• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2012년도 춘계학술대회
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• pp.547-549
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• 2012

In this paper, we propose the verification platform for traffic information of TPEG. Verification platform contains the parsing module of TPEG data and the processing module of GPS data. We compared the traffic information of GPS devices with traffic information of TPEG data. As a result, traffic information from TPEG data is distinguished from actual road traffic information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3858-3874
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• 2021

As an essential part of the urban transportation system, precise perception of the traffic flow parameters at the traffic signal intersection ensures traffic safety and fully improves the intersection's capacity. Traditional detection methods of road traffic flow parameter can be divided into the micro and the macro. The microscopic detection methods include geomagnetic induction coil technology, aerial detection technology based on the unmanned aerial vehicles (UAV) and camera video detection technology based on the fixed scene. The macroscopic detection methods include floating car data analysis technology. All the above methods have their advantages and disadvantages. Recently, indoor location methods based on wireless signals have attracted wide attention due to their applicability and low cost. This paper extends the wireless signal indoor location method to the outdoor intersection scene for traffic flow parameter estimation. In this paper, the detection scene is constructed at the intersection based on the received signal strength indication (RSSI) ranging technology extracted from the wireless signal. We extracted the RSSI data from the wireless signals sent to the road side unit (RSU) by the vehicle nodes, calibrated the RSSI ranging model, and finally obtained the traffic flow parameters of the intersection entrance road. We measured the average speed of traffic flow through multiple simulation experiments, the trajectory of traffic flow, and the spatiotemporal map at a single intersection inlet. Finally, we obtained the queue length of the inlet lane at the intersection. The simulation results of the experiment show that the RSSI ranging positioning method based on wireless signals can accurately estimate the traffic flow parameters at the intersection, which also provides a foundation for accurately estimating the traffic flow state in the future era of the Internet of Vehicles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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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 Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.490-492
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• 2021

In order to realize a safety society in traffic accidents, Korea prepared comprehensive government-wide measures in 2017. Efforts are being made to minimize accidents while walking by children and the elderly by lowering the speed limit in urban areas from 60 km to 50 km and limiting the vehicle to 30 km in the case of child protection zones. In this study, after pre-processing each data with the status of vehicle registration and traffic accident spatial data (GIS) by designating a specific area, Danyang-gun, where the rate of child traffic accidents is increasing every year, it is intended to understand the structure of the data and find out the structural pattern of the data analytical studies were conducted.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제17권3호
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• pp.277-287
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• 1992

In this paper, we study the performance eualuations of the FDDI Network, by mathmeticlal analysis and simulation, in which the Integrated Voice/Data transmission system with voice traffic in synchronous mode and data traffic inasynchronous mode.For the mean waiting times of Voice/Data packet, we use two-state of Marcov models for voice traffic with talkspurt and silenci state, and the data traffic would traffic would transmit at the silence state of voice traffic. By the mean wating times, we analyze the relations between synchronous and asynchronous mode. As a result, using Sync/Async mode with voice and data, voice was not under influnece of data traffic. and in the same time,data can be tanaxmitted in a short waiting time, too.

• Journal of KIISE:Computing Practices and Letters
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• 제12권5호
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• pp.300-311
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• 2006

Traffic information is considered as one of the core contents of the terrestrial DMB. This paper proposes and implements the data broadcasting system, which automatically collects data and transmits the contents in order to provide traffic information promptly The proposed data broadcasting system comprises the following three subsystems: 1 he traffic information integration system for collecting and processing data in real-time, the traffic information authoring system for automatically creating and verifying the contents, and the traffic information transmission system for transmitting the created contents. We describe these subsystems in detail about their functionality, components and interoperability. The proposed data broadcasting system provides the HWS type contents as the PAD data of the terrestrial DMB audio broadcast. Finally, we describe our implementation of the data broadcasting system, and suggest further improvements that need to be made.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• 제20권5호
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• pp.100-112
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• 2021

With the advent of big data, traffic prediction has been developed based on historical data analysis methods, but this method deteriorates prediction performance when a traffic incident that has not been observed occurs. This study proposes a method that can compensate for the reduction in traffic prediction accuracy in traffic incidents situations by hybrid approach of machine learning and traffic simulation. The blind spots of the data-driven method are revealed when data patterns that have not been observed in the past are recognized. In this study, we tried to solve the problem by reinforcing historical data using traffic simulation. The proposed method performs machine learning-based traffic prediction and periodically compares the prediction result with real time traffic data to determine whether an incident occurs. When an incident is recognized, prediction is performed using the synthetic traffic data generated through simulation. The method proposed in this study was tested on an actual road section, and as a result of the experiment, it was confirmed that the error in predicting traffic state in incident situations was significantly reduced. The proposed traffic prediction method is expected to become a cornerstone for the advancement of traffic prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2483-2503
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• 2016

Traffic congestion is a severe problem in many modern cities around the world. Real-time and accurate traffic congestion identification can provide the advanced traffic management systems with a reliable basis to take measurements. The most used data sources for traffic congestion are loop detector, GPS data, and video surveillance. Video based traffic monitoring systems have gained much attention due to their enormous advantages, such as low cost, flexibility to redesign the system and providing a rich information source for human understanding. In general, most existing video based systems for monitoring road traffic rely on stationary cameras and multiple vehicle tracking method. However, most commonly used multiple vehicle tracking methods are lack of effective track initiation schemes. Based on the motion of the vehicle usually obeys constant velocity model, a novel vehicle recognition method is proposed. The state of recognized vehicle is sent to the GM-PHD filter as birth target. In this way, we relieve the insensitive of GM-PHD filter for new entering vehicle. Combining with the advanced vehicle detection and data association techniques, this multiple vehicle tracking method is used to identify traffic congestion. It can be implemented in real-time with high accuracy and robustness. The advantages of our proposed method are validated on four real traffic data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권4호
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• pp.269-276
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• 2013

Information and communication technology (ICT) is a promising solution for mitigating road traffic congestion. ICT allows road users and vehicles to be managed based on real-time road status information. In Tanzania, traffic congestion causes losses of TZS 655 billion per year. The main objective of this study was to develop an optimal approach for integrating real-time road information (RRI) to mitigate traffic congestion. Our research survey focused on three cities that are highly affected by traffic congestion, i.e., Arusha, Mwanza, and Dar es Salaam. The results showed that ICT is not yet utilized fully to solve road traffic congestion. Thus, we established a possible approach for Tanzania based on an analysis of road traffic data provided by organizations responsible for road traffic management and road users. Furthermore, we evaluated the available road information management techniques to test their suitability for use in Tanzania. Using the floating car data technique, fuzzy logic was implemented for real-time traffic level detection and decision making. Based on this solution, we propose a RRI system architecture, which considers the effective utilization of readily available communication technology in Tanzania.

• Journal of Multimedia Information System
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• 제5권2호
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• pp.111-114
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• 2018

Traffic accident analysis is important to reduce the occurrence of the accidents. In this paper, we analyze the traffic accident with Apriori algorithm to find out an association rule of traffic accident in Korea. We first design the traffic accident analysis model, and then collect the traffic accidents data. We preprocessed the collected data and derived some new variables and attributes for analyzing. Next, we analyze based on statistical method and Apriori algorithm. The result shows that many large-scale accident has occurred by vans in daytime. Medium-scale accident has occurred more in day than nighttime, and by cars more than vans. Small-scale accident has occurred more in night time than day time, however, the numbers were similar. Also, car-human accident is more occurred than car-car accident in small-scale accident.