• Journal of Korean Port Research
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• v.14 no.1
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• pp.66-75
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• 2000

From the traffic analysis, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results were obtained : ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy. ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period showed 10mph decrease when compared with the 24hours'average speed, but the speed did not show a big difference in the afternoon peak period. ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge section. ⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.111-121
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• 1999

From the traffic analyses, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results obtained: ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy.ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period shown 10mph decrease when compared with the 24hours' average speed, but the speed did not show a big difference in the afternoon peak period.ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge sectionⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.

• Journal of the Korea Society for Simulation
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• v.29 no.3
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• pp.41-48
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• 2020

In this paper, a dynamic prediction algorithm using the cumulative distribution function for traffic volume is presented as a new method for predicting highway traffic rate more accurately, where an approximation function of the cumulative distribution function is obtained through numerical methods such as natural cubic spline interpolation and Levenberg-Marquardt method. This algorithm is a new structure of random number generation algorithm using the cumulative distribution function used in financial mathematics to be suitable for predicting traffic flow. It can be confirmed that if the highway traffic rate is simulated with this algorithm, the result is very similar to the actual traffic volume. Therefore, this algorithm is a new one that can be used in a variety of areas that require traffic forecasting as well as highways.

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.107-116
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• 2005

The information on travel time in providing the information of traffic to drivers is one of the most important data to control a traffic congestion efficiently. Especially, this information is the major element of route choice of drivers, and based on the premise that it has the high degree of confidence in real situation. This study developed a vehicle arrival time prediction algorithm called as "VAT-DV" for 6 corridors in total 6.1Km of "Nam-san area trffic information system" in order to give an information of congestion to drivers using VMS, ARS, and WEB. The spatial scope of this study is 2.5km~3km sections of each corridor, but there are various situations of traffic flow in a short period because they have signalized intersections in a departure point and an arrival point of each corridor, so they have almost characteristics of interrupted and uninterrupted traffic flow. The algorithm uses the information on a demand volume and a queue length. The demand volume is estimated from density of each points based on the Greenburg model, and the queue length is from the density and speed of each point. In order to settle the variation of the unit time, the result of this algorithm is strategically regulated by importing the AVI(Automatic Vehicle Identification), one of the number plate matching methods. In this study, the AVI travel time information is composed by Hybrid Model in order to use it as the basic parameter to make one travel time in a day using ILD to classify the characteristics of the traffic flow along the queue length. According to the result of this study, in congestion situation, this algorithm has about more than 84% degree of accuracy. Specially, the result of providing the information of "Nam-san area traffic information system" shows that 72.6% of drivers are available.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.131-140
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• 1999

Today freeway is experiencing a severe congestion with incoming or outgoing traffic through freeway ramps during the peak periods. Thus, the objectives of this study is to identify the traffic characteristics, analyze the relationships between the traffic characteristics and finally construct the delay predictive models on the ramp junctions of freeway with 70mph speed limit. From the traffic analyses, and model constructions and verifications for delay prediction on the ramp junctions of freeway, the following results were obtained: ⅰ) Traffic flow showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy also showed a big difference depending on the time periods, and the downstream occupancy(Od) was especially shown to have a higher explanatory power for the delay predictive model construction on the ramp junction of freeway. ⅲ) The speed-occupancy curve showed a remarkable shift based on the occupancies observed ; Od < 9% and Od$\geq$9%. Especially, volume and occupancy were shown to be highly explanatory for delay prediction on the ramp junctions of freeway under Od$\geq$9%, but lowly for delay predicion on the ramp junctions of freeway under Od<9%. Rather, the driver characteristics or transportation conditions around the freeway were through to be a little higher explanatory for the delay perdiction under Od<9%. ⅳ) Integrated delay predictive models showed a higher explanatory power in the morning peak period, but a lower explanatory power in the non-peak periods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.5
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• pp.988-995
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• 2009

One of the most popular services of Telematics is a shortest path finding from a starting point to a destination. In this paper, a dynamic shortest path finding system with forecasting result of traffic flow in the future was developed and various experiments to verify the performance of our system using real-time traffic information has been conducted. Traffic forecasting has been done by a prediction system using Bayesian network. It searched a dynamic shortest path, a static shortest path and an accumulated shortest path for the same starting point and destination and calculated their travel time to compare with one of its real shortest path. From the experiment, over 75%, the travel time of dynamic shortest paths is the closest to one of their real shortest paths than one of static shortest paths and accumulated shortest paths. Therefore, it is proved that finding a dynamic shortest path by applying traffic flows in the future for intermediated intersections can give more accurate traffic information and improve the quality of services of Telematics than finding a static shortest path applying by traffic flows of the starting time for intermediated intersections.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.5 no.1 s.9
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• pp.1-12
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• 2006

In order to estimate a dynamic origin and destination demand between on and off-ramps in the freeways, a traffic flow theory can be used to calculate a link distribution proportion of traffics moving between them. We have developed a dynamic traffic estimation model based on the three-phase traffic theory (Kerner, 2004), which explains the complexity of traffic phenomena based on phase transitions among free-flow, synchronized flow and moving jam phases, and on their complex nonlinear spatiotemporal features. The developed model explains and estimates traffic congestion in terms of speed breakdown, phase transition and queue propagation. We have estimated the link, on and off-ramp volumes at every time interval by using traffic data collected from vehicle detection systems in Korea freeway sections. The analyzed results show that the developed model describes traffic flows adequately.

• Journal of Navigation and Port Research
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• v.36 no.7
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• pp.591-597
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• 2012

Generally an urban freeway means a primary arterial which provides road users with a free-flow speed, except for ramp junctions during rush hours. However, most road users suffer from traffic congestion in the basic segments as well as in the ramp junctions of urban freeway during rush hours, because most road users prefer urban freeways to local roads in the urban areas. This study then intends to analyze lane traffic characteristics of urban freeway basic segments having more freight vehicles during rush hours, find the lane showing a high correlation with the segment speed between lane speeds, and finally suggest a segment-speed predictive model by the lane speed of urban freeway basic segments during rush hours.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1312-1318
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• 2006

One of the common services of telematics is the car navigation that finds the shortest path from source to target. Until now, some routing algorithms of the car navigation do not consider the real-time traffic information and use the static shortest path algorithm. In this paper, we prosed the method to predict the traffic flow in the future. This prediction combines two methods. The former is an accumulated speed pattern, which means the analysis results for all past speeds of each road by classfying the same day and the same time inteval. The latter is the Kalman filter. We predicted the traffic flows of each segment by combining the two methods. By experiment, we showed our algorithm gave better precise predicition than only using accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.3
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• pp.215-222
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• 2022

The purpose of air traffic flow management is to balance demand and capacity in the national airspace, and its performance relies on an accurate capacity prediction of the airport or airspace. This paper developed a regression model that predicts the number of aircraft actually departing and arriving in a terminal maneuvering area. The regression model is based on a boosting ensemble learning algorithm that learns past aircraft operational data such as time, weather, scheduled demand, and unfulfilled demand at a specific airport in the terminal maneuvering area. The developed model was tested using historical departure and arrival flight data at Incheon International Airport, and the coefficient of determination is greater than 0.95. Also, the capacity of the terminal maneuvering area of interest is implicitly predicted by using the model.