• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.136-146
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• 2013

Truck demand analysis based on TCS data has limitation in that TCS data can not provide truck O/D data for each type of truck vehicle. This study conducted video survey for classifying truck vehicle types. By using TCS data and vehicle ratio by region/cities type, truck O/D data on expressway were estimated. It was found that average travel distances of small truck, medium truck and large truck were 52km/veh, 56km/veh and 97km/veh, respectively by analysing truck O/D data estimated in this study. The reliability analysis showed that check points where error rate is lower than 30% comprise of 87.3%. It is considered that estimated O/D data by truck vehicle types would be useful for the analysis of truck demand of expressway.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.7-16
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• 2008

There are unique aspects of truck vehicle movements compared with the personal travel in trip chaining. This paper reports an analysis on the truck vehicle trip chaining which intercity/metropolitan/intraregional trips are classified. Data collected from the travel dairy survey is used the truck trip-chaining analysis. The pattern of trip chaining classes is classified by the GIS mapping based on orgin-destination trip information. The physical index and efficiency index for each trip diary is used to the truck vehicle activity. Truck trips lengths and time differs from its truck type, service type and travel patterns. It is shown that the efficiency of the truck trip chaining depends on vehicle types and its delivery patterns. There are many other topics for research on trip chaining modeling such as the classification of trip chain, time use and mode choice by trip chaining.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.12
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• pp.1237-1244
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• 2016

Vehicle classification system in domestic tollgates is usually to use treadle sensor for calculating wheel width and tread of the vehicle. Due to the impact that occurs when the wheels of the vehicle contact, treadle sensor requires high durability. Recently, KHC(Korea Highway Corporation) began operating high-speed lane for cargo truck. High-speed cargo truck generate more impact the design criteria of previous treadle. Therefore, an increase in the maintenance and management costs of the treadle damage is concerned. In this paper, we propose an algorithm to classify vehicles using non-contact treadle sensors for improving durability from physical impacts. This was based on the KHC's classification criteria and showed a classification accuracy of 99.5 % in one experiment with 1892 vehicles through Changwon tollgate in 1020 local road. Therefore, it shows that vehicle classification system using non-contact treadle sensor could be applied to domestic toll tollgates, effectively.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.375-380
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• 2018

This paper proposes a classification method using the Convolutional Neural Network(CNN) which can obtain the type of trucks from the input image without the feature extraction step. To automatically classify vehicle images according to the type of truck cargo box, the top view images of the vehicle are used as input image and we design the structure of the CNN suitable for the input images. Learning images and correct output results is generated and the weights of neural network are obtained through the learning process. The actual image is input to the CNN and the output of the CNN is calculated. The classification performance is evaluated through comparison CNN output with actual vehicle types. Experimental results show that vehicle images could be classified with more than 90 percent accuracy according to the type of cargo box and this method can be used for pre-classification for inspecting loading defect.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.6
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• pp.627-634
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• 2016

Vehicle classification system in domestic tollgates is usually to use treadle sensor for calculating wheel width and tread of the vehicle. due to the impact that occurs when the wheels of the vehicle contact, treadle sensor requires high durability. recently, KHC(Korea Highway Corporation) began operating high-speed lane for cargo truck. high-speed cargo truck generate more impact the design criteria of previous treadle. therefore, an increase in the maintenance and management costs of the treadle damage is concerned. In this paper, we propose an algorithm for obtaining optimal wheel width and tread using non-contact treadle sensor that been improved durability from physical impacts. for the verification of the proposed algorithm, a field test was performed using 1/2/3/6 class vehicles based on the KHC's classification criteria. through this experiments, maximum error of the width and the tread is each ${\pm}2cm$ and ${\pm}8cm$, also the accuracy was measured as 98%, 97% or more, and proved that the proposed algorithm valid on to apply to the vehicle classification system.

