• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.3
• /
• pp.59-73
• /
• 2021

There are many lives lost due traffic accidents, and which have not decreased despite advances in technology. In order to prevent traffic accidents, it is necessary to accurately forecast how they will change in the future. Until now, traffic accident-frequency forecasting has not been a major research field, but has been analyzed microscopically by traditional methods, mainly based on statistics over a previous period of time. Despite the recent introduction of AI to the traffic accident field, the focus is mainly on forecasting traffic flow. This study converts into time series data the records from 1,339,587 traffic accidents that occurred in Korea from 2014 to 2019, and uses the AI algorithm to forecast the frequency of traffic accidents based on driver's age and time of day. In addition, the forecast values and the actual values were compared and verified based on changes in the traffic environment due to COVID-19. In the future, these research results are expected to lead to improvements in policies that prevent traffic accidents.

• Journal of the Korea Safety Management & Science
• /
• v.13 no.1
• /
• pp.91-98
• /
• 2011

We develop a model to predict traffic accidents in Korea. In contrast to the classical approach that mainly uses regression analysis, Bayesian approach is adopted. A dependent model that incorporates the data from different kinds of accidents is introduced. The rate of severe accident can be updated even with no data of the same kind. The data of minor accident that can be obtained frequently is efficiently used to predict the severe accident.

• Journal of Korean Society of Transportation
• /
• v.18 no.3
• /
• pp.55-76
• /
• 2000

We propose a general solution methodology for identifying the optimal aggregation interval sizes as a function of the traffic dynamics and frequency of observations for four cases : i) link travel time estimation, ii) corridor/route travel time estimation, iii) link travel time forecasting. and iv) corridor/route travel time forecasting. We first develop statistical models which define Mean Square Error (MSE) for four different cases and interpret the models from a traffic flow perspective. The emphasis is on i) the tradeoff between the Precision and bias, 2) the difference between estimation and forecasting, and 3) the implication of the correlation between links on the corridor/route travel time estimation and forecasting, We then demonstrate the Proposed models to the real-world travel time data from Houston, Texas which were collected as Part of the Automatic Vehicle Identification (AVI) system of the Houston Transtar system. The best aggregation interval sizes for the link travel time estimation and forecasting were different and the function of the traffic dynamics. For the best aggregation interval sizes for the corridor/route travel time estimation and forecasting, the covariance between links had an important effect.

• ETRI Journal
• /
• v.41 no.2
• /
• pp.224-234
• /
• 2019

This paper proposes a new spectrum forecasting (SF) model to estimate the spectrum demands for future mobile broadband (MBB) services. The model requires five main input metrics, that is, the current available spectrum, site number growth, mobile data traffic growth, average network utilization, and spectrum efficiency growth. Using the proposed SF model, the future MBB spectrum demand for Malaysia in 2020 is forecasted based on the input market data of four major mobile telecommunication operators represented by A-D, which account for approximately 95% of the local mobile market share. Statistical data to generate the five input metrics were obtained from prominent agencies, such as the Malaysian Communications and Multimedia Commission, OpenSignal, Analysys Mason, GSMA, and Huawei. Our forecasting results indicate that by 2020, Malaysia would require approximately 307 MHz of additional spectrum to fulfill the enormous increase in mobile broadband data demands.

• Electronics and Telecommunications Trends
• /
• v.34 no.3
• /
• pp.106-113
• /
• 2019

Mobile traffic is one of the most important indexes of the growth of the mobile communications market, and it has a close relationship with subscribers' service usage patterns, frequency demand and supply, network management, and information communication policy. The purpose of this paper is to understand mobile data usage in Korea and to suggest the optimal steps for establishing the frequency supply and demand system by researching the traffic trends that reflect the characteristics of radio resources in the mobile communications field. To achieve this goal, attempts were made to increase the possibility of policy use by analyzing and forecasting mobile traffic trends, and to improve the accuracy of the research through the verification of the existing prediction results. The paper ends with a discussion of the necessity of a frequency management system based on data science.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.11 no.6
• /
• pp.145-154
• /
• 2012

This paper analyzed the characteristics of traffic accidents in all tunnels on nationwide freeways and selected some various independent variables related to accident occurrence in tunnels. The study aims to develop reliable accident forecasting models using the various dependent variables such as the number of accident (no.), no./km, and no./MVK. Finally, reliable multiple linear regression models were proposed in this paper. This study tested the validity verification of developed models through statistics such as $R^2$, F values, multicollinearity, residual analysis. The paper selected the accident forecasting models considering the characteristics of tunnel accidents and two models were finally proposed according to two groups of tunnel length. In the selected models, natural logarithm of ln(no./MVK) is used for the dependent variable and AADT, vertical slope, and tunnel hight are used for the independent variables. The reliability of two models was proved by the comparison analysis between field data and estimating data using RMSE and MAE. These models may be not only effective in evaluating tunnel safety under design and planning phases of tunnel but also useful to reduce traffic accidents in tunnels and to manage the traffic flow of tunnel.

• Communications for Statistical Applications and Methods
• /
• v.19 no.3
• /
• pp.415-422
• /
• 2012

In this paper, we first the compare the performance of Holt-Winters, FSARIMA, AR-GARCH and Seasonal AR-GARCH models with in the short term based data. The results of the compared data show that the Holt-Winters model outperformed other models in terms of forecasting accuracy.

• Land and Housing Review
• /
• v.2 no.3
• /
• pp.217-226
• /
• 2011

The Seoul Metropolitan Government is striving to minimize the amount of traffic according to the supply of apartment houses along with the solution of housing shortage for the low income people through high density development near the subway area. Therefore, a stronger policy is necessary to control the traffic of the passenger cars in a subway area for the successful high density development focusing on public transportation, and especially, the estimation of the demand of cars with high reliability is necessary to control the demand of parking such as the limited supply of parking lot. Accordingly, this study developed car ownership forecasting model using Look-up Table among category analyses which are easy to be applied and have high reliability. The estimation method using Look-up-Table is possible to be applied to both measurable and immeasurable types, easy to accumulate data, and features the flexible responding depending on the changes of conditions. This study established Look-up-Table model through the survey of geographical location, the scale of housing, the accessible distance to a subway station and to a bus station, the number of bus routes, and the number of car owned with data regarding 242 blocks in Seoul City as subjects.

• Journal of Korean Society of Transportation
• /
• v.27 no.2
• /
• pp.133-144
• /
• 2009

In Korea, the ITS project has been progressed to improve traffic mobility and safety. Further, it is to relieve traffic jam by supply real time travel information for drivers and to promote traffic convenience and safety. It is important that the traffic information is provided accurately. This study was conducted outlier elimination and missing data adjustment to improve accuracy of raw data. A method for raise reliability of travel time prediction information was presented. We developed Historical Profile model and adjustment formula to reflect quality of interrupted flow. We predicted travel time by developed Historical Profile model and adjustment formula and verified by comparison between developed model and existing model such as Neural Network model and Kalman Filter model. The results of comparative analysis clarified that developed model and Karlman Filter model similarity predicted in general situation but developed model was more accurate than other models in incident situation.

• The Korean Journal of Applied Statistics
• /
• v.24 no.2
• /
• pp.307-313
• /
• 2011

In this paper, we compare performance of three models. The Holt-Winters, FARIMA and ARGARCH models, are used in predicting internet traffic data for analysis of traffic characteristics. We first introduce the time series models and apply them to real traffic data to forecast. Finally, we examine which model is the most suitable for explaining the long memory, the characteristics of the traffic material, and compare the respective prediction performance of the models.