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Journal articles on the topic 'Area traffic signal control'
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1
Liang, Zi Yi, Xin Nan Qu, and Xiang Hui Zhao. "Exploration Based on ZigBee Traffic Signals Area Control System." Applied Mechanics and Materials 135-136 (October 2011): 535–39. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.535.
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In order to solve the problem of traffic jams, this paper presents a solution about traffic signals area control system based on ZigBee wireless sensor network. The basic idea is to connect the area's traffic lights through ZigBee wireless sensor network according to the traffic flow during a time period where the system located. The command center coordinates and controls the length of the green signal, period and the time of the green signal on all roads through the ZigBee wireless sensor network, so that the motor vehicles which move from corresponding two-way intersections could get through with less red signal. The solution adopts TI's CC2430 RF transceiver chip and analog front-end chip CC2591 and some external circuits to design the hardware circuit of sensor nodes and the central control nodes. Then designs the applications about sensor nodes, central control nodes and management software of control center, which based on the ZigBee protocol stack. Test results show that the performance of the system, such as stability, response speed, could meet the actual demand. It can obviously improve the highway capacity.
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Kondratov, Ivan Vladimirovich. "DQN-BASED TRAFFIC SIGNAL CONTROL SYSTEMS." Chronos 6, no. 7(57) (July 13, 2021): 16–18. http://dx.doi.org/10.52013/2658-7556-57-7-6.
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Real-time adaptive traffic control is an important problem in modern world. Historically, various optimization methods have been used to build adaptive traffic signal control systems. Recently, reinforcement learning has been advanced, and various papers showed efficiency of Deep-Q-Learning (DQN) in solving traffic control problems and providing real-time adaptive control for traffic, decreasing traffic pressure and lowering average travel time for drivers. In this paper we consider the problem of traffic signal control, present the basics of reinforcement learning and review the latest results in this area.
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Xia, Xiaomei, Xiaodan Ma, and Jin Wang. "Control Method for Signalized Intersection with Integrated Waiting Area." Applied Sciences 9, no. 5 (March 7, 2019): 968. http://dx.doi.org/10.3390/app9050968.
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To alleviate traffic congestion in the city, an integrated waiting area is introduced to the signalized intersection in this paper. After the design idea and the typical form of the integrated waiting area is proposed, the control method at the signalized intersection is discussed. The coordination control process of the main and pre-signal at the signalized intersection with the integrated waiting area is analyzed and modeled. To assess the operational performance of the integrated waiting area at intersections, a microscopic traffic simulation software (VISSIM) is utilized to simulate intersections with and without integrated waiting areas. Key issues concerning signal timing plans are then discussed. With comparisons between the operation of intersections with and without integrated waiting areas, the implementation effect is quantified based on the statistical data of the simulation result. The results confirm the potential benefits of the integrated waiting areas at the signalized intersections and show that integrated waiting areas work best in heavy traffic demand.
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Sekiyama, Kosuke, and Yasuhiro Ohashi. "Distributed Route Guidance Systems with Self-Organized Multi-Layered Vector Fields." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 2 (March 20, 2005): 106–13. http://dx.doi.org/10.20965/jaciii.2005.p0106.
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This paper deals with novel distributed route guidance that cooperates with self-organizing control of traffic signal networks. Self-organizing control of traffic signals provides a fully distributed approach to coordinate a number of signals distributed in a wide area based on local information of traffic flows so that split and offset control parameters between traffic signals are adjusted for efficient traffic flow. The self-organizing route guidance systems (SRGS) concept is introduced for efficient route guidance to facilitate offset adjustment of the self-organizing control of signal networks by self-organizing multilayered vector fields. Simulation demonstrates the effectiveness of the proposal under nonstationary traffic conditions.
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Zhang, Xin Jie, and Jing Fei Yu. "A Study on Linear Coordinated Control of Intersection Signal in Youyi Street of Baotou City." Applied Mechanics and Materials 361-363 (August 2013): 2240–43. http://dx.doi.org/10.4028/www.scientific.net/amm.361-363.2240.
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As the changing situations of the traffics, according to demanded traffic speed and the distance between two adjacent intersections, a multi-scheme continuous entrance system is taken. The method determine an appropriate time difference in order that vehicles travel in appropriate speed will continuous meet the green lights cross by cross. Furthermore, studies on linear coordinated intersection signal control is the basis of research of area traffic signal control.
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Daneshfar, Fatemeh, and Javad RavanJamJah. "A New Design of Intelligent Traffic Signal Control." International Journal of Fuzzy System Applications 3, no. 3 (July 2013): 51–67. http://dx.doi.org/10.4018/ijfsa.2013070103.
