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Myungsik Do
Department of Urban Engineering, Hanbat National University, Daejeon 34158, Korea

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Journal article
Published: 18 December 2019 in Electronics
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In Korea, data on pavement conditions, such as cracks, rutting depth, and the international roughness index, are obtained using automatic pavement condition investigation equipment, such as ARAN and KRISS, for the same sections of national highways annually to manage their pavement conditions. This study predicts the deterioration of road pavement by using monitoring data from the Korean National Highway Pavement Management System and a recurrent neural network algorithm. The constructed algorithm predicts the pavement condition index for each section of the road network for one year by learning from the time series data for the preceding 10 years. Because pavement type, traffic load, and environmental characteristics differed by section, the sequence lengths (SQL) necessary to optimize each section were also different. The results of minimizing the root-mean-square error, according to the SQL by section and pavement condition index, showed that the error was reduced by 58.3–68.2% with a SQL value of 1, while pavement deterioration in each section could be predicted with a high coefficient of determination of 0.71–0.87. The accurate prediction of maintenance timing for pavement in this study will help optimize the life cycle of road pavement by increasing its life expectancy and reducing its maintenance budget.

ACS Style

Seunghyun Choi; Myungsik Do. Development of the Road Pavement Deterioration Model Based on the Deep Learning Method. Electronics 2019, 9, 3 .

AMA Style

Seunghyun Choi, Myungsik Do. Development of the Road Pavement Deterioration Model Based on the Deep Learning Method. Electronics. 2019; 9 (1):3.

Chicago/Turabian Style

Seunghyun Choi; Myungsik Do. 2019. "Development of the Road Pavement Deterioration Model Based on the Deep Learning Method." Electronics 9, no. 1: 3.

Journal article
Published: 12 October 2019 in Sustainability
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It is common to call a taxi by taxi-apps in Korea and it was believed that an app-taxi service would provide customers with more convenience. However, customers’ requests can often be denied, as taxi drivers can decide whether to take calls from customers or not. Therefore, studies on factors that determine whether taxi drivers refuse or accept calls from customers are needed. This study investigated why taxi drivers might refuse calls from customers and factors that influence the success of matching within the service. This study used origin-destination data in Seoul and Daejeon obtained from T-map Taxis, which was analyzed via a decision tree using machine learning. Cross-validation was also performed. Results showed that distance, socio-economic features, and land uses affected matching success rate. Furthermore, distance was the most important factor in both Seoul and Daejeon. The matching success rate in Seoul was lowest for trips shorter than the average at midnight. In Daejeon, the rate was lowest when the calls were made for trips either shorter or longer than the average distance. This study showed that the matching success for ride-hailing services can be differentiated particularly by the distance of the requested trip depending on the size of the city.

ACS Style

Myungsik Do; Wanhee Byun; Doh Kyoum Shin; Hyeryun Jin. Factors Influencing Matching of Ride-Hailing Service Using Machine Learning Method. Sustainability 2019, 11, 5615 .

AMA Style

Myungsik Do, Wanhee Byun, Doh Kyoum Shin, Hyeryun Jin. Factors Influencing Matching of Ride-Hailing Service Using Machine Learning Method. Sustainability. 2019; 11 (20):5615.

Chicago/Turabian Style

Myungsik Do; Wanhee Byun; Doh Kyoum Shin; Hyeryun Jin. 2019. "Factors Influencing Matching of Ride-Hailing Service Using Machine Learning Method." Sustainability 11, no. 20: 5615.

Journal article
Published: 14 November 2018 in Sustainability
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In this study, we focus on resilience as the ability of specific infrastructure systems at the regional scale to absorb the shocks of extreme events, such as earthquakes. The occurrence of a disaster such as an earthquake leads to a rapid decrease in infrastructure performance. In the case of road networks, performance might refer to the number of drivers using the road within a certain period of time. The objective of this study is to propose a quantitative evaluation method to analyze road network performance (or performance loss) when natural disasters occur. Furthermore, we use cluster analysis and consider the performance loss and asset value in an attempt to propose a method to determine the critical path that should be prioritized for maintenance. This study aimed at analyzing hazard resilience from the network aspect through a scenario analysis depending on damage recovery after disaster occurrence. This study compared the hazard resilience speed to recover existing performance according to the scenario for damage recovery targeting the selected road network. It was found that the total increase in the utility (e.g., total travel time saved) gradually diminished as the restoration cost increased.

ACS Style

Myungsik Do; Hoyong Jung. Enhancing Road Network Resilience by Considering the Performance Loss and Asset Value. Sustainability 2018, 10, 4188 .

AMA Style

Myungsik Do, Hoyong Jung. Enhancing Road Network Resilience by Considering the Performance Loss and Asset Value. Sustainability. 2018; 10 (11):4188.

Chicago/Turabian Style

Myungsik Do; Hoyong Jung. 2018. "Enhancing Road Network Resilience by Considering the Performance Loss and Asset Value." Sustainability 10, no. 11: 4188.

