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Dr. Gunwoo Lee
Hanyang University Erica Campus

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0 Safety Analysis
0 Transportation
0 Sustainable Transportation
0 Environment and Health

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Journal article
Published: 16 August 2021 in Sustainability
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The occurrence of accidents at container ports results in damages and economic losses in the terminal operation. Therefore, it is necessary to accurately predict accidents at container ports. Several machine learning models have been applied to predict accidents at a container port under various time intervals, and the optimal model was selected by comparing the results of different models in terms of their accuracy, precision, recall, and F1 score. The results show that a deep neural network model and gradient boosting model with an interval of 6 h exhibits the highest performance in terms of all the performance metrics. The applied methods can be used in the predicting of accidents at container ports in the future.

ACS Style

Jae Hun Kim; Juyeon Kim; Gunwoo Lee; Juneyoung Park. Machine Learning-Based Models for Accident Prediction at a Korean Container Port. Sustainability 2021, 13, 9137 .

AMA Style

Jae Hun Kim, Juyeon Kim, Gunwoo Lee, Juneyoung Park. Machine Learning-Based Models for Accident Prediction at a Korean Container Port. Sustainability. 2021; 13 (16):9137.

Chicago/Turabian Style

Jae Hun Kim; Juyeon Kim; Gunwoo Lee; Juneyoung Park. 2021. "Machine Learning-Based Models for Accident Prediction at a Korean Container Port." Sustainability 13, no. 16: 9137.

Research article
Published: 28 January 2021 in Journal of Advanced Transportation
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Vehicle platooning service through wireless communication and automated driving technology has become a reality. Vehicle platooning means that several vehicles travel like a train on the road with a minimum safety distance, which leads to the enhancement of safety, mobility, and energy savings. This study proposed a framework for exploring traffic mobility and safety performance due to the market penetration rate (MPR) of truck platoons based on microscopic traffic simulations. A platoon formation algorithm was developed and run on the VISSIM platform to simulate automated truck maneuvering. As a result of the mobility analysis, it was found that the difference in network mobility performance was not significant up to MPR 80%. Regarding the mobility performance of the truck-designated lane, it was found that the average speed was lower than in other lanes. In the truck-designated lane of the on-ramp section, the average speed was identified to be approximately 33% lower. From the viewpoint of network safety, increasing the MPR of the truck platoon has a positive effect on longitudinal safety but has a negative effect on lateral safety. The safety analysis of the truck-designated lane indicated that the speed difference by lane of MPR 100% is 2.5 times higher than that of MPR 0%. This study is meaningful in that it explores traffic flow performance on mobility and safety in the process of platoon formation. The outcomes of this study are expected to be utilized as fundamentals to support the novel traffic operation strategy in platooning environments.

ACS Style

Seolyoung Lee; Cheol Oh; Gunwoo Lee. Impact of Automated Truck Platooning on the Performance of Freeway Mixed Traffic Flow. Journal of Advanced Transportation 2021, 2021, 1 -13.

AMA Style

Seolyoung Lee, Cheol Oh, Gunwoo Lee. Impact of Automated Truck Platooning on the Performance of Freeway Mixed Traffic Flow. Journal of Advanced Transportation. 2021; 2021 ():1-13.

Chicago/Turabian Style

Seolyoung Lee; Cheol Oh; Gunwoo Lee. 2021. "Impact of Automated Truck Platooning on the Performance of Freeway Mixed Traffic Flow." Journal of Advanced Transportation 2021, no. : 1-13.

Journal article
Published: 18 December 2020 in International Journal of Environmental Research and Public Health
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Background: Factors related to the wellness of taxi drivers are important for identifying high-risk drivers based on human factors. The purpose of this study is to predict high-risk taxi drivers based on a deep learning method by identifying the wellness of a driver, which reflects the personal characteristics of the driver. Methods: In-depth interviews with taxi drivers are conducted to collect wellness data. The priorities of factors affecting the severity of accidents are derived through a random forest model. In addition, based on the derived priority of variables, various combinations of inputs are set as scenarios and optimal artificial neural network models are derived for each scenario. Finally, the model with the best performance for predicting high-risk taxi drivers is selected based on three criteria. Results: A model with variables up to the 16th priority as inputs is selected as the best model; this has a classification accuracy of 86% and an F1-score of 0.77. Conclusions: The wellness-based model for predicting high-risk taxi drivers presented in this study can be used for developing a taxi driver management system. In addition, it is expected to be useful when establishing customized traffic safety improvement measures for commercial vehicle drivers.

ACS Style

Seolyoung Lee; Jae Hun Kim; Jiwon Park; Cheol Oh; Gunwoo Lee. Deep-Learning-Based Prediction of High-Risk Taxi Drivers Using Wellness Data. International Journal of Environmental Research and Public Health 2020, 17, 9505 .

