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Author: search.filters.author.Threepak, ThanunchaiSubject: search.filters.subject.Transportation

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    Defect detection of GPS trackers through data visualization
    (2019-07-01)
    Chawuthai, Rathachai
    ;
    Threepak, Thanunchai
    A Global Positioning System (GPS) tracker installed in a vehicle is commonly used to improve logistics management processes and transportation safety. All GPS trackers must send data including locations, timestamps, and speeds to a server all the time. In case of a device failure, it can be checked by incomplete data; however, a device's sensor inaccuracy, which can create negative consequences to many parties, becomes a challenging issue to detect. With this reason, this paper aims to adopt data visualization to find out the defect of GPS trackers. It has been found that some defects noticed by a visualization were reported, and providers got advantage of this result to maintain their devices.
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    A Hybrid Method for Predicting a Potential Next Rest Stop of Commercial Vehicles
    (2018-01-01)
    Chawuthai, Rathachai
    ;
    Chankaew, Nattaphon
    ;
    Threepak, Thanunchai
    Long-distance trips such as freight and passenger transports over cities can create driver fatigue, so drivers prefer to get a rest for a while during their long-time driving. In Thailand, there are rest stops along main roads between cities, such as petrol stations, travel plazas, wayside parks, and scenic areas. In order to provide a better service to customers, the rest stops must have a good management, so the prediction of the number of potential vehicles in a period of time is primarily needed. One important task is to predict the next rest stop of every car at a period of time. Due to this requirement, this paper aims to introduce a prediction model for predicting the next rest stop of a vehicle by analyzing the global positioning system (GPS) tracking data of all commercial vehicles in Thailand. The proposed prediction model is a hybrid model that comprises of three scoring functions depended on the frequent pattern of connected rest stops, the direction of connected rest stops in a route, and the popularity of the rest stops. The experimental result shows that the proposed prediction model gives high accurate result in terms of the area under the receiver-operating-characteristic curve (AUC). This predicted result is also useful for a government department and rest stops' owner to improve transportation, road safety, and other service.