Journal article
Published: 03 September 2018 in Journal of Open Innovation: Technology, Market, and Complexity
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Carpooling—a concept of shared transportation for addressing traffic issues such as congestion and CO2 emissions—has been actively introduced, especially in developed countries. This study proposes a method to estimate the benefits of introducing carpooling for employees in public agencies that are transferring innovation cities. To overcome the shortcomings of previous carpooling services, a carpooling service for inter-company employees was designed and evaluated in our study. The traffic flow theory was used to estimate the direct benefits to carpooling users and the indirect benefits to express highway drivers. The results indicate that carpooling services have a significant socio-economic cost-saving effect on traffic congestion, environmental cost reduction, and so forth, and will therefore play an important role in traffic demand management.

ACS Style

Myungsik Do; Hoyong Jung. The Socio-Economic Benefits of Sharing Economy: Colleague-Based Carpooling Service in Korea. Journal of Open Innovation: Technology, Market, and Complexity 2018, 4, 40 .

AMA Style

Myungsik Do, Hoyong Jung. The Socio-Economic Benefits of Sharing Economy: Colleague-Based Carpooling Service in Korea. Journal of Open Innovation: Technology, Market, and Complexity. 2018; 4 (3):40.

Chicago/Turabian Style

Myungsik Do; Hoyong Jung. 2018. "The Socio-Economic Benefits of Sharing Economy: Colleague-Based Carpooling Service in Korea." Journal of Open Innovation: Technology, Market, and Complexity 4, no. 3: 40.

Journal article
Published: 28 February 2018 in International Journal of Highway Engineering
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ACS Style

Hoyong Jung; Seunghyun Choi; Myungsik Do. Estimation of Road-Network Performance and Resilience According to the Strength of a Disaster. International Journal of Highway Engineering 2018, 20, 35 -45.

AMA Style

Hoyong Jung, Seunghyun Choi, Myungsik Do. Estimation of Road-Network Performance and Resilience According to the Strength of a Disaster. International Journal of Highway Engineering. 2018; 20 (1):35-45.

Chicago/Turabian Style

Hoyong Jung; Seunghyun Choi; Myungsik Do. 2018. "Estimation of Road-Network Performance and Resilience According to the Strength of a Disaster." International Journal of Highway Engineering 20, no. 1: 35-45.

Journal article
Published: 23 July 2014 in International Journal of Environmental Science and Technology
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Carsharing, an alternative to car ownership, is being encouraged by many national governments as a means to alleviate air pollution and traffic congestion. Previously, many carsharing companies determined service locations through trial and error, but they currently define their parking locations in metropolitan cities for maximum customer coverage. However, identifying carsharing locations according to the experiences of the pioneering cities might not yield valid results in some Asian countries where carsharing systems are unknown. Hence, this study examines the characteristics of carsharing users in Daejeon, a small Korean city, to determine that city’s optimal carsharing service locations. A geographic information system was used to analyze and determine the best spatial areas according to two data categories: internal and external demand factors. Suitable carsharing locations were ranked by the results of a grid analysis. Thirty optimal locations were then determined from the location-allocation model in a network analysis module. Determining optimal carsharing locations should also be directly correlated with the reduction of carbon dioxide emissions. Carbon dioxide emission reduction from carsharing was predicted at 62,070 tCO2eq for the year 2013; emission reductions were predicted to increase further to 172,923 tCO2eq by 2020. Thus, carsharing is an innovative strategy for traffic demand management that can alleviate air pollution. The results of this study indicate that further research is necessary to examine the relationship between optimal carsharing locations and carbon dioxide emission reduction from using lower-emission carsharing vehicles, such as electric vehicles.

ACS Style

J.-B. Lee; Wook Byun; Sung Ho Lee; Myungsik Do. Correlation between optimal carsharing locations and carbon dioxide emissions in urban areas. International Journal of Environmental Science and Technology 2014, 11, 2319 -2328.

AMA Style

J.-B. Lee, Wook Byun, Sung Ho Lee, Myungsik Do. Correlation between optimal carsharing locations and carbon dioxide emissions in urban areas. International Journal of Environmental Science and Technology. 2014; 11 (8):2319-2328.

Chicago/Turabian Style

J.-B. Lee; Wook Byun; Sung Ho Lee; Myungsik Do. 2014. "Correlation between optimal carsharing locations and carbon dioxide emissions in urban areas." International Journal of Environmental Science and Technology 11, no. 8: 2319-2328.