AMA Style

Seolyoung Lee, Jae Hun Kim, Jiwon Park, Cheol Oh, Gunwoo Lee. Deep-Learning-Based Prediction of High-Risk Taxi Drivers Using Wellness Data. International Journal of Environmental Research and Public Health. 2020; 17 (24):9505.

Chicago/Turabian Style

Seolyoung Lee; Jae Hun Kim; Jiwon Park; Cheol Oh; Gunwoo Lee. 2020. "Deep-Learning-Based Prediction of High-Risk Taxi Drivers Using Wellness Data." International Journal of Environmental Research and Public Health 17, no. 24: 9505.

Journal article
Published: 15 December 2020 in Electronics
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Due to the advanced spatial data collection technologies, the locations of vehicles on roads are now being collected nationwide, so there is a demand for applying a micro-level emission calculation methods to estimate regional and national emissions. However, it is difficult to apply this method due to the low data collection rate and the complicated calculation procedure. To solve these problems, this study proposes a vehicle trajectory extraction method for estimating micro-level vehicle emissions using massive GPS data. We extracted vehicle trajectories from the GPS data to estimate the emission factors for each link at a specific time period. Vehicle trajectory data was divided into several groups through a k-means clustering method, in which the ratios of each operating mode were used as variables for clustering similar vehicle trajectories. The results showed that the proposed method has an acceptable accuracy in estimating emissions. Furthermore, it was also confirmed that the estimated emission factors appropriately reflected the driving characteristics of links. If the proposed method were utilized to update the link-based micro-level emission factors using continuously accumulated trajectory data for the road network, it would be possible to efficiently calculate the regional- or national-level emissions only using traffic volume.

ACS Style

Hyejung Hu; Gunwoo Lee; Jae Hun Kim; Hyunju Shin. Estimating Micro-Level on-Road Vehicle Emissions Using the K-Means Clustering Method with GPS Big Data. Electronics 2020, 9, 2151 .

AMA Style

Hyejung Hu, Gunwoo Lee, Jae Hun Kim, Hyunju Shin. Estimating Micro-Level on-Road Vehicle Emissions Using the K-Means Clustering Method with GPS Big Data. Electronics. 2020; 9 (12):2151.

Chicago/Turabian Style

Hyejung Hu; Gunwoo Lee; Jae Hun Kim; Hyunju Shin. 2020. "Estimating Micro-Level on-Road Vehicle Emissions Using the K-Means Clustering Method with GPS Big Data." Electronics 9, no. 12: 2151.

Journal article
Published: 23 October 2020 in Sustainability
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With its long coastline, and numerous inlets and offshore islands, coastal ferry industries play a vital role in Korean maritime transportation. This study focuses on the southwestern part of Korea, Mokpo (which has the most inhabited islands and the highest proportion of elderly island residents), and aims to evaluate the impact of passengers’ mobility burdens on the efficiency of ferry routes to achieve a better service for passengers. Integrated principal component analysis–data envelopment analysis and a fuzzy C-means clustering method were applied to analyze the efficiency of ferry routes in the Mokpo area. The efficiency results indicate that longer routes do not always achieve high-efficiency scores. The proportion of general passengers appears to influence the efficiency improvements of both general and subsidiary ferry routes. These findings can assist in better comprehending the relationship between passengers’ mobility burdens and ferry route efficiencies; this will enable the authorities and ferry management departments to develop appropriate policies and strategies and to reconstruct certain features of the inefficient routes, thereby increasing operational efficiency, reducing mobility burdens, and improving the convenience of ferry travel and sustainability of Korean passenger routes.

ACS Style

Thi Pham; Gunwoo Lee; Hwayoung Kim. Toward Sustainable Ferry Routes in Korea: Analysis of Operational Efficiency Considering Passenger Mobility Burdens. Sustainability 2020, 12, 8819 .

AMA Style

Thi Pham, Gunwoo Lee, Hwayoung Kim. Toward Sustainable Ferry Routes in Korea: Analysis of Operational Efficiency Considering Passenger Mobility Burdens. Sustainability. 2020; 12 (21):8819.

Chicago/Turabian Style

Thi Pham; Gunwoo Lee; Hwayoung Kim. 2020. "Toward Sustainable Ferry Routes in Korea: Analysis of Operational Efficiency Considering Passenger Mobility Burdens." Sustainability 12, no. 21: 8819.

Journal article
Published: 12 October 2020 in International Journal of Sustainable Transportation
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ACS Style

Kum Fai Yuen; Fei Ma; Xueqin Wang; Gunwoo Lee. The role of trust in influencing consumers' adoption of automated vehicles: An application of the health belief model. International Journal of Sustainable Transportation 2020, 1 -13.

