• Title/Summary/Keyword: Traffic Signal control

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Combined Traffic Signal Control and Traffic Assignment : Algorithms, Implementation and Numerical Results

  • Lee, Chung-Won
    • Proceedings of the KOR-KST Conference
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    • 2000.02a
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    • pp.89-115
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    • 2000
  • Traffic signal setting policies and traffic assignment procedures are mutually dependent. The combined signal control and traffic assignment problem deals with this interaction. With the total travel time minimization objective, gradient based local search methods are implemented. Deterministic user equilibrium is the selected user route choice rule, Webster's delay curve is the link performance function, and green time per cycle ratios are decision variables. Three implemented solution codes resulting in six variations include intersections operating under multiphase operation with overlapping traffic movements. For reference, the iterative approach is also coded and all codes are tested in four example networks at five demand levels. The results show the numerical gradient estimation procedure performs best although the simplified local searches show reducing the large network computational burden. Demand level as well as network size affects the relative performance of the local and iterative approaches. As demand level becomes higher, (1) in the small network, the local search tends to outperform the iterative search and (2) in the large network, vice versa.

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An approach for traffic signal control using RFID sensors

  • Seo, Gang-Do;Cho, Jin-Ho
    • Journal of Sensor Science and Technology
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    • v.17 no.3
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    • pp.236-244
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    • 2008
  • The Korean government is building several futuristic cities, ubiquitous City (u-City), with the latest information technology (IT) infrastructure and "ubiquitous" environment. In the "u-City", Intelligent Transportation System (ITS) will be one of the important services. This study proposed a traffic responsive urban traffic control system applicable in those u-City, using RFID (Radio Frequency Identification) technology to get traffic information. And, we proposed a predictive control model using the real time traffic information achieved from the proposed system. A simulation example is provided to demonstrate the applicability of the proposed system and model.

Guidelines for installing Traffic Signals at Diamond Interchanges (다이아몬드 입체교차에서의 신호등 설치기준)

  • ;Carroll J. Messer
    • Journal of Korean Society of Transportation
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    • v.4 no.2
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    • pp.75-92
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    • 1986
  • The paper contains the data collection methods and procedures employed in the study to evaluate the operational effects of stop sign and signal control at diamond interchanges. An assessment of traffic control alternatives is described in terms of operational effects of queues and travel speed. Guidelines for installing signal control at diamond interchanges are provided in terms of internal volume, left turn proportion within internal volume, and the sum of internal and external volume. The specific traffic volume guidelines were developed based on a combination of these variables affecting operational performance.

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Deep Learning Based Emergency Response Traffic Signal Control System

  • Jeong-In, Park
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.2
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    • pp.121-129
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    • 2023
  • In this paper, we developed a traffic signal control system for emergency situations that can minimize loss of property and life by actively controlling traffic signals in a certain section in response to emergency situations. When the emergency vehicle terminal transmits an emergency signal including identification information and GPS information, the surrounding image is obtained from the camera, and the object is analyzed based on deep learning to output object information having information such as the location, type, and size of the object. After generating information tracking this object and detecting the signal system, the signal system is switched to emergency mode to identify and track the emergency vehicle based on the received GPS information, and to transmit emergency control signals based on the emergency vehicle's traveling route. It is a system that can be transmitted to a signal controller. This system prevents the emergency vehicle from being blocked by an emergency control signal that is applied first according to an emergency signal, thereby minimizing loss of life and property due to traffic obstacles.

Designing Reward Function for Cooperative Traffic Signal Control at Multi-intersection (다중 교차로에서 협동적 신호제어를 위한 보상함수 설계)

  • Bae, Yo-han;Jang, Jin-heon;Song, Moon-hyuk
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.10a
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    • pp.110-113
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    • 2022
  • Nowadays, breaking through the conventional traffic signal control method based on mathematical optimization, artificial intelligence began to be used in the area. In response to this trend, many studies are ongoing to figure out how to utilize AI technology properly for traffic signal optimization. They just simply focus on which method will work well besides lots of machine learning techniques and abandon the reward function engineering. In many cases, the reward function consists of the average delay of the vehicles in the intersection. However, this may lead to AI's misunderstanding about the traffic signal control: what AI regards as a good situation may not be realistic. Even the reward function itself may not meet the service level. Therefore, this study analyzes the problems of previous reward functions and will suggest how to reward function can be enhanced.

