• Journal of Korean Society of Transportation
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• v.7 no.2
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• pp.29-43
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• 1989

The purpose of this study is to construct a forecasting model involved in a diverted traffic volume of the 2nd intra-urban expressway in construction presently, in the case of the future prediction of traffic demand for the intra-urban expressway in Pusan. In this study, the model involved in a diverted traffic volume is constructed trustworthy. And the future traffic demand of intra-urban expressway by this model was forecasted 114,005 volume/daily in 1996 and 147,090 volume/daily in 2001. However, it will made a study more and more concretely for practicality and limitation as well as construction of the forecasting model considered an intrinsic problem of an observational error and necessity of survey for much more socio-economic data, the traffic volume on all orad and OD pairs in Pusan.

• Korea Trade Review
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• v.44 no.2
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• pp.51-62
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• 2019

This study predicts the future world seaborne trade volume with econometrics methods using 23-year time series data provided by Clarksons. For this purpose, this study uses simple regression analysis, exponential smoothing method and seemingly unrelated regression model (SUR Model). This study is meaningful in that it predicts worldwide total seaborne trade volume and seaborne traffic in four major items (container, bulk, crude oil, and LNG) from 2019 to 2023 as there are few prior studies that predict future seaborne traffic using recent data. It is expected that more useful references can be provided to trade related workers if the analysis period was increased and additional variables could be included in future studies.

• Advances in environmental research
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• v.4 no.4
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• pp.211-218
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• 2015

Traffic noise estimation models are useful in evaluation of the noise pollution in current circumstances. They are helpful tools for design and planning new roads and highways. Measurement of average traffic noise level is possible when traffic speed and volume are known. The objective of this study was to devise a model for prediction of highway traffic noise levels based on current traffic variables in Iran. The design of this model was to take the impact of traffic congestion into consideration and to be field tested. This study is a library research augmented by field study conducted on Saeedi Highway located south west of Tehran. The period for the field study lasted 5 days from 7-12 February, 2013. This study examined liner and non-liner methods in formulation of its model. Liner method without a fixed coefficient was the best fit for the intended model. The proposed model can serve as a decision making tool to estimate the impact of key influential factors on sound pressure levels in urban areas in Iran.

• Journal of Urban Science
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• v.7 no.1
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• pp.45-54
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• 2018

The purpose of this study is to find the factors that reduce prediction error in traffic volume using highway traffic volume data. The ARIMA model was used to predict the day, and it was confirmed that weekday and weekly characteristics were distinguished by prediction error. The forecasting results showed that weekday characteristics were prominent on Tuesdays, Wednesdays, and Thursdays, and forecast errors including MAPE and MAE on Sunday were about 15% points and about 10 points higher than weekday characteristics. Also, on Friday, the forecast error was high on weekdays, similar to Sunday's forecast error, unlike Tuesday, Wednesday, and Thursday, which had weekday characteristics. Therefore, when forecasting the time series belonging to Friday, it should be regarded as a weekly characteristic having characteristics similar to weekend rather than considering as weekday.

• Journal of Environmental Health Sciences
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• v.34 no.5
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• pp.395-402
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• 2008

Road traffic noise causes considerable disturbance and annoyance in exposed inhabitants. Particularly, arterial road noise is a significant environmental problem in many urban areas in which higher traffic volume and higher car speed occur. Arterial road noise became the target of this investigation in Seoul, South Korea. Noise levels were measured at four points that were based on distance from roadside at the same measurement site and under the conditions as reported by the National Institute of Environmental Research (NIER) in 1999. The average noise levels ($L_{eq,1h}$) of the arterial road was 80.3 dBA at 5 m, 77.4 dBA at 10 m, 73.7 dBA at 20 m, 70.9 dBA at 30 m. A comparison between 1999 and 2008's measurement values has shown that in 2008 noise level is up by about 1.5 dBA, traffic volume has increased by about 15.7%, while car speed has decrease by about 8%. The relationship between 2008' measured values and predicted values using the NIER Equation is low under 10 m from the roadside. The influence range of arterial noise is calculated at 26 m for road noise limits in daytime. In relation to the comparison between traffic volume and noise level, the equivalence in traffic volume (Light car+10xHeavy car) is higher than other variables.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.321-327
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• 2023

