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    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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    Travel Time Prediction on Long-Distance Road Segments in Thailand
    (2022-06-01)
    Chawuthai, Rathachai
    ;
    Ainthong, Nachaphat
    ;
    Intarawart, Surasee
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    Boonyanaet, Niracha
    ;
    Sumalee, Agachai
    This study proposes a method by which to predict the travel time of vehicles on long-distance road segments in Thailand. We adopted the Self-Attention Long Short-Term Memory (SA-LSTM) model with a Butterworth low-pass filter to predict the travel time on each road segment using historical data from the Global Positioning System (GPS) tracking of trucks in Thailand. As a result, our prediction method gave a Mean Absolute Error (MAE) of 12.15 min per 100 km, whereas the MAE of the baseline was 27.12 min. As we can estimate the travel time of vehicles with a lower error, our method is an effective way to shape a data-driven smart city in terms of predictive mobility.