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Study on the Effectiveness Analysis of Policies for the Advancement of Traffic Control & Operation Systems (교통운영체계 선진화 방안 효과분석 연구)

  • Lee, Choul-Ki;Yun, Il-Soo;Oh, Young-Tae;Kim, Soo-Hee
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.10 no.2
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    • pp.35-41
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    • 2011
  • The national police agency has executed the project for the advancement of traffic control and operation systems in order to mitigate chronic traffic problems and to meet the global standards regarding the traffic control and operation systems. This study effort was initiated to evaluate the effects of various policies proposed for the advancement of traffic control and operation systems. The evaluation results indicated that the policies proposed for the advancement of traffic control and operation systems showed apparent and positive effects in terms of transportation mobility and safety. However, there have been many difficulties to overcome in order to settle down the policies proposed for the advancement of traffic control and operation systems as smooth as possible. For example, the violation of intersection passing method has been increased after the implementation of the advancement of traffic control and operation systems in majority of evaluation sites. In addition, lacks of awareness as well as improper operations of the advancement of traffic control and operation systems have caused drivers' confusions, which generated more traffic congestion and risks. Currently, the policies for the advancement of traffic control and operation systems focused on the improvements of traffic signal operations, including leading through movements and permissive left-turns. However, synergy effects may be expected if all of the policies including the improvements of traffic signal operations are executed together.

Development of A System Optimum Traffic Control Strategy with Cell Transmission Model (Cell Transmission 이론에 근거한 시스템최적 신호시간산정)

  • 이광훈;신성일
    • Journal of Korean Society of Transportation
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    • v.20 no.5
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    • pp.193-206
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    • 2002
  • A signal optimization model is proposed by applying the Cell-Transmission Model(CTM) as an embedded traffic flow model to estimate a system-optimal signal timing plan in a transportation network composed of signalized intersections. Beyond the existing signal-optimization models, the CTM provides appropriate theoretical and practical backgrounds to simulate oversaturation phenomena such as shockwave, queue length, and spillback. The model is formulated on the Mixed-Integer Programming(MIP) theory. The proposed model implies a system-optimal in a sense that traffic demand and signal system cooperate to minimize the traffic network cost: the demand departing from origins through route choice behavior until arriving at destinations and the signal system by calculating optimal signal timings considering the movement of these demand. The potential of model's practical application is demonstrated through a comparison study of two signal control strategies: optimal and fixed signal controls.

A Study on Network Based Traffic Signal Optimization Using Traffic Prediction Data (교통예측자료 기반 Network 차원의 신호제어 최적화 방안)

  • Han, Jeong-hye;Lee, Seon-Ha;Cheon, Choon-Keun;Oh, Tae-ho;Kim, Eun-Ji
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.14 no.6
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    • pp.77-90
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    • 2015
  • An increasing number of vehicles is causing various traffic problems such as chronic congestion of highways and air pollution. Local governments have been managing traffic by constructing systems such as Intelligent Transport Systems (ITS) and Advanced Traffic Management Systems (ATMS) to relieve such problems, but construction of an infrastructure-based traffic system is insufficient in resolving chronic traffic problems. A more sophisticated system with enhanced operational management capabilities added to the existing facilities is necessary at this point. As traffic patterns of the urban traffic flow is time-specific due to the different vehicle populations throughout the time of the day, a local network-wide signal operation plan that can manage such situation-specific traffic patterns is deemed to be necessary. Therefore, this study is conducted for the purpose of establishment of a plan for contextual signal control management through signal optimization at the network level after setting the Frame Signal in accordance to the traffic patterns gathered from the short-term traffic forecast data as a means to mitigate the problems with existing standardized signal operations.