• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.39-49
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• 2022

In this study, data on traffic volume and weight of small trucks were collected from three National Highways to identify the overload status of small trucks excluded from domestic overloaded vehicle enforcement. These data were classified according to the vehicle type, day of the week, and time of day to analyze the overload distributions by the vehicle total weight. From the analysis results by vehicle type, on average, the overload ratios for trucks of 1.5 tons or less, 2.5 tons or less, 3.5 tons or less were 6.2%, 31.7%, and 13.7%, respectively. In addition, for the same vehicle model, there was a difference by collection point, and the smaller the total weight of the small truck, the higher the overload ratio. From the results according to the day of the week, there was no tendency for the overload ratio to appear high on a specific day, but there was a predictable occurrence pattern at each site. In addition, from the results by the time of day, the overload ratio was the highest on average after lunch (13:00-15:59) at all sites.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.1
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• pp.29-37
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• 2015

In this study, the estimation of axial load and total axial load was conducted using Bridge Weigh-in-Motion(BWIM) technique which generally consists of devices for measuring the strain induced in the bridge by the vehicles. axle detectors for collecting information on vehicle velocity and axle spacing. and data acquisition equipment. Vehicle driving test for the development of the BWIM system is necessary but it needs much cost and time. In addition, it demands various driving conditions for the test. Thus, we need a numerical-simulation method for resolving the cost and time problems of vehicle driving tests, and a way of measuring bridge response according to various driving conditions. Using a bridge model reflecting the dynamic characteristic contributes to increased accuracy in numerical simulation. In this paper, we conduct a numerical simulation which reflects the dynamic characteristic of a bridge using the Bridge Weigh-in-Motion technique, and suggest overload vehicle enforcement technology.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.4
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• pp.67-80
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• 2020

High-speed weigh in motion (HS-WIM) is a real-time unmanned system for measuring the weight of a freight-carrying vehicle while it is in motion without controlling vehicle traffic flow or deceleration. In Korea, HS-WIM systems are installed on the national highways and general national ways for pre-selection by law enforcement. In this study, to improve the measurement accuracy of HS-WIM, we devise improvements to the existing integral and peak weight conversion algorithms, and we provide a new fusion algorithm that can be applied to the mat-type HS-WIM. As a result of analyzing vehicle driving tests at a real site, we confirmed the highest level of weight-measuring accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.57-66
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• 2022

The Road Management Administration is cracking down on overloaded vehicles by installing low-speed or high-speed WIMs at toll gates and main lines on expressways. However, in recent years, the act of intelligently evading the overloaded-vehicle control system of the Road Management Administration by illegally manipulating the variable axle of an overloaded truck is increasing. In this manipulation, when entering the overloaded-vehicle checkpoint, all axles of the vehicle are lowered to pass normally, and when driving on the main road, the variable axle of the vehicle is illegally lifted with the axle load exceeding 10 tons alarmingly. Therefore, this study developed a technology to detect the state of the variable axle of a truck driving on the road using roadside camera images. In particular, this technology formed the basis for cracking down on overloaded vehicles by lifting the variable axle after entering the checkpoint and linking the vehicle with the account information of the checkpoint. Fundamentally, in this study, the tires of the vehicle were recognized using the Mask RCNN algorithm, the recognized tires were virtually arranged before and after the checkpoint, and the height difference of the vehicle was measured from the arrangement to determine whether the variable axle was lifted after the vehicle left the checkpoint.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.4D
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• pp.511-518
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• 2011

Truck weight data are essential for road infrastructure design, maintenance and management. WIM (Weigh-In-Motion) system provides highway planners, researchers and officials with statistical data. Recently high speed WIM data also uses to support a vehicle weight regulation and enforcement activities. This paper aims at developing axle load estimating models with high speed WIM data collected from national highway. We also suggest a method to estimate axle load using simple regression model for WIM system. The model proposed by this paper, resulted in better axle load estimation in all class of vehicle than conventional model. The developed axle load estimating model will used for on-going or re-calibration procedures to ensure an adequate level of WIM system performance. This model can also be used for missing axle load data imputation in the future.