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Dynamic traffic signal control in Intelligent Transportation System (ITS) recently has received increasing attention. This paper proposed an adaptive and cooperative multi-agentfuzzy system for a decentralized traffic signal control. The proposed model has three levels of control, the current intersection traffic situation, its neighboring intersections recommendations and a knowledge base, which provides the current intersection traffic pattern. The proposed architecture comprises a knowledge base, prediction module and a traffic observer that provide data to real traffic data preparation module, then a decision-making layer takes decision to how long should the intersection green light be extended. Also every intersection flow is predicted in two different ways: 1- through a recursive algorithm. 2- based on a two stage fuzzy clustering algorithm. The proposed solution is tested with traffic control of a large connected junction and the result obtained is promising in comparison to the conventional fixed sequence traffic signal and to the vehicle actuated traffic signal control strategies which are the most applicable strategies in this area. Also to simulate the proposed traffic control solutions, a Netlogo-based traffic simulator has been developed as the agents’ world which simulates the roads, traffic flow and intersections.
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Pranevičius, Henrikas, and Tadas Kraujalis. "KNOWLEDGE BASED TRAFFIC SIGNAL CONTROL MODEL FOR SIGNALIZED INTERSECTION." TRANSPORT 27, no. 3 (September 19, 2012): 263–67. http://dx.doi.org/10.3846/16484142.2012.719545.
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Intelligent transportation systems have received increasing attention in academy and industry. Being able to handle uncertainties and complexity, expert systems are applied in vast areas of real life including intelligent transportation systems. This paper presents a traffic signal control method based on expert knowledge for an isolated signalized intersection. The proposed method has the adaptive signal timing ability to adjust its signal timing in response to changing traffic conditions. Based on the traffic conditions, the system determines to extend or terminate the current green signal group. Using the information from its traffic detectors of isolated intersection, the proposed controller gives optimal signals to adapt the phase lengths to the traffic conditions. A comparative analysis between proposed control algorithm, fuzzy logic (FLC) and fixed-timed (pre-timed) controllers has been made in traffic flows control, with varying traffic volume levels, by using simulation software ‘Arena’. Simulation results show that the proposed traffic signal control method (EKC) has better performance over fuzzy logic and conventional pre-time controllers under light and heavy traffic conditions.
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Wang, Yun Xia, and Dong Bo Liu. "Research on the Method of Setting Waiting Area for Non-motor Vehicle at Signal Control Intersection." E3S Web of Conferences 38 (2018): 03021. http://dx.doi.org/10.1051/e3sconf/20183803021.
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Electric bicycle has become an indispensable important component of the transportation system. The fact is that traffic organization and channelizing design of signal control intersection is not intensive, which cannot adapt to the current traffic demand of non-motor vehicle, such as unclear traffic rules and poor visibility, thus the traffic safety of non-motor vehicle is not optimistic. Therefore, it is necessary to study on traffic organization method based on the demand of non-motor vehicle, which can provide certain theoretical basis for traffic administrative department to make policy and traffic design. This article focuses on the method of setting waiting area for non-motor vehicle at signal control intersection, including the advantages, disadvantages and the applicable conditions.
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Gartner, Nathan H., and Mohammed Al-Malik. "Combined Model for Signal Control and Route Choice in Urban Traffic Networks." Transportation Research Record: Journal of the Transportation Research Board 1554, no. 1 (January 1996): 27–35. http://dx.doi.org/10.1177/0361198196155400104.
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Traffic signals have a significant effect on the choice of routes by motorists in urban areas. They are of primary importance in the development of advanced traffic management strategies that involve dynamic rerouting of traffic flows through signal-controlled street networks. A combined network model that simultaneously accounts for both the route choices made by motorists and the desired signal controls to match these choices is presented. Given origin-destination travel demand information, the model generates signal controls to optimize network performance and calculates the resulting traffic volumes in the network. This optimization model inherently reflects the mutual consistency between traffic flows and signal controls. The model is applicable to both fixed-time and demand-responsive signals. Computational procedures and sample network solutions are presented.
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Zhang, Zhenghua, Jin Qian, Chongxin Fang, Guoshu Liu, and Quan Su. "Coordinated Control of Distributed Traffic Signal Based on Multiagent Cooperative Game." Wireless Communications and Mobile Computing 2021 (June 1, 2021): 1–13. http://dx.doi.org/10.1155/2021/6693636.