AMA Style

Kum Fai Yuen, Fei Ma, Xueqin Wang, Gunwoo Lee. The role of trust in influencing consumers' adoption of automated vehicles: An application of the health belief model. International Journal of Sustainable Transportation. 2020; ():1-13.

Chicago/Turabian Style

Kum Fai Yuen; Fei Ma; Xueqin Wang; Gunwoo Lee. 2020. "The role of trust in influencing consumers' adoption of automated vehicles: An application of the health belief model." International Journal of Sustainable Transportation , no. : 1-13.

Journal article
Published: 16 July 2020 in The Asian Journal of Shipping and Logistics
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The shortage of seafarers continues in the global shipping market. In particular, the retention of skilled officers has not been maintained. One of the reasons why the number of officers working on merchant ships is because of the working conditions onboard the ship. For instance, there are work-life balance, contractual employment, high workload and stress levels, insufficient shore leave, and career progression, and so on. Therefore, some public system is required for the seafarers to prepare for a stable life after they leave a ship while working on the ship to relieve anxiety. Among the many ways, the seafarer's retirement pension system is a practical and effective way to ensure a stable life for seafarers. The retirement pension system can be applied differently depending on the characteristics of the employees and companies that are subscribers, and their effects can also be different. So, in this study, we are intended to analyze what type of retirement pension seafarers prefer in Korea. Retirement pension is generally classified into defined benefit (DB), defined contribution (DC) and individual retirement pension (IRP). To survey the seafarers’ preference on retirement pension by type, six hypotheses were established using company variables and seafarer variables. Company variables include the type of business and the number of seafarers employed by shipping company while seafarer variables include the service term, age, type of duty (ship's officer, sailor), and annual wage of the seafarer. To verify the established hypotheses, the survey of the seafarers who are currently employed by the shipping company was carried out. A cross-tabulation analysis among the statistical analysis approaches was performed and the chi-squared statistic was calculated to validate the hypotheses. Consequently, the preference varied depending on the service term and age of the seafarers. That is, the shorter the service term and the younger, individual retirement pension which allows personal asset management was preferred while those who have longer service term and older-age tended to prefer defined benefit. The outcome of this study is expected to be useful for the shipping company to design the retirement pension system for seafarers. Ultimately, the introduction of a seafarer retirement pension system will contribute to the influx of skilled crews, the competitiveness of the shipping company, and reduced marine accidents.

ACS Style

Chong Jin Choe; Gunwoo Lee; Hwayoung Kim. Analysis of the preference on type of retirement pension for the seafarers in Korea. The Asian Journal of Shipping and Logistics 2020, 37, 37 -44.

AMA Style

Chong Jin Choe, Gunwoo Lee, Hwayoung Kim. Analysis of the preference on type of retirement pension for the seafarers in Korea. The Asian Journal of Shipping and Logistics. 2020; 37 (1):37-44.

Chicago/Turabian Style

Chong Jin Choe; Gunwoo Lee; Hwayoung Kim. 2020. "Analysis of the preference on type of retirement pension for the seafarers in Korea." The Asian Journal of Shipping and Logistics 37, no. 1: 37-44.

Journal article
Published: 22 May 2020 in Journal of Marine Science and Engineering
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In the maritime transportation services industry, marine accidents may lead to fatalities, injuries, and property losses. Coastal ferry operators experience marine accidents and must pay attention to safety to guarantee the sustainability of their business. This study is aimed at analyzing the operational efficiency of coastal ferry operators in Korea from a safety perspective. We designed two slack-based measure of efficiency (SBM) models. One is a normal SBM, which includes only the total passenger volume as the desirable output. The other is a safety-constrained SBM, which includes marine accident records as an undesirable output with the desirable output of passenger transportation performance. We selected 44 coastal ferry operators in Korea that have been continuously operating for five years (2013–2017) as decision-making units (DMUs) and compared their operational efficiency scores. The results showed that the impact of marine accidents on business is greater in DMUs with lower transportation sales than in those with higher sales. This suggests that, while it is important for the government to strengthen safety regulations, a combination of policies that also help small ferry operators to stay in business in the long term is necessary to reduce marine accidents effectively while improving efficiency.

ACS Style

Joohwan Kim; Gunwoo Lee; Hwayoung Kim. Analysis of Operational Efficiency Considering Safety Factors as an Undesirable Output for Coastal Ferry Operators in Korea. Journal of Marine Science and Engineering 2020, 8, 367 .