Journal article
Published: 27 March 2013 in KSCE Journal of Civil Engineering
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Deterioration forecasting modeling is an essential element for an efficient pavement management system. The HDM-4 (Highway Development & Management-4) model developed by the World Bank is widely distributed to more than 100 countries around the world. However, many users often point out problems related to calibration limitations, and question the reliability of their results due to the extremely large number of variables, and difficulty in the calibration procedure of deterioration models in the HDM-4. The current calibration method based on the Network-based approach which was introduced by the HDM-4 developer and has several limitations in describing the precise deterioration progress, and practical application. In fact, many HDM-4 users often give up implementation due to these reasons. To mitigate these problems, this paper suggests an improved calibration method for the HDM-4 deterioration models relevant to the deterioration speed and shape of the curve. The benefits are not limited to only high precision calibration, but also easy application by minimum data, and covering problems on incomplete pavement inventory data which are considered the most serious problems in the application of the HDM-4. The validity of the suggested methods was empirically shown through experience with the national highways in Korea. This paper could be a good reference for the implementation of the current HDM-4 model, as well as its future improvement.

ACS Style

Daeseok Han; Kiyoshi Kobayashi; Myungsik Do. Section-based multifunctional calibration method for pavement deterioration forecasting model. KSCE Journal of Civil Engineering 2013, 17, 386 -394.

AMA Style

Daeseok Han, Kiyoshi Kobayashi, Myungsik Do. Section-based multifunctional calibration method for pavement deterioration forecasting model. KSCE Journal of Civil Engineering. 2013; 17 (2):386-394.

Chicago/Turabian Style

Daeseok Han; Kiyoshi Kobayashi; Myungsik Do. 2013. "Section-based multifunctional calibration method for pavement deterioration forecasting model." KSCE Journal of Civil Engineering 17, no. 2: 386-394.

Journal article
Published: 28 January 2011 in KSCE Journal of Civil Engineering
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The estimation of mean life reliability of highway pavement plays a central role in road maintenance and pavement management. In this paper, a methodology to estimate the mean life and failure probability in consideration of road functional characteristics based on parametric and non-parametric estimation models are presented. Based on the three types of functionally classified roads: urban, rural and recreation roads, five different lifetime distributions were tested: Normal, lognormal, exponential, Weibull, and loglogistic to select the appropriate probability distribution and to estimate mean life and failure rates. For functional classification of roads, the permanent traffic counters located along the national highway in 2007 are used. Furthermore, national highway pavement databases from 1999 to 2008 are also used for selection of optimal probability distribution and estimation of mean life for pavement. The goodness-of-fit test, such as the Anderson-Darling test, was performed to select optimal probability distribution. As a result, an appropriate distribution of each case was selected: lognormal distribution for rural roads and Weibull distribution for recreation roads. The non-parametric estimation method for rural roads was applied because there is no appropriate probability distribution for rural roads. Furthermore, in order to verify the validity of the proposed parametric and non-parametric estimation models, the applicability of the estimation methodology presented in this paper is investigated by using the empirical lifetime data of the national highway in Korea.

ACS Style

Myungsik Do. Comparative analysis on mean life reliability with functionally classified pavement sections. KSCE Journal of Civil Engineering 2011, 15, 261 -270.

AMA Style

Myungsik Do. Comparative analysis on mean life reliability with functionally classified pavement sections. KSCE Journal of Civil Engineering. 2011; 15 (2):261-270.

Chicago/Turabian Style

Myungsik Do. 2011. "Comparative analysis on mean life reliability with functionally classified pavement sections." KSCE Journal of Civil Engineering 15, no. 2: 261-270.

Journal article
Published: 08 May 2010 in KSCE Journal of Civil Engineering
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While it is impossible to estimate when a road section will collapse, the understanding of road section deterioration can help asset managers predict the condition of road sections and take appropriate actions for rehabilitations. Deterioration forecasting modeling is an essential element for an efficient pavement management system. Although the Pavement Management System (PMS) has been introduced and operated for optimal road maintenance since the late 1980s in Korea, some problems for accurate prediction of road deterioration remain due to the quality of pavement performance data and the different pavement structural, material and environmental conditions. In this paper, a methodology to estimate the Markov transition probability model is presented to forecast the deterioration process of road sections. The deterioration states of the road sections are categorized into several ranks and the deterioration processes are characterized by hazard models. The Markov transition probabilities between the deterioration states, which are defined by the non-uniform or irregular intervals between the inspection points in time, are described by the exponential hazard models. Furthermore, in order to verify the validity of the proposed method, the applicability of the estimation methodology presented in this paper is investigated by using the empirical surface data set of the national highway in Korea.

ACS Style

Kiyoshi Kobayashi; Myungsik Do; Daeseok Han. Estimation of Markovian transition probabilities for pavement deterioration forecasting. KSCE Journal of Civil Engineering 2010, 14, 343 -351.

AMA Style

Kiyoshi Kobayashi, Myungsik Do, Daeseok Han. Estimation of Markovian transition probabilities for pavement deterioration forecasting. KSCE Journal of Civil Engineering. 2010; 14 (3):343-351.

Chicago/Turabian Style

Kiyoshi Kobayashi; Myungsik Do; Daeseok Han. 2010. "Estimation of Markovian transition probabilities for pavement deterioration forecasting." KSCE Journal of Civil Engineering 14, no. 3: 343-351.