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In the adaptive traffic signal control (ATSC), reinforcement learning (RL) is a frontier research hotspot, combined with deep neural networks to further enhance its learning ability. The distributed multiagent RL (MARL) can avoid this kind of problem by observing some areas of each local RL in the complex plane traffic area. However, due to the limited communication capabilities between each agent, the environment becomes partially visible. This paper proposes multiagent reinforcement learning based on cooperative game (CG-MARL) to design the intersection as an agent structure. The method considers not only the communication and coordination between agents but also the game between agents. Each agent observes its own area to learn the RL strategy and value function, then concentrates the Q function from different agents through a hybrid network, and finally forms its own final Q function in the entire large-scale transportation network. The results show that the proposed method is superior to the traditional control method.
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Wei, Hua, Guanjie Zheng, Vikash Gayah, and Zhenhui Li. "Recent Advances in Reinforcement Learning for Traffic Signal Control." ACM SIGKDD Explorations Newsletter 22, no. 2 (January 17, 2021): 12–18. http://dx.doi.org/10.1145/3447556.3447565.
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Traffic signal control is an important and challenging real-world problem that has recently received a large amount of interest from both transportation and computer science communities. In this survey, we focus on investigating the recent advances in using reinforcement learning (RL) techniques to solve the traffic signal control problem. We classify the known approaches based on the RL techniques they use and provide a review of existing models with analysis on their advantages and disadvantages. Moreover, we give an overview of the simulation environments and experimental settings that have been developed to evaluate the traffic signal control methods. Finally, we explore future directions in the area of RLbased traffic signal control methods. We hope this survey could provide insights to researchers dealing with real-world applications in intelligent transportation systems
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Li, Wei, Xin Bi, Yun Xia Cao, and Jin Song Du. "Modeling and Simulation of Urban Arterial Traffic Signal Coordinated Control." Applied Mechanics and Materials 543-547 (March 2014): 1417–22. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1417.
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Traffic congestion is a major concern for many cities throughout the world. Developing a sophisticated traffic monitoring and control system would result in an effective solution to this problem. In order to reduce traffic delay, a novel urban arterial traffic signal coordinated control method is presented. The total delay of downstream and upstream vehicles is considered and the function describing the relationship between vehicles delay and signal offset among intersections is established. Finally, comparing the performance of traffic signal under method proposed in this paper with the traditional isolated traffic signal control method, the microscopic simulation results show that the method proposed in this paper has better performance in the aspect of reducing the vehicles delay. The offset model is tested in a simulation environment consisting of a core area of three intersections. It can be concluded that the proposed method is much more effective in relieving oversaturation in a network than the isolated intersection control strategy.
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Cai, Zhen, Zizhen Deng, Jinglei Li, Jinghan Zhang, and Mangui Liang. "An Intersection Signal Control Mechanism Assisted by Vehicular Ad Hoc Networks." Electronics 8, no. 12 (November 25, 2019): 1402. http://dx.doi.org/10.3390/electronics8121402.
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The urban intersection signal decision-making in traditional control methods are mostly based on the vehicle information within an intersection area. The far vehicles that have not reached the intersection area are not taken into account, which results in incomplete information and even incorrectness in decision-making. This paper presents an intersection signal control mechanism assisted by far vehicle information. Using the aid of real-time information collection for far vehicles through vehicular ad hoc networks (VANETs), we can consider them together and calculate the accumulative waiting time for each intersection traffic flow at a future moment to make the optimal signal decision. Simulation results show that, under three different traffic flow environments—same even traffic flows, same uneven traffic flows, and different traffic flows—the two proposed implementation schemes based on the mechanism (fixed phase and period timing improvement scheme, and dynamic phase and period control scheme) show good performances, in which the average waiting time and the ratio of long-waiting vehicles are both less than the results of the traditional signal timing scheme. Especially, in the second scheme, the waiting time was reduced by an average of 38.6% and the ratio of long-waiting vehicles was reduced by an average of 7.67%.
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Chan, K. W., and S. C. Wong. "Optimisation of Fixed-Time Signal Timings for Area Traffic Control Using Genetic Algorithm." HKIE Transactions 6, no. 1 (January 1999): 1–5. http://dx.doi.org/10.1080/1023697x.1999.10667785.
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Park, Sangmin, Eum Han, Sungho Park, Harim Jeong, and Ilsoo Yun. "Deep Q-network-based traffic signal control models." PLOS ONE 16, no. 9 (September 2, 2021): e0256405. http://dx.doi.org/10.1371/journal.pone.0256405.