AMA Style

Joohwan Kim, Gunwoo Lee, Hwayoung Kim. Analysis of Operational Efficiency Considering Safety Factors as an Undesirable Output for Coastal Ferry Operators in Korea. Journal of Marine Science and Engineering. 2020; 8 (5):367.

Chicago/Turabian Style

Joohwan Kim; Gunwoo Lee; Hwayoung Kim. 2020. "Analysis of Operational Efficiency Considering Safety Factors as an Undesirable Output for Coastal Ferry Operators in Korea." Journal of Marine Science and Engineering 8, no. 5: 367.

Articles
Published: 21 February 2020 in Maritime Policy & Management
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Due to the outstanding strength of advanced machine-learning techniques, they have become increasingly common in predictive studies in recent years, particularly in predicting ship energy performance. In constructing predictive models, prior studies have mostly employed vessels’ technical parameters to establish machine-learning algorithms. To bridge this research gap and enable wider applications, this paper presents the design of a multilayer perceptron artificial neural network (MLP ANN) as a machine-learning technique to estimate ship fuel consumption. We utilized the real operational data from 100–143 container ships to estimate fuel consumption for five different container ships grouped by size. We compared the performance of two ANN models and two multiple-regression models. Four input parameters (sailing time, speed, cargo weight, and capacity) were included in the first ANN and the first regression model, while the other two models only consider two inputs from physical function. The mean absolute percentage error of the ANN models with four inputs was the smallest and less than those in extended statistical models, demonstrating the MLP’s superiority over the statistical model. The MLP ANN model can thus be applied to confirm the effectiveness of the slow-steaming method for achieving energy efficiency.

ACS Style

Luan Thanh Le; Gunwoo Lee; Keun-Sik Park; Hwayoung Kim. Neural network-based fuel consumption estimation for container ships in Korea. Maritime Policy & Management 2020, 47, 615 -632.

AMA Style

Luan Thanh Le, Gunwoo Lee, Keun-Sik Park, Hwayoung Kim. Neural network-based fuel consumption estimation for container ships in Korea. Maritime Policy & Management. 2020; 47 (5):615-632.

Chicago/Turabian Style

Luan Thanh Le; Gunwoo Lee; Keun-Sik Park; Hwayoung Kim. 2020. "Neural network-based fuel consumption estimation for container ships in Korea." Maritime Policy & Management 47, no. 5: 615-632.

Research article
Published: 23 January 2020 in Journal of Advanced Transportation
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Vehicle emissions are largely determined by the details of driving behaviours. Accordingly, emissions are often estimated by integrating micro-scale emission models into traffic simulations. Under this approach, it is essential to replicate the actual traffic situation being considered in an emission evaluation using a proper calibration procedure. Most previous research with respect to traffic flow has primarily focused on adjusting the complex combinations of parameters evaluated in these models, but it is not guaranteed that the use of widely used calibration measures can lead more accurate emissions estimates. Accordingly, we propose a systematic guideline for calibration to ensure reliable micro-scale emissions estimates. A calibration procedure is thus established in this paper based on various measure of effect (MOE) compositions (i.e., calibration levels) consisting of aggregated traffic data to identify the level that most reliably estimates micro-scale emissions. Five calibration levels of progressively more detailed measurements are first defined, valid calibration levels are identified, and the reliable calibration level is finally selected based on the available traffic data. The effect of vehicle type (i.e., light vs. heavy vehicles) composition on the estimated emissions is also evaluated for a well-calibrated simulation. We expect that a highly reliable estimation of emissions is possible using this more detailed traffic simulation calibration measurement.

ACS Style

Jinsoo Kim; Jae Hun Kim; Gunwoo Lee; Hyun-Ju Shin; Jahng Hyon Park. Microscopic Traffic Simulation Calibration Level for Reliable Estimation of Vehicle Emissions. Journal of Advanced Transportation 2020, 2020, 1 -13.

AMA Style

Jinsoo Kim, Jae Hun Kim, Gunwoo Lee, Hyun-Ju Shin, Jahng Hyon Park. Microscopic Traffic Simulation Calibration Level for Reliable Estimation of Vehicle Emissions. Journal of Advanced Transportation. 2020; 2020 ():1-13.

Chicago/Turabian Style

Jinsoo Kim; Jae Hun Kim; Gunwoo Lee; Hyun-Ju Shin; Jahng Hyon Park. 2020. "Microscopic Traffic Simulation Calibration Level for Reliable Estimation of Vehicle Emissions." Journal of Advanced Transportation 2020, no. : 1-13.