• International Journal of Highway Engineering
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• v.8 no.3 s.29
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• pp.29-37
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• 2006

The primary objective of this study is to characterize traffic axle loadings that consider Korea specific traffic conditions for developing mechanistic-based pavement design method as a part of Korea Pavement Research Program(KPRP). Although the concept of equivalent single axle load(ESAL) has been generally used since the 1960s for the pavement design, the mechanistic-based pavement design procedure requires more accurate axle loading data on the specific pavement. In this study, axle loading data were collected according to vehicle type and highway functional classification. Axle-load spectrum was then standardized by cumulative density function(cdf), because the axle load spectrum could vary from the observed site, truck traffic volume, and truck type, Finally, this study presented the procedure and S-shaped exponential models for characterizing axle load spectra according to vehicle type and highway functional classification.

• Journal of IKEEE
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• v.24 no.1
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• pp.97-105
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• 2020

A thermal imaging sensor receives the radiating energy from the target and the background, so it has been widely used for detection, tracking, and classification of targets at night for military purpose. In recognizing the target automatically using thermal images, if the correct edges of object are used then it can generate the classification results with high accuracy. However since the thermal images have lower spatial resolution and more blurred edges than color images, the accuracy of the classification using thermal images can be decreased. In this paper, to overcome this problem, a new hierarchical classifier using both shape and local features based on the segmentation reliabilities, and the class/pose updating method for vehicle classification are proposed. The proposed classification method was validated using thermal video sequences of more than 20,000 images which include four types of military vehicles - main battle tank, armored personnel carrier, military truck, and estate car. The experiment results showed that the proposed method outperformed the state-of-the-arts methods in classification accuracy.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.1
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• pp.40-47
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• 2017

To classify the vehicle targets automatically using thermal images, there are usually two main categories of feature extraction method, local and shape feature extraction methods. Since thermal images have less texture information than color images, the shape feature extraction method is useful when the segmentation results are correct. However, if there are some errors in target segmentation, the shape feature may contain some errors, then the classification accuracy can be decreased. To overcome these problems, in this paper, we propose the segmentation reliability estimation method for target classification. The segmentation reliability can be estimated by using the difference information of average intensities and edge energies between the target and the background area. The estimated segmentation reliability is applied in the decision level fusion method of classification results using both shape and local features. Experiment results using the thermal images of the vehicle targets (main battle tank, armored personnel carrier, military truck, and an estate car) show that the proposed classification method and the segmentation reliability estimation method have a good performance in classification accuracy.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.2
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• pp.111-123
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• 2006

In the design of bridges, exact evaluation of traffic loading is very important for the safety and maintenance of bridges. In general, traffic loading is represented by live load (including impact load) and fatigue load. For exact evaluation of traffic loading, it is important to get reliable and comprehensive truck data including the traffic and weight information. It requires the development of Bridge Weigh-In-Motion (BWIM), which measures the truck weights without stopping the traffic. Objectives of the study is (1) to develop the BWIM system, (2) to verified the system in bridges in Highways.

• Journal of Korean Society of Transportation
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• v.20 no.1
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• pp.39-52
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• 2002

Traffic data by vehicle classification is difficult for mutual exchange of data due to the different vehicle classification from each other by the data sources; as a result, application of the data is very limited. In Particular. in case of TCS vehicle classification in national highways, passenger car, van and truck are mixed in one category and the practical usage is very low. The research standardize the vehicle classification to convert other data and develop the model which can estimate national highway traffic data by the standardized vehicle classification from the raw traffic data obtained at the highway tollgates. The tollgates are categorized into several groups by their features and the model estimates traffic data by the standardized vehicle classification by using the point estimation and bootstrap algorithm. The result indicates that both of the two methods above have the significant level. When considering the bias of the extreme value by the sample size, the bootstrap algorithm is more sophisticated. Using result of this study, we is expect the usage improvement of TCS data and more specific comparison between the freeway traffic investigation and link volume on freeway using the TCS data.