• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4268-4289
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• 2020

Advanced traffic signal timing method plays very important role in reducing road congestion and air pollution. Reinforcement learning is considered as superior approach to build traffic light timing scheme by many recent studies. It fulfills real adaptive control by the means of taking real-time traffic information as state, and adjusting traffic light scheme as action. However, existing works behave inefficient in complex intersections and they are lack of feasibility because most of them adopt traffic light scheme whose phase sequence is flexible. To address these issues, a novel adaptive traffic signal timing scheme is proposed. It's based on actor-critic reinforcement learning algorithm, and advanced techniques proximal policy optimization and generalized advantage estimation are integrated. In particular, a new kind of reward function and a simplified form of state representation are carefully defined, and they facilitate to improve the learning efficiency and reduce the computational complexity, respectively. Meanwhile, a fixed phase sequence signal scheme is derived, and constraint on the variations of successive phase durations is introduced, which enhances its feasibility and robustness in field applications. The proposed scheme is verified through field-data-based experiments in both medium and high traffic density scenarios. Simulation results exhibit remarkable improvement in traffic performance as well as the learning efficiency comparing with the existing reinforcement learning-based methods such as 3DQN and DDQN.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.2
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• pp.62-68
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• 1998

In this paper, we proposed the design of the intelligent traffic control system by using multiprocessor architecture. The inter-processor communication of the architecture is implemented by sharing the serial communication channel. In comparing the conventional traffic control system using single processor architecture, the proposed system uses multiple processors controlling the sub systems such as the signal lights, traffic measurement unit, auxiliary signal lights and peripherals. The main processor controls the communication among the processors and the communication protocol link to the central control center at remote site. The proposed architecture reduces the load and simplifies the program of each processor and enables the real time processing of the add-on features of intelligent traffic control systems. The architecture is implemented and the common channel inter-processor communications and the real time operation is experimented .experimented .

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.273-279
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• 2022

Owing to the need to establish a cooperative-intelligent transport system (C-ITS) environment in the transportation sector locally and abroad, various research and development efforts such as high-tech road infrastructure, connection technology between road components, and traffic information systems are currently underway. However, the current central control center-oriented information collection and provision service structure and the insufficient road infrastructure limit the realization of the C-ITS, which requires a diversity of traffic information, real-time data, advanced traffic safety management, and transportation convenience services. In this study, a network construction method based on the existing received signal strength indicator (RSSI) selected as a comparison target, and the experimental target and the proposed intelligent edge network compared and analyzed. The result of the analysis showed that the data transmission rate in the intelligent edge network was 97.48%, the data transmission time was 215 ms, and the recovery time of network failure was 49,983 ms.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1017-1021
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• 2013

The traffic signal has been widely used in the transport system with a fixed time interval currently. This kind of setting time was determined based on experience for vehicles to generate a waiting time while allowing pedestrians crossing the street. However, this strict setting causes inefficient problems in terms of economic and safety crossing. In this research, we propose a monitoring algorithm to detect, track and check pedestrian crossing the crosswalk by the patterns of behavior. This monitoring system ensures the safety for pedestrian and keeps the traffic flow in efficient. In this algorithm, pedestrians are detected by using HOG feature which is robust to illumination changes in outdoor environment. According to a complex computation, the parallel process with the GPU as well as CPU is adopted for real-time processing. Therefore, pedestrians are tracked by the relationship of hue channel in image sequence according to the predefined pedestrian zone. Finally, the system checks the pedestrians' crossing on the crosswalk by its HOG based behavior patterns. In experiments, the parallel processing by both GPU and CPU was performed so that the result reaches 16 FPS (Frame Per Second). The accuracy of detection and tracking was 93.7% and 91.2%, respectively.

• ETRI Journal
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• v.44 no.2
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• pp.194-207
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• 2022

In the last decade, substantial progress has been achieved in intelligent traffic control technologies to overcome consistent difficulties of traffic congestion and its adverse effect on smart cities. Edge computing is one such advanced progress facilitating real-time data transmission among vehicles and roadside units to mitigate congestion. An edge computing-based deep reinforcement learning system is demonstrated in this study that appropriately designs a multiobjective reward function for optimizing different objectives. The system seeks to overcome the challenge of evaluating actions with a simple numerical reward. The selection of reward functions has a significant impact on agents' ability to acquire the ideal behavior for managing multiple traffic signals in a large-scale road network. To ascertain effective reward functions, the agent is trained withusing the proximal policy optimization method in several deep neural network models, including the state-of-the-art transformer network. The system is verified using both hypothetical scenarios and real-world traffic maps. The comprehensive simulation outcomes demonstrate the potency of the suggested reward functions.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.181-190
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• 2022

