• Journal of Digital Convergence
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• v.14 no.11
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• pp.289-295
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• 2016

Subway is a future-oriented means of transportation that can be safely and quickly mass transport many passengers than buses and taxis. Congestion growth due to the increase of the metro users is one of the factors that hinder citizens' rights to comfortably use the subway. Accordingly, congestion prediction in the subway is one of the ways to maximize the use of passenger convenience and comfort. In this paper, we monitor the level of congestion in real time via the existing congestion on the metro using multiple regression analysis and big data processing, as well as their departure station and arrival station information More information about the transfer stations offer a personalized congestion prediction system. The accuracy of the predicted congestion shows about 81% accuracy, which is compared to the real congestion. In this paper, the proposed prediction and recommendation application will be a help to prediction of subway congestion and user convenience.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.236-242
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• 2003

ATM ABR service controls network traffic using feedback information on the network congestion situation in order to guarantee the demanded service qualities and the available cell rates. In this paper we apply the control method using queue length prediction to the formation of feedback information for more efficient ABR traffic control. If backward node receive the longer delayed feedback information on the impending congestion, the switch can be already congested from the uncontrolled arriving traffic and the fluctuation of queue length can be inefficiently high in the continuing time intervals. The feedback control method proposed in this paper predicts the queue length in the switch using the slope of queue length prediction function and queue length changes in time-series. The predicted congestion information is backward to the node. NLMS and neural network are used as the predictive control functions, and they are compared from performance on the queue length prediction. Simulation results show the efficiency of the proposed method compared to the feedback control method without the prediction. Therefore, we conclude that the efficient congestion and stability of the queue length controls are possible using the prediction scheme that can resolve the problems caused from the longer delays of the feedback information.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.3
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• pp.285-291
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• 2014

This study applied regression analysis to evaluate the impact of hourly average congestion calculated by bumper model in the congested area of each passage of each port on the peak time congestion, to suggest the model formula that can predict the peak time congestion. This study conducted regression analysis of hourly average congestion and peak time congestion based on the AIS survey study of 20 ports in Korea. As a result of analysis, it was found that the hourly average congestion has a significant impact on the peak time congestion and the prediction model formula was derived. This formula($C_p=4.457C_a+29.202$) can be used to calculate the peak time congestion based on the predicted hourly average congestion.

• Journal of Navigation and Port Research
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• v.47 no.4
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• pp.212-219
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• 2023

Vessel Traffic Service (VTS) area presents a complex traffic pattern due to ships entering or leaving the port to utilize port facilities, as well as ships passing through the coastal area. To ensure safe and efficient management of maritime traffic, VTS operators continuously monitor and control vessels in real time. However, during periods of high traffic congestion, the workload of VTS operators increases, which can result in delayed or inadequate VTS services. Therefore, it would be beneficial to predict traffic congestion and congested areas to enable more efficient traffic control. Currently, such prediction relies on the experience of VTS operators. In this paper, we defined vessel traffic congestion from the perspective of a VTS operator. We proposed a method to generate traffic networks using historical navigational data and predict traffic congestion and congested areas. Experiments were performed to compare prediction results with real maritime data (Daesan port VTS) and examine whether the proposed method could support VTS operators.

• IE interfaces
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• v.23 no.4
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• pp.349-356
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• 2010

The role of Intelligent Transportation System (ITS) is to efficiently manipulate the traffic flow and reduce the cost in logistics by using the state of the art technologies which combine telecommunication, sensor, and control technology. Especially, the hardware part of ITS is rapidly adapting to the up-to-date techniques in GPS and telematics to provide essential raw data to the controllers. However, the software part of ITS needs more sophisticated techniques to take care of vast amount of on-line data to be analyzed by the controller for their decision makings. In this paper, the authors develop a traffic congestion prediction model based on several different parameters from the sensory data captured in the Vehicle Detection System (VDS). This model uses the neural network technology in analyzing the traffic flow and predicting the traffic congestion in the designated area. This model also validates the results by analyzing the errors between actual traffic data and prediction program.

• The KIPS Transactions:PartC
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• v.9C no.4
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• pp.489-496
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• 2002

This study proposes a new polling algorithm allowing dynamic change in polling period based on line utilization prediction. Polling is the most important function in network monitoring, but excessive polling data causes rather serious congestion conditions of network when network is In congestion. Therefore, existing multiple polling algorithms decided network congestion or load of agent with previously performed polling Round Trip Time or line utilization, chanced polling period, and controlled polling traffic. But, this algorithm is to change the polling period based on the previous polling and does not reflect network conditions in the current time to be polled. A algorithm proposed in this study is to predict whether polling traffic exceeds threshold of line utilization on polling path based on the past data and to change the polling period with the prediction. In this study, utilization of each line configuring network was predicted with AR model and violation of threshold was presented in probability. In addition, suitability was evaluated by applying the proposed dynamic polling algorithm based on line utilization prediction to the actual network, reasonable level of threshold for line utilization and the violation probability of threshold were decided by experiment. Performance of this algorithm was maximized with these processes.

• 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.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.145-150
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• 2023

The subway is a public transportation that many people use every day. Line 2 especially has the most crowded stations during the day. However, the risk of crush accidents is increasing due to high congestion during rush hour and this reduces the safety and comfort of passengers. Subway congestion prediction is helpful to forestall problems caused by high congestion. Therefore, this study proposes machine learning classification models that predict subway congestion during commuting time. To predict congestion in Line 2 based in machine learning, we investigate variables that affect subway congestion through previous research and collect a dataset of subway congestion on Line 2 during rush hour from PUBLIC DATA PORTAL. The proposed model is expected to establish the subway operation plane to make passengers safe and satisfied.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.522-530
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• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.