• International Journal of Highway Engineering
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• v.16 no.5
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• pp.143-154
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• 2014

PURPOSES : This study drew factors affecting motorcycle accidents in Seoul by severity using an ordered probit model and aimed to analyze and verify the drawn influence factors. METHODS : As the severity of the accidents could be classified into three types (fatal injury, serious injury and minor injury), this study drew the factors affecting accidents by a comparative analysis employing an ordered probit model, removed the variables that would not secure significance sequentially to construct a model with high explanatory power regarding the factors affecting the severity of motorcycle accidents, and calculated the marginal effect of each factor to understand the degree of each factor's impact on the severity. First, Model 1 put in all variables; Model 2 was constructed by removing the variables of the road surface conditions that could not meet the level of significance (p=0.608); Model 3 was constructed by removing gender variable (p=0.423); and Model 4 was constructed finally by removing age variable (p=0.320). RESULTS : As a result of an analysis, statistically significant variables were time of occurrence, type of accident, road alignment and motorcycle displacement, and it turned out that the impacts on the severity were in the following order: a road alignment of left downhill, the type of motorcycle-to-vehicle accidents and a road alignment of a flatland on the left. The significance of the models was tested using the likelihood ratio, the level of significance and suitability statistics about them, and as a result of the test, the significance level and suitability of the constructed models were all excellent. In addition, the model accuracy indicating the accuracy of a predicted value compared to that of the value actually observed was 70.3% for minor injury; 70.1% for serious injury; and 68.6% for fatal injury, and the overall accuracy was 70.2%, which was very high. CONCLUSIONS : As a result of an analysis of motorcycle accidents in Seoul through the ordered probit model and the marginal effect, it turned out that their severity increased in nighttime accidents as compared to daytime ones and gradually increased in the order of motorcycle-to-vehicle accidents, motorcycle-to-person ones and the ones involving motorcycle only. As a result of an analysis, the severity of accidents in road alignments of left downhill, left flatland and straight downhill increased as compared to those in a road alignment of straight flatland and that the severity of accidents of motorcycles with a displacement larger than 50cc was higher than that of those with a displacement smaller than 50cc.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.583-595
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• 2014

Currently, Korean transportation policies are aiming for increase of safety and environment-friendly and efficient operation, by avoiding construction and expansion of roads, and upgrading road alignments and facilities. This is revealed by that there have been 22 road expansion projects (30%) and 50 road improvement projects (70%) under the 3rd Five-Year Plan for National Highways ('11~'15), while there were 53 road expansion projects (71%) and 22 road improvement projects (29%) under the 2nd Five-Year Plan for National Highways. For more effective road improvement projects, there is a need of choosing projects after an objective and scientific safety assessment of each road, and assessing safety improvement depending on projects. This study is intended to develop a model for this road safety analysis and assessment. The major objective of this study is creating a road safety analysis and assessment model appropriate for Korean society, based on the HSM (Highway Safety Manual) of the U.S. In order to build up data for model development, the sections thought to have identical geometrical structure factors in 5 lines, Cheonbuk province, were divided as homogeneous sections, and representative values of geometric structures, facilities, traffic volume, climate conditions and land usage were collected from the 1,452 sections divided. In order to build up data for model development, the sections thought to have identical geometrical structure factors in 5 lines, Cheonbuk province, were divided as homogeneous sections, and representative values of geometric structures, facilities, traffic volume, climate conditions and land usage were collected from the 1,452 sections divided. The collected data was processed correlation analysis of each road element was implemented to see which factor had a big effect on traffic accidents. On the basis of these results, then, an accident model was established as a negative binomial regression model.Using the developed model, an Crash Modification Factor (CMF) which determines accident frequency changes depending on safety performance function (SPF) predicting the number of accident occurrence through traffic volume and road section expansion, road geometric structure and traffic properties, was extracted.

