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Taoyuan, Taiwan

Chung Y.-S.,Kainan University
Accident Analysis and Prevention | Year: 2013

Factor complexity is a characteristic of traffic crashes. This paper proposes a novel method, namely boosted regression trees (BRT), to investigate the complex and nonlinear relationships in high-variance traffic crash data. The Taiwanese 2004-2005 single-vehicle motorcycle crash data are used to demonstrate the utility of BRT. Traditional logistic regression and classification and regression tree (CART) models are also used to compare their estimation results and external validities. Both the in-sample cross-validation and out-of-sample validation results show that an increase in tree complexity provides improved, although declining, classification performance, indicating a limited factor complexity of single-vehicle motorcycle crashes. The effects of crucial variables including geographical, time, and sociodemographic factors explain some fatal crashes. Relatively unique fatal crashes are better approximated by interactive terms, especially combinations of behavioral factors. BRT models generally provide improved transferability than conventional logistic regression and CART models. This study also discusses the implications of the results for devising safety policies. © 2012 Elsevier Ltd. Source


Chang M.-C.,Kainan University
Energy Policy | Year: 2013

This study provides a no-output growth model to conveniently calculate the total-factor energy efficiency (TFEE) index originally proposed by Hu and Wang (2006). The TFEE index serves as a very well-known and popular means of estimating overall energy efficiency. While many previous studies have used the indicator of energy inefficiency, including the indicator of energy intensity (i.e., Energy input/Gross Domestic Product (GDP)) to measure energy efficiency, Hu and Kao (2007) point out that the indicator of energy intensity is not only a partial-factor energy efficiency indicator, but that this partial-factor ratio is also quite inappropriate for analyzing the impact of changing energy use over time. The TFEE index overcomes the disadvantage of the indicator of energy intensity as mentioned above, but five steps are needed to calculate the TFEE score. In this study, we provide a no-output growth model to conveniently calculate the TFEE score. Furthermore, we extend this no-output growth model to an output growth model. This study concludes that the output growth model not only makes it easier to calculate the TFEE index than the model proposed by Hu and Wang (2006) and Hu and Kao (2007), but that it can also obtain better TFEE scores. © 2012 Elsevier Ltd. Source


While the previous literature shows that a decline in energy intensity represents an improvement in energy use efficiency, it does not provide a target level of energy intensity, nor what room for improvement in terms of energy intensity could entail. This study establishes an indicator of such room for improvement in terms of energy intensity by measuring the difference between the target level of energy intensity and the actual energy intensity and thereby monitors energy use efficiency. The traditional indicator of energy intensity, defined as energy use over GDP, mainly estimates energy use efficiency, but is a partial effect between the energy input and GDP output. However, our proposed indicator of the room for improvement in terms of energy intensity is the total-factor effects based on the multiple-inputs model. By taking the 27 EU members to investigate their energy use efficiency using the indicator of the room for improvement in terms of energy intensity, this study concludes that an improvement in energy intensity does not fully depend on a decline in energy intensity, and we instead need to confirm whether the room for improvement in terms of energy intensity decreases. This finding is particularly relevant for energy policy-makers. © 2013 Elsevier Ltd. Source


Pai C.-W.,Kainan University
Accident Analysis and Prevention | Year: 2011

Recent emphasis on bicycling as an alternative to automobile transportation has underscored the need for research efforts directed at bicycle safety when sharing roadways with motorised vehicles. Much of the research attention is focused on junction accidents where motorists tend to infringe upon bicycles' right of way. Non-junction accidents where a motorist strikes a bicycle while overtaking it, or crashes into the rear of the bicycle, have been less frequently researched. Another common crash type is a door crash that involves a bicycle striking an open door of an automobile. Using British Stats19 accident data, the present study estimates a mixed multinomial model to predict the likelihood of a non-junction crash being of a certain crash type (out of three possible types). The methodological approach adopted allows for the individuals within the observations to have different parameter estimates (as opposed to a single parameter representing all observations). Main findings include that buses/coaches as collision partners were associated with overtaking crashes; and bicycles' traversing manoeuvres were associated with overtaking and rear-end collisions. Given a crash where a bicycle collides with a motorcycle/taxi, it is more likely a rear-end crash and a door crash, respectively. Implications of the research findings, the concluding remarks, and recommendations for future research are finally provided. © 2011 Elsevier Ltd All rights reserved. Source


Sun H.-M.,Kainan University
Pattern Recognition | Year: 2010

Up-to-date skin detection techniques use adaptive skin color modeling to overcome the varying skin color problem. Most methods for tracking skin regions in videos utilize the correlation between contiguous frames. This paper proposes a new approach for detecting skin in a single image. This approach uses a local skin model to shift a globally trained skin model to adapt the final skin model to the current image. Experimental results show that the proposed method can achieve better accuracy. Two improvements for speeding up the processing are also discussed. © 2009 Elsevier Ltd. All rights reserved. Source

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