methodology is applied to the state-controlled roads in Queensland, Australia and the results are compared to an Empirical Bayesian Negative Binomial (EB-NB) model. A comparison of goodness of fit measures illustrates significantly improved performance of the proposed model compared to the NB model. The detection of blackspots was also improved when compared to the EB-NB model. In addition, modelling crashes as the result of two fundamentally separate underlying processes reveals more detailed information about unobserved crash causes.

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- Purpose The purpose of this paper is to investigate the current skills gap in both generic and skill areas within the construction industry in Queensland, Australia. - Design/methodology/approach An internet-based survey was administered to collect the opinions of construction employees about the workplace-training environment and their perceptions towards training. The survey intended to address the following research questions, specifically in relation to the construction industry. - Findings The survey results reveal that whilst overall participation in workplace training is high, the current workplace training environments do not foster balanced skill development. The study reveals that in the current absence of a formal and well-balanced training mechanism, construction workers generally resort to their own informal self-development initiatives to develop the needed role-specific theoretical knowledge. - Research limitations/implications The findings of the research are based on the data primarily collected in the construction industry in Queensland, Australia. The data are limited to a single Tier 2 construction company. - Practical implications The findings of this study can be utilised to suggest improvements in the current (or develop new) workplace training initiatives. - Social implications The research suggests that workplace training has positive relationship with career growth. The results suggest that in the construction industry, employees are generally well aware of the importance of workplace training in their career development and they largely appreciate training as being a critical factor for developing their capacity to perform their roles successfully, and to maintain their employability. - Originality/value This paper is unique as it investigates the current skills gap in both generic and skill areas within the construction industry in Queensland, Australia. So far no work has been undertaken to identify and discusses the main method of workplace learning within the Tier 2 industry in the context of Queensland Australia.