KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Watthanaklang, Duangdao

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Applying hierarchical logistic models to compare urban and rural roadway modeling of severity of rear-end vehicular crashes
    (2020-06-01)
    Champahom, Thanapong
    ;
    Jomnonkwao, Sajjakaj
    ;
    Watthanaklang, Duangdao
    ;
    Karoonsoontawong, Ampol
    ;
    Chatpattananan, Vuttichai
    A rear-end crash is a widely studied type of road accident. The road area at the crash scene is a factor that significantly affects the crash severity from rear-end collisions. These road areas may be classified as urban or rural and evince obvious differences such as speed limits, number of intersections, vehicle types, etc. However, no study comparing rear-end crashes occurring in urban and rural areas has yet been conducted. Therefore, the present investigation focused on the comparison of diverse factors affecting the likelihood of rear-end crash severities in the two types of roadways. Additionally, hierarchical logistic models grounded in a spatial basis concept were applied by determining varying parameter estimations with regard to road segments. Additionally, the study compared coefficients with multilevel correlation model and those without multilevel correlation. Four models were established as a result. The data used for the study pertained to rear-end crashes occurring on Thai highways between 2011 and 2015. The results of the data analysis revealed that the model parameters for both urban and rural areas are in the same direction with the larger number of significant parameter values present in the rural rear-end crash model. The significant variables in both the urban and rural road segment models are the seat belt use, and the time of the incident. To conclude, the present study is useful because it provides another perspective of rear-end crashes to encourage policy makers to apply decisions that favor rules that assure safety.
  • 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).