• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.8
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• pp.1420-1426
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• 2008

This paper derives medium access delay of each AC(Access Category) in a stationary station using inter-departure time of IEEE 802.11e WLAN(Wireless LAN). Inter-departure time of IEEE 802.11e WLAN is obtained using saturation throughput of IEEE 802.11e WLAN which was the topic of our prior work. By using a simple queueing model, we evaluate mean queue length and mean delay for each AC on stationary condition. We calculate performance of each AC using this simple model and validate results using simulation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.137-154
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• 2019

This study analysed the center control emergency vehicle preemption[EVP] test result on the 1.782 km section around Gangbuk Fire Station. The pros and cons between center control and site control EVP was compared through the review of existing research. The test site was selected based on the higher link speed for choosing low congested area and 4 to 6 lane road. EVP operates green extension under the estimated arrival time to each intersection. This study is about EVP system field application and its evaluation by analyzing EVP operation result with the emergency vehicle's trace, GPS data. The impact on the surrounding traffic was analysed in delay from the queue length survey. Analysis showed the decrease in averge travel time 41.81%, but the increase in delay of surrounding traffic slightly. It is expected that EVP can be applied to the expanded area by researching EVP compensation scheme.

• Journal of Korean Society of Transportation
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• v.20 no.3
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• pp.17-30
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• 2002

Incident duration prediction is one of the most important steps of the overall incident management process. An accurate and reliable estimate of the incident duration can be the main difference between an effective incident management operation and an unacceptable one since, without the knowledge of such time durations, traffic impact can not be estimated or calculated. This research presents several multiple linear regression models for incident duration prediction using data consisting of 384 incident cases. The main source of various incident cases was the Traffic Incident Reports filled out by the Motorist Assistant Units of the Korea Highway Corporation. The models were proposed separately according to the time of day(daytime vs. nighttime) and the fatality/injury incurred (fatality/injury vs. property damage only). Two models using an integrated dataset, one with an intercept and the other without it, were also calibrated and proposed for the generality of model application. Some findings are as follows ; ?Variables such as vehicle turnover, load spills, the number of heavy vehicles involved and the number of blocked lanes were found to significantly affect incident duration times. ?Models, however, tend to overestimate the duration times when a dummy variable, load spill, is used. It was simply because several of load spill incidents had excessively long clearance times. The precision was improved when load spills were further categorized into "small spills" and "large spills" based on the size of vehicles involved. ?Variables such as the number of vehicles involved and the number of blocked lanes found not significant when a regression model was calibrated with an intercept. whereas excluding the intercept from the model structure signifies those variables in a statistical sense.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.276-290
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• 2023

Several efforts in Korea are currently underway with the goal of commercializing autonomous vehicles. Hence, various studies are emerging on autonomous vehicles that drive safely and quickly according to operating guidelines. The current study examines the path search of an autonomous vehicle from a microscopic viewpoint and tries to prove the efficiency required by learning the lane change of an autonomous vehicle through Deep Q-Learning. A SUMO was used to achieve this purpose. The scenario was set to start with a random lane at the starting point and make a right turn through a lane change to the third lane at the destination. As a result of the study, the analysis was divided into simulation-based lane change and simulation-based lane change applied with Deep Q-Learning. The average traffic speed was improved by about 40% in the case of simulation with Deep Q-Learning applied, compared to the case without application, and the average waiting time was reduced by about 2 seconds and the average queue length by about 2.3 vehicles.

• The Transactions of the Korea Information Processing Society
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• v.3 no.2
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• pp.369-380
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• 1996

Inductive Loop Detector(ILD) has been commonly used in collecting traffic data such as occupancy time and non-occupancy time. From the data, the traffic volume and type of passing vehicle is calculated. To provide reliable data for traffic control and plan, accuracy is required in type recognition which can be utilized to determine split of traffic signal and to provide forecasting data of queue-length for over-saturation control. In this research, a new recognition model issuggested for recognizing typeof vehicle from thecollected data obtained through ILD systems. Two clustering methods, based on statistical algorithms, and one neural network clustering method were employed to test the reliability and occuracy for the methods. In a series of experiments, it was found that the new model can greatly enhance the reliability and accuracy of type recongition rate, much higher than conventional approa-ches. The model modifies the neural network clustering method and enhances the recongition accuracy by iteratively applying the algorithm until no more unclustered data remains.