Research article
Published: 25 December 2019 in Journal of Advanced Transportation
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Since various freeway design features are simultaneously installed on roadways, it is important to assess their combined safety effects correctly. This study investigated associations between multiple roadway cross-section design features on freeways and traffic safety. In order to consider the interaction impact of multiple design features and nonlinearity of predictors concurrently, multivariate adaptive regression splines (MARS) models were developed for all types and freight vehicle crashes. In MARS models, a series of basis functions is applied to represent the space of predictors and the combined safety effectiveness of multiple design features can be interpreted by the interaction terms. The generalized linear regression models (GLMs) with negative binomial (NB) distribution were also evaluated for comparison purposes. The results determine that the MARS models show better model fitness than the NB models due to its strength to reflect the nonlinearity of crash predictors and interaction impacts among variables under different ranges. Various interaction impacts among parameters under different ranges based on knot values were found from the MARS models, whereas two interaction terms were found in the NB models. The results also showed that the combined safety effects of multiple treatments from the NB models over-estimated the real combined safety effects when using the simple multiplication approach suggested by the HSM (Highway Safety Manual). Therefore, it can be recommended that the MARS is applied to evaluate the safety impacts of multiple treatments to consider both the interaction impacts among treatments and nonlinearity issues simultaneously.

ACS Style

Juneyoung Park; Mohamed Abdel-Aty; Ling Wang; Gunwoo Lee; Jungyeol Hong. Influence of Multiple Freeway Design Features on Freight Traffic Safety. Journal of Advanced Transportation 2019, 2019, 1 -8.

AMA Style

Juneyoung Park, Mohamed Abdel-Aty, Ling Wang, Gunwoo Lee, Jungyeol Hong. Influence of Multiple Freeway Design Features on Freight Traffic Safety. Journal of Advanced Transportation. 2019; 2019 ():1-8.

Chicago/Turabian Style

Juneyoung Park; Mohamed Abdel-Aty; Ling Wang; Gunwoo Lee; Jungyeol Hong. 2019. "Influence of Multiple Freeway Design Features on Freight Traffic Safety." Journal of Advanced Transportation 2019, no. : 1-8.

Transportation engineering
Published: 12 November 2019 in KSCE Journal of Civil Engineering
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Using the Korean In-Depth Accident Study (KIDAS) database, this study estimated the safety benefits of autonomous emergency braking systems (AEBS). We analyze crash severity using an ordered probit model to identify contributing factors based on the KIDAS database as well as the statistical relationship between collision speed and crush extent. We estimate the change in injury severity after AEBS installation using the results of both analyses. From the results, we identify that adolescence, fastened seatbelts, and crush extent are statistically significantly correlated with the dependent variable, injury severity score. Analysis of the relationship between crush extent and collision speed reveals that the explanatory power of the exponential model is higher than that of the linear model, while evaluating the effect of AEBS using the two relationships shows that it had a maximum injury reduction effect of 25% when the injuries were not minor. Results showing the effectiveness of installing an AEBS are presented, and a method to evaluate the potential safety benefits obtained from the analyses conducted in this study is proposed. The methods used in this study could be useful in promoting the rapid propagation of in-vehicle safety measures and developing relevant policies.

ACS Style

Seolyoung Lee; Eunbi Jeong; Cheol Oh; Gunwoo Lee. Estimation of the Safety Benefits of AEBS Based on an Analysis of the KIDAS Database. KSCE Journal of Civil Engineering 2019, 23, 5208 -5214.

AMA Style

Seolyoung Lee, Eunbi Jeong, Cheol Oh, Gunwoo Lee. Estimation of the Safety Benefits of AEBS Based on an Analysis of the KIDAS Database. KSCE Journal of Civil Engineering. 2019; 23 (12):5208-5214.

Chicago/Turabian Style

Seolyoung Lee; Eunbi Jeong; Cheol Oh; Gunwoo Lee. 2019. "Estimation of the Safety Benefits of AEBS Based on an Analysis of the KIDAS Database." KSCE Journal of Civil Engineering 23, no. 12: 5208-5214.

Articles
Published: 05 November 2019 in Maritime Policy & Management
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Accurately estimating fuel consumption of ships is crucial for shipping companies, port authorities, and environmental protection agencies. The bottom-up approach is becoming increasingly popular because it can estimate ship fuel consumption by accounting for ship activity conditions, such as changes in voyage speed, time, and distance; however, its use is still limited when estimating ship fuel consumption. Ship-specific information, such as the daily fuel consumption rate for main and auxiliary engines for every vessel, is expensive to gather, and generally not collected from private shipping companies. To address this research gap, we develop simplified and composite ship fuel consumption models for ocean-going container ships by size using a regression model. To estimate the fuel consumption models for container ships, we rely on ship activity data, including average speed and sailing time, distance, and actual fuel consumption for main and auxiliary engines. This information is obtained from a major container shipping company in Korea. We estimate and validate the parameters associated with fuel consumption for five different container ship sizes, all of which are smaller than the Post-Panamax container ship (15,000 TEU and above).

