Chatpattananan, Vuttichai

Loading...
Thumbnail Image
Preferred name
Chatpattananan, Vuttichai
Alternative Name
Chatpattananan, V.
Main Affiliation
Email
vuttichai.ch@kmitl.ac.th

Settings

Now showing 1 - 3 of 3
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Measuring the motivation to ride bicycles for tourism through a comparison of tourist attractions
    (2016-11-01)
    Watthanaklang, Duangdao
    ;
    Ratanavaraha, Vatanavongs
    ;
    Chatpattananan, Vuttichai
    ;
    Jomnonkwao, Sajjakaj
    In Thailand, supporting bicycle riding is regarded as an essential strategy. Many organizations are developing campaigns and activities to promote bicycle riding. However, most Thai people do not enjoy riding bicycles. Thus, this study aims to understand the motivational components and compare the different motivations for bicycle riding in various areas using confirmatory factor analysis (CFA). Six factors were considered: self-development, contemplation, exploration, physical challenge, stimulus seeking, and social interaction. The samples used in this study were 798 Thai tourists. The results of the second-order CFA indicate that six factors indicated motivation to ride bicycles at these tourist attractions at a statistical significance of 0.01. Moreover, the invariance analysis of the model parameters for the two areas through chi-square difference testing shows that factor loadings, intercepts, and the structural path have different values for tourist attractions in the mountains and those by the sea at a statistical significance of 0.01. Thus, models for tourist attractions in the mountain and those by the sea should be developed separately to determine suitable policies for these areas. Consequently, the government sectors and other involved organizations should use these indicators to develop more precise and suitable policies to promote bicycle riding for targeted groups. The CFA loadings obtained from this study can be used for ranking the priority of improving motivation for riding bicycles. Regarding mountain tourist attractions, contemplation was the factor having maximum CFA loading (β=0.935), followed by exploration (β=0.900). For sea tourist attractions, contemplation was the factor having the highest CFA loadings equal 0.992 followed by stimulus seeking (β=0.937).
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Analysis of Rear-End Crash on Thai Highway: Decision Tree Approach
    (2019-01-01)
    Champahom, Thanapong
    ;
    Jomnonkwao, Sajjakaj
    ;
    Chatpattananan, Vuttichai
    ;
    Karoonsoontawong, Ampol
    ;
    Ratanavaraha, Vatanavongs
    Objective. Among crash types on Thai highways, rear-end crashes have been found to cause the largest number of fatalities. This study aims to find ways to decrease rear-end crashes and fatal rear-end crashes. Methods. Classification and regression tree (CART) was used to analyze the complicated relationship of variables of big data. The analysis was conducted by creating two models: (1) a model which indicates the causes of rear-end crashes by applying Quasi-Induced Exposure to at-fault driver characteristics; (2) a determined model which studies fatal crashes. Results. Predictor variables in the model of at-fault and not-at-fault drivers found that driver age is most significant, followed by number of lanes and median opening area. For the mode of fatality, the use of safety equipment was found to be of most importance. Conclusion. The model results can be used to develop guidelines for public awareness programs for motorists and to propose policy changes to the Department of Highway in order to reduce the severity of rear-end crashes. Moreover, this paper discusses the variables that may result in both the perspective of rear-end crash number and the fatality rate of rear-end crashes as strategies in future research.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A nonlinear optimization model on the reinforcement length of a modular block wall by varying surcharge and soil strength parameters
    (2018-08-14)
    Chatpattananan, Vuttichai
    ;
    Kongsomboon, Thanadol
    This study proposes a nonlinear optimization approach in designing a modular block wall which is atype of the mechanical stabilized earth wall. A nonlinear optimization model is proposed based on minimizing the reinforcement length where the constraints considered are the external stability and the internal stability. Theoptimum reinforcement length can be determined based on available soil strength parameters and the maximum surcharge. This study also includes the parametric study of the reinforced soil, retained soil, and foundation soil by varying the ranges of the wall height, surcharge, and soil strength parameters in density and friction angle to see the behaviours of the aforementioned external stability and internal stability. This can be beneficial in designing thismodular block wall encountering a poor soil condition or a large amount surcharge.