• International Journal of Contents
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• v.16 no.3
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• pp.66-86
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• 2020

With the rapid growth of high-tech, the development of cultural and creative industries has gradually become the focus of national industrial development. With the proposal of China's "Belt and Road" strategy, the role of cultural and creative industries in the provinces and cities along the "Belt and Road" in the entire international trade is becoming increasingly critical. It is necessary to explore solutions to improve the competitiveness of China's cultural and creative industries, factoring the surrounding cities of the "Belt and Road" as an example. Thus, this paper proposes the six-element diamond model based on innovation capability and government support to render a comprehensive evaluation of the competitiveness of the cultural and creative industries in the 31 provinces and cities across the country. The results show that the overall competitiveness of the 18 provinces and cities along the "Belt and Road" cultural and creative industries is weak. Focusing on the 18 provinces and cities along the "Belt and Road", using the linear regression measurement model quantitative analysis, the four types of influencing factors affecting the development of the competitiveness of cultural and creative industries along the "Belt and Road" were obtained. Finally, according to the four types of influence, the competitiveness improvement plan is proposed from the four aspects: government role, consumption preference, industrial innovation ability, and the introduction of high-quality talent.

• International Journal of Highway Engineering
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• v.19 no.2
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• pp.35-44
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• 2017

PURPOSES: This study suggests a specific methodology for the prediction of road surface temperature using vehicular ambient temperature sensors. In addition, four kind of models is developed based on machine learning algorithms. METHODS : Thermal Mapping System is employed to collect road surface and vehicular ambient temperature data on the defined survey route in 2015 and 2016 year, respectively. For modelling, all types of collected temperature data should be classified into response and predictor before applying a machine learning tool such as MATLAB. In this study, collected road surface temperature are considered as response while vehicular ambient temperatures defied as predictor. Through data learning using machine learning tool, models were developed and finally compared predicted and actual temperature based on average absolute error. RESULTS : According to comparison results, model enables to estimate actual road surface temperature variation pattern along the roads very well. Model III is slightly better than the rest of models in terms of estimation performance. CONCLUSIONS : When correlation between response and predictor is high, when plenty of historical data exists, and when a lot of predictors are available, estimation performance of would be much better.

• Geomechanics and Engineering
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• v.35 no.4
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• pp.411-424
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• 2023

Reasonable estimates of tunnel lining dislocations in the operation stage, especially under longitudinal differential settlement, are important for the design of waterproof gaskets. In this paper, a modified shell-joint model is proposed to calculate shield tunnel dislocations under longitudinal differential settlement, with the ability to consider the nonlinear shear stiffness of the joint. In the case of shell elements in the model, an elastoplastic damage constitutive model was adopted to describe the nonlinear stress-strain relationship of concrete. After verifying its applicability and correctness against a full-scale tunnel test and a joint shear test, the proposed model was used to analyze the dislocation behaviors of a shield tunnel in Shanghai Metro Line 2 under longitudinal differential settlement. Based on the results, when the tunnel structure is solely subjected to water-earth load, circumferential and longitudinal joint dislocations are all less than 0.1 mm. When the tunnel suffers longitudinal differential settlement and the curvature radius of the differential settlement is less than 300 m, although maximum longitudinal joint dislocation is still less than 0.1 mm, the maximum circumferential joint dislocation is approximately 10.3 mm, which leads to leakage and damage of the tunnel structure. However, with concavo-convex tenons applied to circumferential joints, the maximum dislocation value reduces to 4.5 mm.

• Journal of the Korean Society of Safety
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• v.32 no.6
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• pp.143-149
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• 2017

According to traffic accidents statistics, the number of fatalities, injuries and the rate of increase of traffic accidents have been decreasing over last 5-years. The fatality rate is 1.9 for total accidents but the fatality rate for single vehicle accidents shows a 7.9, which is 4 times greater than the average for all accidents. Single vehicle accidents, usually occur as a vehicle impacts a fixed objects on the roadside as the vehicle runs-off from the road. However, few researches have been conducted considering the accident severity of single vehicle accidents which impact to the fixed objects on the road. The single vehicle accident is directly related to the composition of road cross section, (since it is the required the minimum width of a road for all run-off-the-road vehicles to recover or come to a safe stop). Therefore, this study analyzes the influence of road cross section on traffic accidents to find out the severity of single vehicle accident. To analyze the road elements which are related to the accident severity, the Ordered Probit Model was used. As variables, the element of road cross section such as the radius(m), vertical curve(%), cross sectional grade(%), road width(m). number of climbing lane, median, and curb, were used (as was the 3-years of accidents data). This study found out that cross slope(%), road width(m), and the number of climbing lane are related to the severity of accident. The result of this study could be expected to improve the road safety and to be used as the base data for further road safety research.