ACS Style

Luan Thanh Le; Gunwoo Lee; Hwayoung Kim; Su-Han Woo. Voyage-based statistical fuel consumption models of ocean-going container ships in Korea. Maritime Policy & Management 2019, 47, 304 -331.

AMA Style

Luan Thanh Le, Gunwoo Lee, Hwayoung Kim, Su-Han Woo. Voyage-based statistical fuel consumption models of ocean-going container ships in Korea. Maritime Policy & Management. 2019; 47 (3):304-331.

Chicago/Turabian Style

Luan Thanh Le; Gunwoo Lee; Hwayoung Kim; Su-Han Woo. 2019. "Voyage-based statistical fuel consumption models of ocean-going container ships in Korea." Maritime Policy & Management 47, no. 3: 304-331.

Journal article
Published: 01 October 2019 in Accident Analysis & Prevention
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Freight truck-involved crashes result in a high mortality rate and significantly impact logistic costs; therefore, many researchers have analyzed the causes of truck-involved traffic crashes. In the existing literature, it was found that truck-involved crashes are affected by factors such as road geometry, weather, driver and vehicle characteristics, and traffic volume based on a variety of statistical methodologies; however, the endogenous impact resulting from driver traffic violation has not been considered. The goal of the study is to discover the factors influencing freight vehicle crashes and develop more accurate crash probability estimation by explaining the endogenous driver traffic violations. To achieve the purpose of this study, we applied the two-stage residual inclusion (2SRI) approach, a methodology used in the nonlinear regression analysis model for capturing the endogeneity issue. This method improves the accuracy of the model by capturing the unobserved effects of driver traffic violations. From the results, traffic violations were identified to be influenced by the driver's physical condition, as well as driver and vehicle characteristics. Furthermore, variables of driver traffic violations such as improper passing, speeding, and safe distance violation were found to be endogenous in the probability model of freight truck crashes on expressway mainlines.

ACS Style

Jungyeol Hong; Juneyoung Park; Gunwoo Lee; Dongjoo Park. Endogenous commercial driver’s traffic violations and freight truck-involved crashes on mainlines of expressway. Accident Analysis & Prevention 2019, 131, 327 -335.

AMA Style

Jungyeol Hong, Juneyoung Park, Gunwoo Lee, Dongjoo Park. Endogenous commercial driver’s traffic violations and freight truck-involved crashes on mainlines of expressway. Accident Analysis & Prevention. 2019; 131 ():327-335.

Chicago/Turabian Style

Jungyeol Hong; Juneyoung Park; Gunwoo Lee; Dongjoo Park. 2019. "Endogenous commercial driver’s traffic violations and freight truck-involved crashes on mainlines of expressway." Accident Analysis & Prevention 131, no. : 327-335.

Article
Published: 13 September 2019 in International Journal of Intelligent Transportation Systems Research
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Monitoring road surface temperature (RST) is crucial to establish winter maintenance strategies for traffic safety and proactive congestion management. Public agencies have conventionally relied on mathematical models to predict road conditions. Typically, those models employ data collected from fixed environmental sensor stations sporadically located over a wide network and estimate parameters that are specific to a site. In addition, taking interactions among meteorological, geographical, and physical road characteristics into a model is almost impossible. This study proposes a new and practical framework that can estimate an RST variation model via an off-the-shelf Classification Learner application embedded in the MATLAB machine learning tool. To develop the model, this study uses climatological information, vehicular ambient temperature data from a probe vehicle, and road section information (i.e., basic section, bridge section, tunnel section). The performance of the developed models is then compared with actual RSTs measured from a thermal mapping system. The final evaluation found the estimated RST variation along road section and observed ones compatible, indicating that the proposed procedure can be readily implemented. The proposed method can help public agencies develop both reliable and readily transferrable procedures for monitoring RST variation without having to rely on data collected from costly fixed sensors.

ACS Style

Choong Heon Yang; Duk Geun Yun; Jin Guk Kim; Gunwoo Lee; Seoung Bum Kim. Machine Learning Approaches to Estimate Road Surface Temperature Variation along Road Section in Real-Time for Winter Operation. International Journal of Intelligent Transportation Systems Research 2019, 18, 343 -355.

AMA Style

Choong Heon Yang, Duk Geun Yun, Jin Guk Kim, Gunwoo Lee, Seoung Bum Kim. Machine Learning Approaches to Estimate Road Surface Temperature Variation along Road Section in Real-Time for Winter Operation. International Journal of Intelligent Transportation Systems Research. 2019; 18 (2):343-355.