• Journal of Korean Society of Transportation
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• v.31 no.4
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• pp.3-17
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• 2013

The purpose of this study is to develop a traffic assignment model where the variable of signal intersection delay is taken into account in assigning traffic in large-scale network settings. Indeed, despite the fact that the majority of the increase in travel time or cost involving congested urban network or interrupted flow are accounted for by stop delays or congested delays at signal intersections, the existing traffic assignment models did not reflect this. The traffic assignment model considering intersection delays presented in this study was built based on the existing traffic assignment models, which were added to by the analysis technique for the computation of intersection delay provided in Korea Highway Capacity Manual. We can conclude that a multiple variety of simulation tests prove that this model can be applied to real network settings. Accordingly, this model shows the possibility of utilizing a model considering intersection delay for traffic policy decisions through analysis of effects of changes in traffic facilities on large urban areas.

• Journal of Korean Society of Transportation
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• v.24 no.5 s.91
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• pp.7-18
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• 2006

Data for travel information Provision are regularly aggregated to Provide travel time information in a reliable and convenient manner and to manage traffic data and information efficiently. In most of practices in Korea, the GPS based travel time data are mainly aggregated every 5 minutes As a result, some probes can't pass by a link within aggregation interval and thereby create uncompleted link passing data. But these data are mostly generated during the congested times and therefore a method that uses such uncompleted link passing data are required. This study estimated queue dissipation length, green time and cycle that use GPS spot speed and developed a link travel time estimation method using such uncompleted link passing data. It also presents method and the overall process of using such data to estimate link travel time in a more accurate manner. As a result, MAPE 1.98% and MAE 4.75 sec of link travel time accuracy improvement has been reported, which is not much different from the real link travel time. The method Proposed here would be an alternative to increase the amount of GPS probe data, especially in congested urban arterial case.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.41-49
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• 2008

Signalized intersection analysis using lane groups often generalizes distinct characteristics of individual lanes. An individual lane level analysis procedure is proposed. The method can assess individual lane LOS without additional data collection efforts. Analysis results of the proposed method were compared with the current HCM analysis results and outputs of aaSIDRA, which can calculate intersection performance lane-by-lane. The results showed individual lane analysis mostly produces similar results with HCM and aaSIDRA. However, lane analysis results for permitted left lanes showed some differences with aaSIRDRA due to the different method of assessing saturation flow rate of the permitted left turn movement. It is expected that the proposed method could complement the weaknesses of current HCM signalized intersection analysis.

• Journal of Korean Society of Transportation
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• v.17 no.1
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• pp.105-112
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• 1999

Image Processing Technique has been used as an efficient method to collect traffic information on the road such as vehicle counts, speed, queues, congestion and incidents. Most of the current methods which have been used to detect vehicles by the image processing are based on point processing, dealing with the local gray level of each pixel in the small window. However, these methods have some drawbacks. Firstly, detection is restricted by image quality. Secondly, they can not deal with occlusion and perspective projection problems, In this research, a new method which possibly deals with occlusion and perspective problems will be proposed. It extracts spatial information such as the position, the relationship of vehicles in 3-dimensional space, as well as vehicle detection in the image. The main algorithm used in this research is based on an extension of the Hough Transform. The Hough Transform which is proposed to estimates parameters of vertices and directed edges analytically on the Hough Space, is a valuable method for the 3-dimensional analysis of static scenes, motion detection and the estimation of viewing parameters.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.65-80
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• 2023

In this study, a classification analysis was performed based on the type of traffic situation. The purpose was to derive the major variable factors that could represent the traffic situation. The TTI(Travel Time Index) was used as a criterion for determining traffic conditions, and analysis was performed using data generally detected by the Vehicle Detecting System(VDS). First, the major factors influencing the traffic situation were selected through factor analysis, and traffic conditions were clustered through a cluster analysis of the major factors. After that, variance analysis for each cluster was performed based on the TTI, and similar clusters were merged to categorize the type of traffic situation. The analysis derived, the maximum queue length and occupancy as major factors that could represent the traffic situation. Through this study, it is expected that efficient management of traffic congestion would be possible by just concentrating on the main variable factors that affect the traffic situation.