Analysis and Diagnosis of Truck Transport Routes in Underground Mines Using Transport Time Data Collected through Bluetooth Beacons and Tablet Computers

Park, Sebeom; Choi, Yosoon

doi:10.3390/app11104525

Cited by 4 publications

(5 citation statements)

References 31 publications

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“…The system developed by Park and Choi [33] uses a tablet computer to record the time a truck passes through the point where a Bluetooth beacon is installed; however, it cannot record the travel time of a truck traveling between the two beacons. Therefore, in this study, the truck travel time for each section was calculated using the log data analysis program developed by Park and Choi [24]. The program calls the log data files uploaded to the cloud server at once, organizes the log data, and calculates the truck travel time for each section.…”

Section: Study Area and Data Collectionmentioning

confidence: 99%

“…The method of collecting log data related to truck travel time is the same as that used in this study. However, Park and Choi [24] used percentiles (P10, P90) of truck travel time to evaluate the stability and condition of each section of the transport route. That is, if the newly collected truck travel time was measured in the range between percentiles P10 and P90, the status of the transport route was classified as normal; otherwise, it was classified as abnormal.…”

Section: Practical Use At the Underground Mine Sitementioning

confidence: 99%

“…However, hitherto, no research case has been reported on monitoring and diagnosing the condition of a mine transport system using machine learning techniques. As a similar research case related to diagnosing and predicting the status of transport routes using truck travel time data, Park and Choi [24] method can be sufficiently applied not only in underground mines but also in open-pit mines from a methodological perspective. Institutional Review Board Statement: Not applicable.…”

Section: Normalizationmentioning

confidence: 99%

“…Thompson et al [23] provided the basis for mine maintenance management systems (MMS) by integrating data collected through onboard multi-sensors that were installed on trucks with existing mine communication and asset management systems. Park and Choi [24] developed a system that could collect truck travel time data using Bluetooth beacons and tablet computers. In addition, a method for analyzing and diagnosing the transport route status of underground mines was proposed using the collected data.…”

Section: Introductionmentioning

confidence: 99%

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Diagnosis of Problems in Truck Ore Transport Operations in Underground Mines Using Various Machine Learning Models and Data Collected by Internet of Things Systems

et al. 2021

Self Cite

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This study proposes a method for diagnosing problems in truck ore transport operations in underground mines using four machine learning models (i.e., Gaussian naïve Bayes (GNB), k-nearest neighbor (kNN), support vector machine (SVM), and classification and regression tree (CART)) and data collected by an Internet of Things system. A limestone underground mine with an applied mine production management system (using a tablet computer and Bluetooth beacon) is selected as the research area, and log data related to the truck travel time are collected. The machine learning models are trained and verified using the collected data, and grid search through 5-fold cross-validation is performed to improve the prediction accuracy of the models. The accuracy of CART is highest when the parameters leaf and split are set to 1 and 4, respectively (94.1%). In the validation of the machine learning models performed using the validation dataset (1500), the accuracy of the CART was 94.6%, and the precision and recall were 93.5% and 95.7%, respectively. In addition, it is confirmed that the F1 score reaches values as high as 94.6%. Through field application and analysis, it is confirmed that the proposed CART model can be utilized as a tool for monitoring and diagnosing the status of truck ore transport operations.

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Section: Study Area and Data Collectionmentioning

confidence: 99%

Section: Practical Use At the Underground Mine Sitementioning

confidence: 99%

Section: Normalizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diagnosis of Problems in Truck Ore Transport Operations in Underground Mines Using Various Machine Learning Models and Data Collected by Internet of Things Systems

et al. 2021

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“…Conversely, the non-GPS-based approach utilises alternative technologies to determine the location of the bus fleet. These technologies include Radio-Frequency Identification (RFID) (Prinsloo and Malekian, 2016;Vinod and Mohan, 2018), Bluetooth Low Energy (BLE) (Gunady and Keoh, 2019;Park and Choi, 2021), cellular information (Song, 1994), IEEE 802.11p which is also referred to as the Wireless Access in Vehicular Environments (WAVE) (Gan et al, 2023). Most of these approaches are facilitated by the adoption of the Internet of Things (IoT).…”

Section: Introductionmentioning

confidence: 99%

Transforming urban mobility with internet of things: public bus fleet tracking using proximity-based bluetooth beacons

Elijah,

Keoh,

bin Abdul Rahim

et al. 2023

Front. Internet Things

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In today’s fast-paced world, efficient and reliable public transportation systems are crucial for optimising time and reducing carbon dioxide emissions. However, developing countries face numerous challenges in their public transportation networks, including infrequent services, delays, inaccurate and unreliable arrival times, long waiting time, and limited real-time information available to the users. GPS-based systems have been widely used for fleet management, but they can be a significant infrastructure investment for smaller operators in developing countries. The accuracy of the GPS location can be easily affected by the weather condition and GPS signals are susceptible to spoofing attacks. When the GPS device is faulty, the entire location traces will be unavailable. This paper proposes the use of Internet-of-Things (IoT)-enabled Bluetooth Low Energy (BLE) systems as an alternative approach to fleet tracking for public bus service. The proposed approach offers simplicity and easy implementation for bus operators by deploying BLE proximity beacons on buses to track their journeys, with detection devices using Raspberry Pi (RPi) Zero strategically placed at terminals and selected stops. When the bus approaches and stops at the bus stops, the BLE advertisements emitted by the proximity beacons can be reliably detected by the RPi Zero. Experiment results show that the BLE signals can be detected up to 20 m in range when the RPi Zero is placed inside a metal enclosure. The location of the bus is then sent to the cloud to estimate the arrival times. A field trial of the proposed IoT-based BLE proximity sensing system involving two public bus services in southern Malaysian cities, namely, Johor Bahru, Iskandar Puteri and Kulai is presented. Based on the data collected, a bus arrival time estimation algorithm is designed. Our analysis shows that there was a 5–10 min reduction in journey time on public holidays as compared to a normal day. Overall, the paper emphasises the importance of addressing public transportation challenges. It also describes the challenges, experience, and mitigation drawn from the deployment of this real-world use case, demonstrating the feasibility and reliability of IoT-based proximity sensing as an alternative approach to tracking public bus services.

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Recent Advances in Smart Mining Technology

Choi

2023

Applied Sciences

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Analysis and Diagnosis of Truck Transport Routes in Underground Mines Using Transport Time Data Collected through Bluetooth Beacons and Tablet Computers

Cited by 4 publications

References 31 publications

Diagnosis of Problems in Truck Ore Transport Operations in Underground Mines Using Various Machine Learning Models and Data Collected by Internet of Things Systems

Diagnosis of Problems in Truck Ore Transport Operations in Underground Mines Using Various Machine Learning Models and Data Collected by Internet of Things Systems

Transforming urban mobility with internet of things: public bus fleet tracking using proximity-based bluetooth beacons

Recent Advances in Smart Mining Technology

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