• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.391-393
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• 2022

In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.146-164
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• 2022

Recently, the Korean government promoted the construction of metropolitan express subway to connect major transportation hub in the metropolitan area within 30 minutes. Most stations of the metropolitan express subway are connected to existing subway stations, so the importance of transfer increased. Although many studies have been conducted on the effect of transfer penalty on route choice, there are few studies on the transfer behavior of the metropolitan express subway. Therefore, in this study, a transfer behavior analysis was conducted on the Shinbundang Line, a representative metropolitan express subway. To analyze the transfer behavior according to the degree of traffic congestion and the presence of fare payment, route choice models were made using transport card data divided according to week, time, and user characteristics. As a result of the analysis, users of the metropolitan express subway had greater disutility to the transfer waiting time compared to the transfer moving time. Furthermore, especially during the peak time, EIVM(Equivalent in-vehicle minutes) of the transfer waiting time was 3.51. In this study, EIVM for metropolitan express subway users were analyzed to be 2.6 minutes, which is significantly lower than the results of previous studies on subways. This suggests that there is a difference in the transfer penalty between subways and metropolitan express subway, and that it is necessary to apply the transfer penalty between subways and express subway differently when forecasting subway traffic demand.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.32-48
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• 2023

Autonomous driving technology is shaping the future of personalized travel, encouraging personalized travel, and traffic impact could be influenced by individualized travel behavior during the transition of driving entity from human to machine. In order to evaluate traffic impact, it is necessary to estimate the total number of trips based on an understanding of individual travel characteristics. The Activity-based model(ABM), which allows for the reflection of individual travel characteristics, deals with all travel sequences of an individual. Understanding the relationship between travel and travel must be important for assessing traffic impact using ABM. However, the ABM has a limitation in the data hunger model. It is difficult to adjust in the actual demand forecasting. Therefore, we utilized a Tour-based model that can explain the relationship between travels based on household travel survey data instead. After that, vehicle registration and population data were used for correction. The result showed that, compared to the KTDB one, the traffic generation exhibited a 13% increase in total trips and approximately 9% reduction in working trips, valid within an acceptable margin of error. As a result, it can be used as a generation correction method based on Tour, which can reflect individual travel characteristics, prior to building an activity-based model to predict demand due to the introduction of autonomous vehicles in terms of road operation, which is the ultimate goal of this study.

• International Journal of Highway Engineering
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• v.12 no.4
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• pp.93-100
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• 2010

Trip generation is the first step in the conventional four-step model and has great effects on overall demand forecasting, so accuracy really matters at this stage. A linear regression model is widely used as a current trip generation model for such plans as urban transportation and SOC facilities, assuming that the relationship between each socio-economic index and trip generation stays linear. But when rapid urban development or an urban planning structure has changed, socio-economic index data for trip estimation may be lacking to bring many errors in estimated trip. Hence, instead of assuming that a socio-economic index widely used for a general purpose, this study aims to develop a new trip generation model by type based on the market separation for the variables to reflect the characteristics of various zones. The study considered the various characteristics (land use, socio-economic) of zones to enhance the forecasting accuracy of a trip generation model, the first-step in forecasting transportation demands. For a market separation methodology to improve forecasting accuracy, data mining (CART) on the basis of trip generation was used along with a regression analysis. Findings of the study indicated as follows : First, the analysis of zone characteristics using the CART analysis showed that trip production was under the influence of socio-economic factors (men-women relative proportion, age group (22 to 29)), while trip attraction was affected by land use factors (the relative proportion of business facilities) and the socio-economic factor (the relative proportion of third industry workers). Second, model development by type showed as a result that trip generation coefficients revealed 0.977 to 0.987 (trip/person) for "production" 0.692 to 3.256 (trip/person) for "attraction", which brought the necessity for type classifications. Third, a measured verification was conducted, where "production" and "attraction" showed a higher suitability than the existing model. The trip generation model by type developed in this study, therefore, turned out to be superior to the existing one.

• Korean Journal of Construction Engineering and Management
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• v.16 no.4
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• pp.12-20
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• 2015

Construction industry has played a pivotal role in the national economy, but the crisis situation of a construction industry has been worse due to the lack of recognition of the contribution of a construction industry. In particular, the transport sector is responsible for a critical function in the movement of humans and material resources, and has a profound impact on national competitiveness and the peoples' welfare, which requires quantitative analysis. In this study, economic contribution and impact of the transportation sector are measured based on the input-output model. Road and railway facilities account for 1.03% and 0.165% of the total industry respectively, and consist of a final demand and total output. Although value-added inducing effect is small, production inducing effect and backward linkage effect has been high. The results in this study will be used as the basic information for validity of investment and policy decisions.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.17-24
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• 2008