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Traffic congestion has become common in urban areas worldwide. To solve this problem, the method of searching a solution using artificial intelligence has recently attracted widespread attention because it can solve complex problems such as traffic signal control. This study developed two traffic signal control models using reinforcement learning and a microscopic simulation-based evaluation for an isolated intersection and two coordinated intersections. To develop these models, a deep Q-network (DQN) was used, which is a promising reinforcement learning algorithm. The performance was evaluated by comparing the developed traffic signal control models in this research with the fixed-time signal optimized by Synchro model, which is a traffic signal optimization model. The evaluation showed that the developed traffic signal control model of the isolated intersection was validated, and the coordination of intersections was superior to that of the fixed-time signal control method.
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Wang, Hao, and Yun Xiang Liu. "Self-Organized Traffic Signal Coordinated Control Based on Interactive and Distributed Subarea." Applied Mechanics and Materials 241-244 (December 2012): 2031–37. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2031.
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In traditional traffic signal coordination control, all intersections are shared a common cycle and have fixed offsets in one sub-area. The range of sub-area is normally fixed, plan switching is difficult, and the reaction to the traffic problem is slow. In this paper, a self-organized dynamic control strategy is proposed. it decomposes the network optimization into overlapped and interactive problems between Basic Coordination Units (BCU). It presents an interaction-forecast model among intersections, each intersection not only considers its own benefit, but also takes downstream intersections into account. Multi-scenarios simulation shows it works well in all saturation degrees, reduces total delay around 8% to 12.3%.
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Richardson, Lucy M., Matthew D. Luker, Christopher M. Day, Mark Taylor, and Darcy M. Bullock. "Outcome Assessment of Peer-to-Peer Adaptive Control Adjacent to a National Park." Transportation Research Record: Journal of the Transportation Research Board 2620, no. 1 (January 2017): 43–53. http://dx.doi.org/10.3141/2620-05.
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In the town of Moab, Utah, a combination of seasonal tourist traffic, heavy truck traffic, and high pedestrian volumes creates a unique traffic management challenge; Moab’s remote location adds additional challenges for real-time traffic monitoring and maintaining of signal timing plans. The Main Street corridor is a strong candidate for an adaptive traffic control system (ATCS). Peer-to-peer (P2P) communication and user-definable control logic were used to develop and implement a cost-effective ATCS called “P2P adaptive control” that used only the existing local controllers and detection. The adaptive control logic adjusts green time along the mainline in response to detector inputs while keeping the side streets at the minimum time needed for pedestrian service. System performance was evaluated by comparing performance measures generated from high-resolution signal controller data before and after implementation of P2P adaptive control. The P2P adaptive control increased the through bandwidth of the corridor and reduced the number of split failures (i.e., the number of phase occurrences with insufficient green). Future work will include adjusting the algorithm to improve service on side streets and expanding P2P adaptive control to additional signals expected to be constructed in the area.
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Qu, Xu, Tangyi Guo, Jin Guo, Yi Lin, and Bin Ran. "A real-time signal control strategy at an isolated pedestrian crossing based on radar data." Advances in Mechanical Engineering 11, no. 3 (March 2019): 168781401982590. http://dx.doi.org/10.1177/1687814019825908.
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Fixed-time traffic signal control strategy in an isolated pedestrian crossing tends to reduce traffic capacity and expose vulnerable road users to more danger. To mitigate the negative impact of previous control strategy, this study proposed an optimal real-time signal timing strategy to protect pedestrian crossing and at the same time minimize the system-wide traffic delay. With the application of a wide-area radar data, the features of vehicles, pedestrians, and the passing time of non-motor vehicles and pedestrian were captured considering conflicts and traffic delay. The support vector machine for regression was utilized to hypothesize traffic delay by training. The discrete values of hypothetical passing time will be tested. The minimum value of delay can be recognized and the corresponding hypothetical passing time will be recommended as the green time for crossing. The performance of the proposed ORSTS outperformed the fixed-time traffic signal control strategy in reducing traffic delay by 22.3%.
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Hansen, Blake G., Peter T. Martin, and H. Joseph Perrin. "SCOOT Real-Time Adaptive Control in a CORSIM Simulation Environment." Transportation Research Record: Journal of the Transportation Research Board 1727, no. 1 (January 2000): 27–30. http://dx.doi.org/10.3141/1727-04.