In this paper, we use a LiDAR sensor and an image camera to detect a left-turning waiting vehicle in two ways, unlike the existing image-type or loop-type left-turn detection system, and a left-turn traffic signal corresponding to the waiting length of the left-turning lane. A system that can efficiently assign a system is introduced. For the LiDAR signal transmitted and received by the LiDAR sensor, the left-turn waiting vehicle is detected in real time, and the image by the video camera is analyzed in real time or at regular intervals, thereby reducing unnecessary computational processing and enabling real-time sensitive processing. As a result of performing a performance test for 5 hours every day for one week with an intersection simulation using an actual signal processor, a detection rate of 99.9%, which was improved by 3% to 5% compared to the existing method, was recorded. The advantage is that 99.9% of vehicles waiting to turn left are detected by the LiDAR sensor, and even if an intentional omission of detection occurs, an immediate response is possible through self-correction using the video, so the excessive waiting time of vehicles waiting to turn left is controlled by all lanes in the intersection. was able to guide the flow of traffic smoothly. In addition, when applied to an intersection in the outskirts of which left-turning vehicles are rare, service reliability and efficiency can be improved by reducing unnecessary signal costs.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.1
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• pp.92-100
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• 2008

The existing transportation systems is emerged as a major obstacle to solve the problems such as a traffic jam and the increasing cost for a distribution and a traffic safety. In hun, ITS targeting intellectual vehicles and transportation infrastructure like road and signals is getting more important and the standards of ITS wireless communication is also getting attention. New traffic control strategies are being developed to utilize real-time traffic information collected by detection method using ITS wireless technology. Especially, DSRC system is being expanded wit ETCS and the use of OBU is spreading. These infrastructures will have much influence on ITS industry and a profound study on the method of utilizing a present infrastructure is going on in various fields. The optimum traffic signal control system using quality real-time information through these infrastructure is under development and so is WISDOM. Accordingly, this paper proposes communication protocol utilizing DSRC to collect real-time traffic information in WISDOM.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1593-1598
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• 2012

In this paper, we propose an intelligent signal control method based on fuzzy logic applicable in real time. We design membership functions to model occupied time and the number of vehicles for each lane. A priority for each signal phase is computed by the popular Max-Min fuzzy inference based on control rules and membership degrees of prepared two functions at any given time. A tie breaking scheme is considering weighted sum of the rate of occupied time per number of vehicles in that block and the standard deviation of these blocks. Only a signal phase with the highest priority is opened and all others are closed and the duration of the phase opening is computed proportional to the rate of number of weighting vehicles in that signal per all weighted vehicles. The simulation result shows that the proposed method is more efficient than the static control in all simulation conditions in $2{\times}3$ experimental designs with the number of vehicles in intersection and congestion degrees that have all three levels.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.3
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• pp.59-72
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• 2017

As the part of study which handles the measure to use the individual vehicle information of taxi GPS data on signal controls in order to overcome the limitation of Loop detector-based collecting methods of real-time signal control system, this paper conducted series of evaluations and improvements on link travel time, queue vehicle time estimates and traffic condition decision algorithm from the research introduced in 2016. considering the control group and the other, the link travel time has enhanced the travel time and the length of queue vehicle has enhanced the estimated model taking account of the traffic situation. It is analyzed that the accuracy of the average link travel time and the length of queue vehicle are respectably both approximately 95 % and 85%. The traffic condition decision algorithm reflected the improved travel speed and vehicle length. Smoothing was performed to determine the trend of the traffic situation and reduce the fluctuation of the data, and the algorithms have refined so as to reflect the pass period on overflow judgment criterion.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2908-2914
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• 1998

This paper presents an intelligent signal for controling the traffic lights on traffic junction network with dynamic traffic flow, When a junction is connected to adjacent junctions on four sides. Prior researches have been done on the single traffic junction. However, it is dificult to apply single junction controller to real traffic situation. In this paper, we develop a fuzzy taffic network controller which adjusts the extension time of current green phase by using teh fuzzy input variables such as the number of entering cars at the green light, the number of waiting cars during the red light, and the traffic volume. The proposed method was compared to the existing junction signal control methods on controllers in terms of average delay time of cars and the cost function defined in this paper.