With the development of artificial intelligence, the prediction system has become one of the essential technologies in our lives. Despite the growth of these technologies, traffic congestion at intersections in the 21st century has continued to be a problem. This paper proposes a system that predicts intersection traffic jams using a Convolutional LSTM (Conv-LSTM) algorithm. The proposed system models data obtained by learning traffic information by time zone at the intersection where traffic congestion occurs. Traffic congestion is predicted with traffic volume data recorded over time. Based on the predicted result, the intersection traffic signal is controlled and maintained at a constant traffic volume. Road congestion data was defined using VDS sensors, and each intersection was configured with a Conv-LSTM algorithm-based network system to facilitate traffic.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.6
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• pp.612-620
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• 2013

In this study, time series analysis was tried, which is widely applied to demand forecast of diverse fields such as finance, economy, trade, and so on, different from previous regression analysis. Future marine traffic volume was forecasted on the basis of data of the number of ships entering Incheon port from January 1996 to June 2013, through courses of stationarity verification, model identification, coefficient estimation, and diagnostic checking. As a result of prediction January 2014 to December 2015, February has less traffic volume than other months, but January has more traffic volume than other months. Also, it was found out that Incheon port was more proper to ARIMA model than exponential smoothing method and there was a difference of monthly traffic volume according to seasons. The study has a meaning in that future traffic volume was forecasted per month with time series model. Also, it is judged that forecast of future marine traffic volume through time series model will be the more suitable model than prediction of marine traffic volume with previous regression analysis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.76-83
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• 2020

Postal logistics organizations are characterized as having high labor intensity and short response times. These characteristics, along with rapid change in mail volume, make load scheduling a fundamental concern. Load analysis of major postal infrastructures such as post offices, sorting centers, exchange centers, and delivery stations is required for optimal postal logistics operation. In particular, the performance of mail traffic forecasting is essential for optimizing the resource operation by accurate load analysis. This paper addresses a traffic forecast problem of postal parcel that arises at delivery stations of Korea Post. The main purpose of this paper is to describe a method for predicting short-term traffic of postal parcel based on self-similarity analysis and to introduce an application of the traffic prediction model to postal logistics system. The proposed scheme develops multiple regression models by the clusters resulted from feature engineering and individual models for delivery stations to reinforce prediction accuracy. The experiment with data supplied by main postal delivery stations shows the advantage in terms of prediction performance. Comparing with other technique, experimental results show that the proposed method improves the accuracy up to 45.8%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.1-13
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• 2021

In this study, a time series analysis technique was applied to calibrate and predict traffic data for various purposes, such as planning, design, maintenance, and research. Existing algorithms have limitations in application to data such as traffic data because they show strong periodicity and seasonality or irregular data. To overcome and supplement these limitations, we applied the SARIMA model, an analytical technique that combines the autocorrelation model, the Seasonal Auto Regressive(SAR), and the seasonal Moving Average(SMA). According to the analysis, traffic volume prediction using the SARIMA(4,1,3)(4,0,3) 12 model, which is the optimal parameter combination, showed excellent performance of 85% on average. In addition to traffic data, this study is considered to be of great value in that it can contribute significantly to traffic correction and forecast improvement in the event of missing traffic data, and is also applicable to a variety of time series data recently collected.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.1
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• pp.33-41
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• 2014

Unlike the existing regression analysis, this study anticipated future marine traffic volume using time series analysis and artificial neural network model. Especially, it tried to anticipate future marine traffic volume by applying predictive value through time series analysis on artificial neural network model as an additional input variable. This study used monthly observed values of Incheon port from 1996 to 2013. In order for the verification of the forecasting of the model, value for 2013 is anticipated from the built model with observed values from 1996 to 2012 and a proper model is decided by comparing with the actual observed values. Marine traffic volume of Incheon port showed more traffic than average for May and November by 5.9 % and 4.5 % respectably, and January and August showed less traffic than average by 8.6 % and 4.7 % in 2015. Thus, it is found that Incheon port has difference in monthly traffic volume according to the season. This study can be utilized as a basis to reflect the characteristics of traffic according to the season when investigating marine traffic field observation.