Traffic accident forecasting model has been developed steadily to understand factors affecting traffic accidents and to reduce them. In Korea, the length of highways is over 3,000km, and it is within the top ten in the world. However, the number of accidents-per-one kilometer highway is higher than any other countries. The rapid increase of travel demand and transportation infrastructures since 1980's may influence on the high rates of traffic accident. Accident severity is one of the important indices as well as the rate of accident and factors such as road geometric conditions, driver characteristics and type of vehicles may be related to traffic accident severity. However, since all these factors are interacted complicatedly, the interactions are not easily identified. A structural equations model is adopted to capture the complex relationships among variables. In the model estimation, we use 2,880 accident data on highways in Korea. The SEM with several factors mentioned above as endogenous and exogenous variables shows that they have complex and strong relationships.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.19-28
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• 2009

In case of land development projects constructed, to solve induced transportation volume needs analysis of traffic demand. Trip-generation of land development projects is exactly predicted by using traffic instigating-basic-unit in each facility of land developments. But in case of a phase of trip-distribution, because a range of destinations is very enormous and it needs enormous data to reflect all of its characters, whenever trip-distribution is predicted, the method which assumes the rate of trip-distribution is same both before completion of land development projects and after is often used. But because there is no exact criterion, the method suggested above is also affected by subjective opinion. Accordingly, this study look over using trip-distribution of specific areas's DB and suggests a size of zone to predict a distribution of land development projects exactly. Also production - constrained gravity model which uses the gap between a distribution of suggested ranges and induced land development project is suggested for more exact prediction of trip-distribution. Besides accuracy of prediction is scrutinized by using Mean Squared Error.

• Journal of the Korean Society for Railway
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• v.20 no.2
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• pp.265-274
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• 2017

For accurate demand forecasting of railway logistics, we estimated intercity freight mode choice models based on the binary logit model and using production-consumption data from the Korea Transport Database. We estimated two types of models and compared the results by major item of railway logistics, such as container, cement, and steel: 1) The aggregate freight mode choice models are based on the revealed preference (RP) data and 2) The disaggregate models are based on the stated preference (SP) data. With respect to the container, the travel time variable was found to be statistically significant; however, the travel cost variable was not statistically significant in the RP model, while the travel cost variable was statistically significant in the SP model. For cement and steel, the travel cost variables were statistically significant but the travel time variables were not statistically significant in either the RP or the SP models. These results are inconsistent with results from previous studies based on SP data, which showed that the travel time variables were significant. Consequently, it can be concluded that the travel time factor should be considered in container transport, but that this factor is negligible for cement and steel transport.

• Journal of the Korean Society for Railway
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• v.18 no.4
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• pp.391-400
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• 2015

This research used a survey of 350 staff members working at Korail with the purpose of analyzing influence factors for the railway tourism policy for job satisfaction; results were presented for the proposed factor. First, we selected the influence factor through precedent research related to the railway tourism policy. Second, the selected influence factor and the extent of satisfaction were used to determine whether or not any kind of difference existed according to individual attributes of the railway employees. Finally, we analyzed what the influence factor was between the category factor and the sub-category factor. In conclusion, it was found that government subsidy had a meaningful correlation with infrastructure expansion and the improvement of the railway business as it is connected to tourism efficiency. Human resources have a meaningful correlation with the needs of educational institutions and the retaining of talent. Railway tourism production has a meaningful correlation with railway tour production as it is conducted to satisfy tourists and the consortium. The shift of viewpoint has a meaningful correlation with the escape from the peace-at-any price principle and demand forecasting.

• Journal of Korean Society of Transportation
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• v.24 no.5 s.91
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• pp.89-108
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• 2006

Korea has been entering the ageing society as the population of age over 65 shared over 7% since the year 2000. The ageing society needs to have transportation facility considering elderly people's travel behavior. This study aims to understand the elderly people's travel behavior using recent data in Korea. The activity schedule approach begins with travel outcomes are part of an activitv scheduling decision. For tho?e approach. used discrete choice models (especially. Nested Logit Model) to address the basic modeling problem capturing decision interaction among the many choice dimensions of the immense activity schedule choice set The day activity schedule is viewed as a sot of tours and at-home activity episodes tied togather with overarching day activity pattern using the Seoul Metropolitan Area Transportation Survey data, which was conducted in June, 2002. Decisions about a specific tour in the schedule are conditioned by the choice of day activity pattern. The day activity scheduling model estimated in this study consists of tours interrelated in a day activity pattern. The day activity pattern model represents the basic decision of activity participation and priorities and places each activity in a configuration of tours and at-home episodes. Each pattern alternative is defined by the primary activity of the day, whether the primary activity occurs at home or away, and the type of tour for the primary activity. In travel mode choice of the elderly and non-workers, especially, travel cost was found to be important in understanding interpersonal variations in mode choice behavior though, travel time was found to be less important factor in choosing travel mode. In addition, although, generally, the elderly was likely to choose transit mode, private mode was preferred for the elderly over 75 years old owing to weakened physical health for such things as going up and down of stairs. Therefore. as entering the ageing society, transit mode should be invested heavily in transportation facility Planning tor improving elderly transportation service. Although the model has not yet been validated in before-and-after prediction studies. this study gives strong evidence of its behavioral soundness, current practicality. and potential for improving reliability of transportation Projects superior to those of the best existing systems in Korea.