• International Journal of Highway Engineering
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• v.15 no.3
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• pp.93-101
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• 2013

PURPOSES : This study is to investigate the relationship of socioeconomic characteristics and road network structure with traffic growth patterns. The findings is to be used to tweak traffic forecast provided by traditional four step process using relevant socioeconomic and road network data. METHODS: Comprehensive statistical analysis is used to identify key explanatory variables using historical observations on traffic forecast, actual traffic counts and surrounding environments. Based on statistical results, a multiple regression model is developed to predict the effects of socioeconomic and road network attributes on traffic growth patterns. The validation of the proposed model is also performed using a different set of historical data. RESULTS : The statistical analysis results indicate that several socioeconomic characteristics and road network structure cleary affect the tendency of over- and under-estimation of road traffics. Among them, land use is a key factor which is revealed by a factor that traffic forecast for urban road tends to be under-estimated while rural road traffic prediction is generally over-estimated. The model application suggests that tweaking the traffic forecast using the proposed model can reduce the discrepancies between the predicted and actual traffic counts from 30.4% to 21.9%. CONCLUSIONS : Prediction of road traffic growth patterns based on surrounding socioeconomic and road network attributes can help develop the optimal strategy of road construction plan by enhancing reliability of traffic forecast as well as tendency of traffic growth.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.1037-1044
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• 2008

In this paper, we show the performance of the power transformation GARCH(PGARCH) model to analyze the internet traffic data. The long memory property which is the typical characteristic of internet traffic data can be explained by the PGARCH model rather than the linear GARCH model. Small simulation and the analysis of the real internet traffic show the out-performance of the PARCH MODEL over the linear GARCH one.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.6
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• pp.1033-1041
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• 2017

Path travel time based on departure time (PTTDP) is key information in advanced traveler information systems (ATIS). Despite the necessity, forecasting PTTDP is still one of challenges which should be successfully conquered in the forecasting area of intelligent transportation systems (ITS). To address this problem effectively, a methodology to dynamically predict PTTDP between motorway interchanges is proposed in this paper. The method was developed based on the relationships between traffic demands at motorway tollgates and PTTDPs between TGs in the motorway network. Two different data were used as the input of the model: traffic demand data and path travel time data are collected by toll collection system (TCS) and dedicated short range communication (DSRC), respectively. The proposed model was developed based on k-nearest neighbor, one of data mining techniques, in order for the real applications of motorway information systems. In a feasible test with real-world data, the proposed method performed effectively by means of prediction reliability and computational running time to the level of real application of current ATIS.

• Journal of Navigation and Port Research
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• v.32 no.1
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• pp.81-88
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• 2008

The forecast of a container traffic has been very important for port plan and development. Generally, statistic methods, such as regression analysis, ARIMA, have been much used for traffic forecasting. Recent research activities in forecasting with artificial neural networks(ANNs) suggest that ANNs can be a promising alternative to the traditional linear methods. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. The results with port traffic data indicate that effectiveness can differ according to the characteristics of ports.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2007.12a
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• pp.259-260
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• 2007

The forecast of a container traffic has been very important for port plan and development Generally, statistic methods, such as regression analysis, ARIMA, have been much used for traffic forecasting. Recent research activities in forecasting with artificial neural networks(ANNs) suggest tint ANNs am be a promising alternative to the traditional linear methods. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. The results with port traffic data indicate tint effectiveness can differ according to the ch1racteristics of ports.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.54-61
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• 2019

Recently, IITS (intelligent integrated transportation system) has been important topic in Smart City related industry. As a main objective of IITS, prevention of traffic jam (due to car accidents) has been attempted with help of advanced sensor and communication technologies. Studies show that car accident has certain correlation with some factors including characteristics of location, weather, driver's behavior, and time of day. We concentrate our study on observing auto correlativity of car accidents in terms of time of day. In this paper, we performed the ARIMA tests including ADF (augmented Dickey-Fuller) to check the three factors determining auto-regressive, stationarity, and lag order. Summary on forecasting of hourly car crash counts is presented, we show that the traffic accident data obtained in Korea can be applied to ARIMA model and present a result that traffic accidents in Korea have property of being recurrent daily basis.

