Threepak, Thanunchai

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Threepak, Thanunchai
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Threepak, T.
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thanunchai.th@kmitl.ac.th

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    Item type:Publication,
    Feature Selection Method Based on Correlation Tree
    (2020-01-01)
    Yapila, Prajak
    ;
    Threepak, Thanunchai
    Machine learning is one of techniques adapted to detect intrusion for cyber security. One of importance techniques to find anomaly is classification. But classification with huge dataset has the resources and time consumption. Feature selection is choice to reduce the data dimension to improve processing performance. In this paper, we introduce the new feature selection method that selects some fields of data set using position of each feature in correlation tree. Then, the result from the correlation tree feature selection of KDDCUP’99 data set are compared with two feature selection technique, correlation of coefficient (CC-type) and BFS by using three reference classifier, Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB).
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    Item type:Publication,
    GPS Data Analytics for the Assessment of Public City Bus Transportation Service Quality in Bangkok
    (2023-04-01)
    Chawuthai, Rathachai
    ;
    Sumalee, Agachai
    ;
    Threepak, Thanunchai
    Evaluation of the quality of service (QoS) of public city buses is generally performed using surveys that assess attributes such as accessibility, availability, comfort, convenience, reliabilities, safety, security, etc. Each survey attribute is assessed from the subjective viewpoint of the service users. This is reliable and straightforward because the consumer is the one who accesses the bus service. However, in addition to summarizing personal feedback from humans, using data analytics has become another useful method for assessing the QoS of bus transportation. This work aims to use global positioning system (GPS) data to measure the reliability, accessibility, and availability of bus transportation services. There are three QoS scoring functions for tracking complete trips, on-path driving, and on-schedule operation. In the analytical process, GPS coordinates rounding is adopted and applied for detecting trips on each route path. After assessing the three QoS scores, it has been found that most bus routes have good operations with high scores, while some bus routes show room for improvement. Future work could use our data to create recommendations for policy makers in terms of how to improve a city’s smart mobility.
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    Route Prediction from GPS Trajectory and Road Data
    (2023-01-01)
    Chawuthai, Rathachai
    ;
    Kawachakul, Kampanart
    ;
    Boonrod, Kittikom
    ;
    Threepak, Thanunchai
    This paper presents an approach to create a route prediction model for multiple vehicles from GPS trajectory and road data. Since the baseline model is designed for a single car and it provides low performance for our experiment, our approach using the HDBSCAN clustering for route data preprocessing and the prediction model based on Viterbi algorithm, which is an extension of the Hidden Markov Model, provides the better performance in terms of Hit@K where K being 3. The result of our work demonstrates the feasibility to improve the smart city technology under the scope of smart mobility as well. (Abstract)
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    Spatial-Temporal Traffic Speed Prediction on Thailand Roads
    (2021-04-01)
    Chawuthai, Rathachai
    ;
    Pruekwangkhao, Kasidit
    ;
    Threepak, Thanunchai
    The ultimate goal of our research mission is to build a traffic model for travel time prediction in Thailand in order to improve the mobility domain of the smart city. To achieve our mission, this piece of research places important on a traffic speed prediction of any reference points at an incoming time that is one significant part of the travel time prediction. In this study, we employ a linear model for predicting traffic speed of some kilometer stones in the next several minutes. Our prediction model performs less root-mean-squared-error score under some spatial-temporal conditions. In addition, the temporal-lagged associations among kilometer stones, which were extracted during the feature selection process, are observed as a traffic-dependent network on roads for analyzing the traffic congestion spreads in the future.