• Journal of Advanced Navigation Technology
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• v.21 no.5
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• pp.472-478
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• 2017

In this paper, we deals with optimizing traffic signal timing in grid networks by using a network topology design method. Optimizing traffic signal timing includes minimizing delay time delay between departure and destination by interlocking straight traffic signal in the minimum spanning tree(MST). On the assumption that users of network abide by the paths provided in this paper, this paper shows optimizing traffic signal timing in grid networks. the paths provided in this paper is gathered by using Dijkstra algorithm used in computer networks. The results indicate minimizing delay time of passing through the grid network and interlocking traffic signal in the grid network.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1375-1378
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• 1997

In this paper, we presents the algorithm which is to recognize the traffic sign on the road the traffic signal in a video image for autonomous navigation. First, the rocognition of traffic sign on the road can be detected using boundary point estimation form some scan-lines within the lane deducted. For this algorithm, index matrix method is used to detemine what sign is. Then, the traffic signal recognition is performed by usign the window minified by several scan-lines which position may be expected. For this algoritm, line profile concept is adopted.

• Journal of IKEEE
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• v.23 no.4
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• pp.1381-1386
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• 2019

As traffic congestion in cities becomes more serious, intelligent traffic control is actively being researched. Reinforcement learning is the most actively used algorithm for traffic signal control, and recently Deep reinforcement learning has attracted attention of researchers. Extended versions of deep reinforcement learning have been emerged as deep reinforcement learning algorithm showed high performance in various fields. However, most of the existing traffic signal control were studied in a single intersection environment, and there is a limitation that the method at a single intersection does not consider the traffic conditions of the entire city. In this paper, we propose a cooperative traffic control at multi-intersection environment. The traffic signal control algorithm is based on a combination of extended versions of deep reinforcement learning and we considers traffic conditions of adjacent intersections. In the experiment, we compare the proposed algorithm with the existing deep reinforcement learning algorithm, and further demonstrate the high performance of our model with and without cooperative method.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.2
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• pp.69-80
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• 1999

A genetic algorithm to determine the optimal signal sequence and double cycle pattern is described. The signal sequence and double cycle pattern are used as the input for TRANSYT to find optimal signal timing at each junction in the area traffic networks, In the genetic process, the partially matched crossover and simple crossover operators are used for evolution of signal sequence and double cycle pattern respectively. A special conversion algorithm is devised to convert the signal sequence into the link-stage assignment for TRANSYT. Results from tests using data from an area traffic network in Leicester region R are given.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.671-680
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• 2016

This research suggested a new real-time traffic signal control algorithm using fusion data of the travel time and the occupancy rate. This research applied the travel time data of traffic information system to traffic signal operation, and developed the signal control process using the degree of saturation that was estimated from the travel time data. This algorithm estimates a queue length from the travel time based on a deterministic delay model, and includes the process to change from the queue length to the degree of saturation. In addition, this model can calculate the traffic signal timings using fusion data of the travel time and the occupancy rate based on the saturation degree. The micro simulation analysis was conducted for effectiveness evaluation. We checked that the average delay decreased by up to 27 percent. In addition, we checked that this signal control algorithm could respond to a traffic condition of oversaturation and detector breakdown effectively and usefully. This research has important contribution to apply the traffic information system to traffic signal operation sectors.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.2
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• pp.214-224
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• 2016

This paper proposes an algorithm to effectively detect the traffic lights and recognize the traffic signals using a monocular camera mounted on the front windshield glass of a vehicle in day time. The algorithm consists of three main parts. The first part is to generate the candidates of a traffic light. After conversion of RGB color model into HSI and YCbCr color spaces, the regions considered as a traffic light are detected. For these regions, edge processing is applied to extract the borders of the traffic light. The second part is to divide the candidates into traffic lights and non-traffic lights using Haar-like features and Adaboost algorithm. The third part is to recognize the signals of the traffic light using a template matching. Experimental results show that the proposed algorithm successfully detects the traffic lights and recognizes the traffic signals in real time in a variety of environments.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.66-66
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• 2000

In this parer, we introduce an algorithm to control flows of the traffic in multi-intersections. It is pointed out that the main problem in traffic control is how to resolve the congested situations for the particular time-durations and directions. The heavy load to a certain direction usually leads the intersection to congested situations, and the adjacent intersections are affected. We control and analyze the traffic flow of multi-intersections consisting of five intersections, in which four intersections are linked to the four directions of the central one. The entrance of vehicles of each direction is described using the concept of probability. We compare the performance of the pretimed signal controls to the traffic adaptive signal controller using a Petri Net simulation tool, Exspect.

• The Journal of the Korea Contents Association
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• v.14 no.3
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• pp.451-462
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• 2014

In this paper, Coordinated Semi-Actuated Signal Control algorithm was developed and evaluated. According to the analysis of simulation, the coordinated semi-actuated signal control led to reduced vehicle delay as the difference of traffic volume between major and minor streets was getting bigger. But when there was relatively high traffic volume, or the equivalent amount of traffic volume on major and minor streets, optimized pre-timed signal control was verified to lower delay times compared to coordinated semi-actuated signal control; however, it might increase pedestrian delay. Therefore, the coordinated semi-actuated signal control should be implemented at intersections where traffic volume is relatively low.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.179-187
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• 2009

This study develops a real-time signal control algorithm based on sectional travel times and includes a field test and evaluation. The objective function of the signal control algorithm is the equalization of delay of traffic movements, and the main process is calculating dissolved time of the queue and delay using the sectional travel time and detection time of individual vehicles. Then this algorithm calculates the delay variation and a targeted red time and calculates the length of the cycle and phase. A progression factor from the US HCM was applied as a method to consider the effect of coordinating the delay calculation, and this algorithm uses the average delay and detection time of probe vehicles, which were collected during the accumulated cycle for a stabile signal control. As a result of the field test and evaluation through the application of the traffic signal control algorithm on four consecutive intersections at 400m intervals, reduction of delay and an equalization effect of delay against TOD control were confirmed using the standard deviation of delay by traffic movements. This study was conducted to develop a real-time traffic signal control algorithm based on sectional travel time, using general-purpose traffic information detectors. With the current practice of disseminating ubiquitous technology, the aim of this study was a fundamental change of the traffic signal control method.

• 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.