• Journal of Korean Society of Transportation
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• v.13 no.1
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• pp.63-82
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• 1995

This paper deals with the forecasting model for traffic accident. Its objective is to develop the appropriate model to project the accident of Chung-Chong Region. Two types of models between motorization (M) and personal hazard (P) are tested : One is inverted-U (bell type) curve and the other is increasing (or decreasing) curve. The statiscal and sensitivity analyses show that exponential model (type III) and multiplicative model (type II) are well fit to the given cross-sectional and time-series accident data. The model projects that the fatality per 100, 000 persons of Chung-Chong region, when the motorization level (M) is 0.2, would be in the range between 18 and 77 persons. The paper concludes that the accident level is the function of motorization and the result of implementing the safety policy of a region.

• The Journal of Information Systems
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• v.26 no.3
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• pp.171-185
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• 2017

Purpose Recently, there has been an increase in attempts to analyze social phenomena, consumption trends, and consumption behavior through a vast amount of customer data such as web search traffic information and social buzz information in various fields such as flu prediction and real estate price prediction. Internet portal service providers such as google and naver are disclosing web search traffic information of online users as services such as google trends and naver trends. Academic and industry are paying attention to research on information search behavior and utilization of online users based on the web search traffic information. Although there are many studies predicting social phenomena, consumption trends, political polls, etc. based on web search traffic information, it is hard to find the research to explain and predict tourism demand and establish tourism policy using it. In this study, we try to use web search traffic information to explain the tourism demand for major cities in Gangwon-do, the representative tourist area in Korea, and to develop a nowcasting model for the demand. Design/methodology/approach In the first step, the literature review on travel demand and web search traffic was conducted in parallel in two directions. In the second stage, we conducted a qualitative research to confirm the information retrieval behavior of the traveler. In the next step, we extracted the representative tourist cities of Gangwon-do and confirmed which keywords were used for the search. In the fourth step, we collected tourist demand data to be used as a dependent variable and collected web search traffic information of each keyword to be used as an independent variable. In the fifth step, we set up a time series benchmark model, and added the web search traffic information to this model to confirm whether the prediction model improved. In the last stage, we analyze the prediction models that are finally selected as optimal and confirm whether the influence of the keywords on the prediction of travel demand. Findings This study has developed a tourism demand forecasting model of Gangwon-do, a representative tourist destination in Korea, by expanding and applying web search traffic information to tourism demand forecasting. We compared the existing time series model with the benchmarking model and confirmed the superiority of the proposed model. In addition, this study also confirms that web search traffic information has a positive correlation with travel demand and precedes it by one or two months, thereby asserting its suitability as a prediction model. Furthermore, by deriving search keywords that have a significant effect on tourism demand forecast for each city, representative characteristics of each region can be selected.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.3
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• pp.521-528
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• 2016

The tollbooths of a main motorway toll plaza are usually operated proactively responding to the variations of traffic demands of two-type vehicles, i.e. cars and the other (heavy) vehicles, respectively. In this vein, it is one of key elements to forecast accurate traffic volumes for the two vehicle types in advanced tollgate operation. Unfortunately, it is not easy for existing univariate short-term prediction techniques to simultaneously generate the two-vehicle-type traffic demands in literature. These practical and academic backgrounds make it one of attractive research topics in Intelligent Transportation System (ITS) forecasting area to forecast the future traffic volumes of the two-type vehicles at an acceptable level of accuracy. In order to address the shortcomings of univariate short-term prediction techniques, a Multiple In-and-Out (MIO) forecasting model to simultaneously generate the two-type traffic volumes is introduced in this article. The MIO model based on a non-parametric approach is devised under the on-line access conditions of large-scale historical data. In a feasible test with actual data, the proposed model outperformed Kalman filtering, one of a widely-used univariate models, in terms of prediction accuracy in spite of multivariate prediction scheme.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.111-117
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• 2017

The purpose of forecasting the traffic accident is to reduce the traffic accident. Therefore, the goal of this study is to provide severity of the accident by Forecasting of Probability of Accident. In Korea, accident data are distributed to the public via internet that includes numbers of accident and fatality as well. And crude level of accident severity in accordance with weather information for metropolitan city level are available by weekly. However, It can not reflect personal needs at specific origin of the travel for a certain traveller. This study aims to consider 68 major intersections with precipitation data, and eventually introduces link based accident severity. In estimating the accident severity both dynamic data such as drivers' characteristics, driving conditions and static data such as geometry of road, intersection characteristics are considered. Also, we identifies accident severity according to the accident type - 'vehicle to vehicle,' 'vehicle to person.' Finally, the outcomes of this study suggests taylor-made accident severity information for a specific traveller for a certain route.