Chicago/Turabian Style

Choong Heon Yang; Duk Geun Yun; Jin Guk Kim; Gunwoo Lee; Seoung Bum Kim. 2019. "Machine Learning Approaches to Estimate Road Surface Temperature Variation along Road Section in Real-Time for Winter Operation." International Journal of Intelligent Transportation Systems Research 18, no. 2: 343-355.

Journal article
Published: 09 September 2019 in Transport Policy
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Truck platooning is a promising strategy to not only reduce the workload of truck drivers but also to increase the efficiency of freight transportation. In addition, truck platooning is expected to contribute to the increase in highway capacity, which causes changes in travel demand and travel time patterns. The reliable identification of the impacts of truck platooning based on a scientific and systematic approach is a fundamental task for implementing truck platooning in practice and maximizing its effectiveness. The purpose of this study is to quantify the benefits of travel time savings that would be achieved as a result of truck platooning. A nice feature of the proposed methodology is the integration of microscopic and macroscopic phenomena in its evaluation framework. First, micro-level analysis was conducted to identify the change in highway capacity in a mixed traffic stream consisting of truck platoons and general vehicles. A widely used microscopic traffic simulator, VISSIM, was adopted to quantify the rate of change of the capacity. For the macro-level analysis, the transportation planning software TransCAD was used to estimate the extent of the reduction in travel times based on the increase in the capacity obtained from the micro-level analysis. Simulations based on the proposed methodology were performed on Korean freeways of 3 or more lanes. It was observed that truck platooning would result in annual benefits of travel time savings corresponding to approximately 187.6 billion Korean won (167.7 million US $) in 2020. The results are expected to support various policy-making activities to facilitate the implementation of truck platooning on freeways.

ACS Style

Young Jo; Jungin Kim; Cheol Oh; Ikki Kim; Gunwoo Lee. Benefits of travel time savings by truck platooning in Korean freeway networks. Transport Policy 2019, 83, 37 -45.

AMA Style

Young Jo, Jungin Kim, Cheol Oh, Ikki Kim, Gunwoo Lee. Benefits of travel time savings by truck platooning in Korean freeway networks. Transport Policy. 2019; 83 ():37-45.

Chicago/Turabian Style

Young Jo; Jungin Kim; Cheol Oh; Ikki Kim; Gunwoo Lee. 2019. "Benefits of travel time savings by truck platooning in Korean freeway networks." Transport Policy 83, no. : 37-45.

Articles
Published: 30 August 2019 in Maritime Policy & Management
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After the collapse of Hanjin Shipping in 2016, Korea faced the task of reconstructing its container shipping industry by enhancing the competitiveness of its shipping companies in a rapidly evolving market environment. Responding to this need for policy design, this study first attempts to understand the industry based on the shipping ecosystem, which comprises the following four areas: shipping finance, collection of cargo, acquisition of ships, and partnership among carriers. Second, it lists the structural problems, along with the remedial policy alternatives, that were identified after conducting in-depth interviews with industry experts, which included mid-level managers. Third, it conducts an importance-performance analysis to classify problems according to their importance and performance, followed by an analytic hierarchy process analysis to define the priorities of policy alternatives. Finally, drawing on the empirical results, the paper concludes with suggestions on an integrated policy package for the container shipping industry.

ACS Style

Byoung-Wook Ko; Juhyeoun Kim; Young-Jae Choi; Kwang-Soo Kil; Gunwoo Lee. Enhancing the competitiveness of Korea’s container shipping industry through structural improvements. Maritime Policy & Management 2019, 47, 57 -72.

AMA Style

Byoung-Wook Ko, Juhyeoun Kim, Young-Jae Choi, Kwang-Soo Kil, Gunwoo Lee. Enhancing the competitiveness of Korea’s container shipping industry through structural improvements. Maritime Policy & Management. 2019; 47 (1):57-72.

Chicago/Turabian Style

Byoung-Wook Ko; Juhyeoun Kim; Young-Jae Choi; Kwang-Soo Kil; Gunwoo Lee. 2019. "Enhancing the competitiveness of Korea’s container shipping industry through structural improvements." Maritime Policy & Management 47, no. 1: 57-72.

Journal article
Published: 21 June 2019 in Energy Policy
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In Korea, the sales of battery electric vehicles (BEVs) have increased since their introduction to the market in 2010. However, considering the government's plan to introduce BEVs, current BEVs sales in Korea are still below targeted numbers. Among several reasons for this, consumers' intentions and desired time to purchase BEVs are the most important. This study identifies the factors that affect and influence these two reasons. We used survey data containing these two reasons as stated preferences and applied a binary choice model to estimate consumers' intentions to purchase BEVs and an ordered model to estimate the desired period for purchasing BEVs. The study's results show that prior experience driving BEVs and other additional factors—including number of household vehicles, educational level, and perception of government incentives and public parking benefits—have a significant effect on consumers' intentions and desired time to purchase BEVs. Therefore, providing consumers with prior opportunities to drive BEVs is critical for BEVs' full market penetration in Korea.

