• KIISE Transactions on Computing Practices
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• v.22 no.2
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• pp.113-118
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• 2016

The walking speed of handicapped people generally is slower than that of normal people. So it is difficult for them to cross at crosswalks within the allotted time provided by the traffic light. This problem can be solved by expanding the time of the traffic light. However, if the latency of the traffic light is increased without distinguishing the handicapped among all other pedestrians, the efficiency of traffic signal lights will decrease. In this paper, we propose a smart traffic signal connecting mechanism between the previous pedestrian traffic signal and a pedestrian's device (smartphone). This Smart pedestrian traffic light, through this mechanism, minimizes traffic congestion by providing additional walking time only to the handicapped among pedestrians. This crosswalk traffic light recognizes the handicapped using a technique called Internet of things (IOT). In this paper, we extract the data necessary to build an effective smart crosswalk traffic light mechanism through simulation techniques. We have extracted different kinds of traffic signal times with our virtual simulation environment to verify the efficiency of the smart crosswalk pedestrian traffic light system. This approach can validate the effective delay time of the traffic signal time through a comparison based on number of pedestrians.

• ETRI Journal
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• v.42 no.3
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• pp.366-375
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• 2020

The network traffic prediction of a smart substation is key in strengthening its system security protection. To improve the performance of its traffic prediction, in this paper, we propose an improved gravitational search algorithm (IGSA), then introduce the IGSA into a wavelet neural network (WNN), iteratively optimize the initial connection weighting, scalability factor, and shift factor, and establish a smart substation network traffic prediction model based on the IGSA-WNN. A comparative analysis of the experimental results shows that the performance of the IGSA-WNN-based prediction model further improves the convergence velocity and prediction accuracy, and that the proposed model solves the deficiency issues of the original WNN, such as slow convergence velocity and ease of falling into a locally optimal solution; thus, it is a better smart substation network traffic prediction model.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.67-72
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• 2019

The self-driving cars identify appropriate navigation paths and obstacles to arrive at their destinations without human control. The autonomous cars are capable of sensing driving environments to improve driver and pedestrian safety by sharing with neighbor traffic infrastructure. In this paper, we have focused on pedestrian protection and have designed an improved localization algorithm to track mobile users on roads by interacting with smart traffic lights in vehicle environments. We developed smart traffic lights with the RSSI sensor and built the proposed method by improving the Kalman filter algorithm to localize mobile users accurately. We successfully evaluated the proposed algorithm to improve the mobile user localization with deployed five smart traffic lights.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.48-62
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• 2023

Recently, smart intersections have been installed in many intelligent transportation system projects, but few cases use them for traffic signal operations besides traffic volume collection and statistical analysis. In order to respond to chronic traffic congestion, it is necessary to implement efficient signal operations using data collected from smart intersections. Therefore, this study establishes a procedure for operating a real-time traffic signal control algorithm using smart intersection data for efficient traffic signal operations and improving the existing algorithm. Effect analysis confirmed that intersection delays are reduced and the section speed improves when the offset is adjusted.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.336-342
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• 2017

Traffic can be localized by reducing the traffic load on the physical network by causing traffic to be generated at the end of the packet network. By localizing traffic, the IoT-based sensitive data-related security issues can be supported effectively. In addition, it can be applied effectively to the next-generation smart network environment without changing the existing network infrastructure. In this paper, a content priority scheme was applied to smart network-based IoT data. The IoT contents were localized to efficiently pinpoint the flow of traffic on the network to enable smart forwarding. In addition, research was conducted to determine the effective network traffic routes through content localization. Through this study, the network load was reduced. In addition, it is a network structure that can guarantee user quality. In addition, it proved that the IoT service can be accommodated effectively in a smart network-based environment.

• Journal of Korean Society of Transportation
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• v.33 no.5
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• pp.470-477
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• 2015

VMS informs the public of traffic and weather information in real time and functions to facilitate traffic flow. However, as smart phone navigation becomes more popular with the rapid propagation of the smart phone, the efficiency and applicability of VMS are challenged. Accordingly, this study aims to investigate the drivers' preference for the traffic information service between VMS and smart phone navigation by conducting a survey using a stated preference in Busan Metropolitan City in August 2013. This study found that 60% of survey respondents prefer VMS to the smart phone navigation. Further analysis to investigate the preference focused on the smart phone users revealed that female, younger, more educated, and less experienced drivers more rely on the smart phone navigation. Consequently, this study implicates that private and governmental institutes have to take a measure to develop the integrated traffic information system.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.867-878
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• 2021

Recently, there has been active research on smart street lamps that can collect real-time traffic data and provide traffic information by attaching images and radars to road lighting facilities. Smart street light technology can detect, identify, and provide dense information compared to existing technologies. In order to effectively utilize the smart streetlight as a high-resolution information delivery medium, a branch-type operation strategy that is different from the existing centralized operation strategy is required. This study presents dynamic information delivery strategies, release strategies, and their criteria for various purposes in a spatial range, separated by the context before and after the occurrence of smart street lights-based accidents. Through this, it is expected that smart road lighting facilities can be used more effectively.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.4
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• pp.127-134
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• 2013

While there are many services that can check current traffic condition and application program such as bus arrival alarm are developed, since it only provide simple alarm and check level of information, it is still insufficient in many senses. Therefore, the program that try to develop in this study is the system that predict arrival time to destination and inform the bus passengers by applying real time traffic information. The system developed related to this study is still very inadequate. In the system developed in this thesis, when the user input the current bus number and destination using smart-phone, relevant server acquire the bus route information from bus information DB, and analyze real time traffic information based on the information from traffic information DB, and inform customer of expected arrival time to destination. In this thesis, traffic congestion can be eased off and regular operation of public transportation can be improved with reliable destination arrival alarm. Also, it is considered that pattern of bus users can be analyzed by using these information, and analyzing average transport speed and time of public transportation, travel time depending on various situation can give a boost to study related to transportation information and its development.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.3191-3196
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• 2011

Smart home system are being installed in the most new construction of building for the convenience of living life. As smart home systems are becoming more common and their diffusion rates are faster, hacker's attack for the smart home system will be increased. In this paper, Risk level of smart home's to do respond to intrusion that occurred from the wired network and wireless network intrusion cases and attacks can occur in a virtual situation created scenarios to build a database. This is based on the smart home users vulnerable to security to know finding illegal intrusion traffic in real-time and attack prevent was designed the intrusion detection algorithm.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.951-969
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• 2017

In this paper, we newly propose a traffic information service model that collects traffic information sensed by an individual vehicle in real time by using a smart device, and which enables drivers to share traffic information on all roads in real time using an application installed on a smart device. In particular, when the driver requests traffic information for a specific area, the proposed driver-personalized service model provides him/her with traffic information on the driving directions in advance by predicting the driving directions of the vehicle based on the learning of the driving records of each driver. To do this, we propose a traffic information management model to process and manage in real time a large amount of online-generated traffic information and traffic information requests generated by each vehicle. We also propose a road node-based indexing technique to efficiently store and manage location-based traffic information provided by each vehicle. Finally, we propose a driving learning and prediction model based on the hidden Markov model to predict the driving directions of each driver based on the driver's driving records. We analyze the traffic information processing performance of the proposed model and the accuracy of the driving prediction model using traffic information collected from actual driving vehicles for the entire area of Seoul, as well as driving records and experimental data.