Threepak, Thanunchai

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GPS Data Analytics for the Assessment of Public City Bus Transportation Service Quality in Bangkok
(2023-04-01)
Chawuthai, Rathachai
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Sumalee, Agachai
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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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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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Route Prediction from GPS Trajectory and Road Data
(2023-01-01)
Chawuthai, Rathachai
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Kawachakul, Kampanart
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Boonrod, Kittikom
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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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A Hybrid Method for Predicting a Potential Next Rest Stop of Commercial Vehicles
(2018-01-01)
Chawuthai, Rathachai
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Chankaew, Nattaphon
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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.
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Spatial-Temporal Traffic Speed Prediction on Thailand Roads
(2021-04-01)
Chawuthai, Rathachai
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Pruekwangkhao, Kasidit
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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.

Threepak, Thanunchai

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