• Journal of Korean Society on Water Environment
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• v.37 no.2
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• pp.103-113
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• 2021

In this study, an Excel-based model (ROADMOD) was developed to estimate pollutant loading from the road and evaluate BMPs. ROADMOD employs the Chezy-Manning equation and empirical expression for estimating surface runoff, and power function for pollutant buildup, and exponential function for pollutant washoff in SWMM. The results of model calibration for buildup and washoff using observed data revealed a good match between the simulation results and the observed data. The long-term surface runoff and sediment simulated by ROADMOD demonstrated a good match with those by SWMM with 2 ~ 14% of relative error. The shorter sweeping interval (within 8 days) remarkably decreased sediment loads from the road. It was found that the effect of reducing sediment loads from the road was greatly affected not only by the sweeping interval but also by sweeping on the day before a rainfall event. The 48% of removal efficiency of sediment loads from the road was achieved with 26 times of road sweeping per year when sweeping was performed on the day before the rainfall event. A 4-day sweeping interval showed similar removal efficiency (48%) with 96 times of sweeping per year. It is considered that the road sweeping on the day before a rainfall event could maximize the effect of reducing the non-point source pollution from the road with minimization of the number of road sweeping. So, the road sweeping on the day before a rainfall event can be considered as one of the useful and best management practices (BMPs) on road.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.587-593
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• 2018

The current road traffic noise prediction programs of Korea, which are widely used, are based upon foreign prediction model. Thus, it is necessary to verify foreign prediction models to find out whether they are suitable for the domestic road traffic environment. In addition, an analysis and an in-depth study on the main factors should be conducted in advance as the influence factors on the occurrence of traffic noise vary for each prediction model. Therefore, this study examined the influence factors and the existing prediction models used to forecast road traffic noise. Also, analyzed their relationship with the factors influencing the noise generated by driving vehicles through multiple regression analysis using a prediction model, taking into consideration of the traffic environment and the road geometric structure. In addition, this study will apply experimental values to the existing road traffic noise prediction model (NIER, RLS-90) and the deducted road traffic noise prediction model. As a result, the order of the absolute value sum of the errors are NIER, RLS-90, model value. Through comparison and verification, developed models are to be analyzed for providing basic research results for future study on road traffic noise prediction modeling.

• International Journal of Highway Engineering
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• v.12 no.2
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• pp.9-16
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• 2010

Roughness is the most important factor to maintain the road performance, and affects greatly on the design life in Jointed Plain Concrete pavements. Also, the factors the evaluate pavement‘s commonality is the three method such as functionality, safety and structural performance. In evaluating function of road, representative factors is the roughness, which has been used to determine maintenance time as key standard. As research for roughness is absence in pavement design. Applied roughness-model had a low-reliability in Korea. Therefore, it is needed to develop reliable model in road roughness. In this research, uniform specific is applied to distribute them after selecting the concrete pavements. Concrete pavement is divided by sections of 238. Total length of this sections has 281km and account for 16% of total road length in korean concrete pavements for selected sections. Considering the korean roughness-model, the evaluation of roughness is performed for the freezing index, average annual rainfall, condition for the base, the amount of traffic as well as spalling(%), cracking(%), age(year) at the selected section at the selected section. Also, additional sections is selected to evaluate various age which affects on the roughness. As a result of the analysis, it showed that spalling(%), cracking(%), age(year), and the condition of the base affected road roughness. When the correlation with the road roughness was analyzed, the reliable model for road roughness was proposed, and the ratio that can explain road roughness was R2-68.8% and P value-0 which is statistically meaningful.