ACS Style

Jae Hun Kim; Gunwoo Lee; Ji Young Park; Jungyeol Hong; Juneyoung Park. Consumer intentions to purchase battery electric vehicles in Korea. Energy Policy 2019, 132, 736 -743.

AMA Style

Jae Hun Kim, Gunwoo Lee, Ji Young Park, Jungyeol Hong, Juneyoung Park. Consumer intentions to purchase battery electric vehicles in Korea. Energy Policy. 2019; 132 ():736-743.

Chicago/Turabian Style

Jae Hun Kim; Gunwoo Lee; Ji Young Park; Jungyeol Hong; Juneyoung Park. 2019. "Consumer intentions to purchase battery electric vehicles in Korea." Energy Policy 132, no. : 736-743.

Articles
Published: 25 March 2019 in Maritime Policy & Management
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The container shipping industry is receiving growing attention in driving the performance of global supply chains. This phenomenon has accelerated supply chain integration (SCI) within the industry. Although SCI could offer numerous benefits, it is often quoted to be implemented easier in theory than in practice. The high failure rate that is associated with SCI is often not addressed in the literature. Grounded on resource-based view (RBV) theory, this paper is aimed at identifying the critical success factors (CSFs) and examining their influence on SCI and supply chain performance (SCP). Survey questionnaires were administered on 164 container shipping firms. The constructs were validated empirically using confirmatory factor analysis and were subsequently analysed using structural equation modelling. The proposed CSFs in this study are found to be positively corelated with SCI, which, in turn, is positively correlated with SCP. This paper has contributed to both theory and practice by applying RBV theory to identify the key resources and capabilities that are necessary for SCI in the container shipping industry.

ACS Style

Kum Fai Yuen; Xueqin Wang; Fei Ma; Gunwoo Lee; Xiangyi Li. Critical success factors of supply chain integration in container shipping: an application of resource-based view theory. Maritime Policy & Management 2019, 46, 653 -668.

AMA Style

Kum Fai Yuen, Xueqin Wang, Fei Ma, Gunwoo Lee, Xiangyi Li. Critical success factors of supply chain integration in container shipping: an application of resource-based view theory. Maritime Policy & Management. 2019; 46 (6):653-668.

Chicago/Turabian Style

Kum Fai Yuen; Xueqin Wang; Fei Ma; Gunwoo Lee; Xiangyi Li. 2019. "Critical success factors of supply chain integration in container shipping: an application of resource-based view theory." Maritime Policy & Management 46, no. 6: 653-668.

Research article
Published: 21 March 2019 in Transportation Research Record: Journal of the Transportation Research Board
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Big text data show trends from past logistics research and define freight flow and socio-economic relationships in the global logistics network. This relationship plays an important role in predicting future logistics trends and determining the direction of research. The purpose of this study was to collect logistics and freight related papers published in Transportation Research Record: Journal of the Transportation Research Board, since 1996 and to derive the main topics of the logistics studies that have been performed via topic modeling, using the Latent Dirichlet Allocation (LDA) approach. From the results, 20 main topics with keywords and phrases were extracted from the logistics research papers, which suggests that topics such as trip generation model, urban freight, and logistics hub have been emerging for scholars in the fields of road, air, and shipping logistics and have been examined for some time. In addition, big data, the Internet of Things (IoT), and information and communications technology have recently been applied to the logistics field. Research on data collection technology and route optimization algorithms that incorporate the technologies have, therefore, attracted a great deal of interest from current researchers. Through the framework of this study, it is expected that future trends in the field of logistics will be predicted, and that appropriate planning and strategies can be established.

ACS Style

Jungyeol Hong; Reuben Tamakloe; Gunwoo Lee; Dongjoo Park. Insight from Scientific Study in Logistics using Text Mining. Transportation Research Record: Journal of the Transportation Research Board 2019, 2673, 97 -107.

AMA Style

Jungyeol Hong, Reuben Tamakloe, Gunwoo Lee, Dongjoo Park. Insight from Scientific Study in Logistics using Text Mining. Transportation Research Record: Journal of the Transportation Research Board. 2019; 2673 (4):97-107.

Chicago/Turabian Style

Jungyeol Hong; Reuben Tamakloe; Gunwoo Lee; Dongjoo Park. 2019. "Insight from Scientific Study in Logistics using Text Mining." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4: 97-107.