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Street Surface Quality Assessment and Visualization using Gyro Sensor
(2018-08-21)
Chawuthai, Rathachai
;
Jearanaitanakij, Kietikul
Street maintenance is one of important tasks for transportation safety. An inclusive report is required for demonstrating the quality of street surface in order to have a precise plan for repairing any defects on street at the right locations. Most observations from any government agencies are usually done by human, however reports about street surface are not well-appointed enough due to the limitation of human cognition and documentation. This paper addresses the according issue by using computation process that uses the power of the Internet of Things (IoT) to analyze the movement of gyro data with locations and time along driving routes, and then generates a visualization report to point out any broken street surface. It has been found that the analysis of gyro data using linear regression could enable street surface quality assessment and visualization for improving road safety.
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The analysis of a microwave sensor signal for detecting a kick gesture
(2018-08-13)
Chawuthai, Rathachai
;
Sakdanuphab, Rachsak
A hands-free operation is a solution for people who require a hand to do an action but both right and left hands are busy carrying something. There are many techniques, and most of them use sensors to check a command from humans such as voice and movement. A kick gesture is one technique that people can kick into the air to invoke an operation of a target device such as a kick-activation liftgate of a car. In this paper, we use a microwave sensor to detect the movement of a human's foot and employ machine learning techniques to analyses the sensor data. It has found that the Logistic Regression technique provides the best accuracy, and the model can be simply programmed in an embedded system.
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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.

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