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The manner in which the adaptive signal control system SCOOT (Split, Cycle, Offset Optimization Technique) has been connected to the CORSIM traffic simulation model is described. To demonstrate the connection, CORSIM simulates the traffic activity of a six-node traffic network under SCOOT’s adaptive traffic signal control. CORSIM’s “virtual detectors” provide the necessary data for SCOOT optimization in real time. In a completed loop, the optimized signal timing is then communicated from SCOOT to CORSIM, which implements the timing and updates the traffic simulation. This means that SCOOT is now functioning in an entirely simulated environment. A comparison of delay and travel time is presented for a six-intersection street network under SCOOT control and under fixed-time area control optimized with TRANSYT-7F. The results show reductions in delay and numbers of stops of 20 to 30 percent. Previously, the measurement of the benefit of adaptive control has been limited to evaluations of systems after implementation. It is shown how SCOOT can now be evaluated under various network traffic conditions in a simulation environment and tested on a specific city network to evaluate the benefits before capital costs are committed.
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NISHIKAWA, Ikuko, Shigeto NAKAZAWA, and Hajime KITA. "Area-wide Control of Traffic Signals by Phase Model." Transactions of the Society of Instrument and Control Engineers 39, no. 2 (2003): 199–208. http://dx.doi.org/10.9746/sicetr1965.39.199.
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Bai, Wei Qing, and Mao Lei Zhang. "Research on the Cooperation Method of Traffic Control and Route Guidance of Local Area Network." Applied Mechanics and Materials 347-350 (August 2013): 559–63. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.559.
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The city traffic control system and traffic guidance system are two important subsystems of Intelligent Transportation Systems, also main means of the city traffic management. This paper studies the method of control and guidance system coordination for a specific local area, usually 2-6 signalized intersections, places a detailed analysis from the signal phase changes, guidance information display and other aspects of information to ensure the practical feasibility of the method. This paper presents a systematic approach to ensure interoperability in a variety of state regional road traffic running smoothly and orderly, reduce the incidence of congestion, to avoid further deterioration of the congestion, improving the efficiency of the vehicle access to and pass through the region.
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Wang, Zhao, Mengjie Wang, and Wenqiang Bao. "Development and Application of Dynamic Timing Optimization Platform for Big Data Intelligent Traffic Signals." E3S Web of Conferences 136 (2019): 01008. http://dx.doi.org/10.1051/e3sconf/201913601008.
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As the number of car ownership increases, road traffic flow continues to increase. At the same time, traffic pressure at intersections is increasing as well. At present, most of the traffic lights in China are fixed cycle control. This timing control algorithm obviously cannot make timely adjustments according to changes in traffic flow. In this case, a large number of transportation resources would be wasted. It is very necessary to establish a dynamic timing system for Big data intelligent traffic signals. In this research, the video recognition method was used to acquire the number of vehicles at the intersection in real time, and the obtained data was processed by the optimization algorithm to make a reasonable dynamic timing of the traffic signals. The test results show that after using the big data intelligent traffic signal dynamic timing optimization control platform, in the experimental area, the overall total delay time was reduced by 23%, and the travel time was reduced by 15%. During the off-peak period, the overall total delay time in the experimental region was reduced by 17% and travel time was reduced by 10%. The big data intelligent traffic signal dynamic timing optimization platform would improve the operational efficiency and traffic supply capacity of the existing transportation infrastructure, and could provide real convenience for citizens.
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Ahn, Sanghyun, and Jonghwa Choi. "Internet of Vehicles and Cost-Effective Traffic Signal Control." Sensors 19, no. 6 (March 13, 2019): 1275. http://dx.doi.org/10.3390/s19061275.
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The Internet of Vehicles (IoV) is attracting many researchers with the emergence of autonomous or smart vehicles. Vehicles on the road are becoming smart objects equipped with lots of sensors and powerful computing and communication capabilities. In the IoV environment, the efficiency of road transportation can be enhanced with the help of cost-effective traffic signal control. Traffic signal controllers control traffic lights based on the number of vehicles waiting for the green light (in short, vehicle queue length). So far, the utilization of video cameras or sensors has been extensively studied as the intelligent means of the vehicle queue length estimation. However, it has the deficiencies like high computing overhead, high installation and maintenance cost, high susceptibility to the surrounding environment, etc. Therefore, in this paper, we propose the vehicular communication-based approach for intelligent traffic signal control in a cost-effective way with low computing overhead and high resilience to environmental obstacles. In the vehicular communication-based approach, traffic signals are efficiently controlled at no extra cost by using the pre-equipped vehicular communication capabilities of IoV. Vehicular communications allow vehicles to send messages to traffic signal controllers (i.e., vehicle-to-infrastructure (V2I) communications) so that they can estimate vehicle queue length based on the collected messages. In our previous work, we have proposed a mechanism that can accomplish the efficiency of vehicular communications without losing the accuracy of traffic signal control. This mechanism gives transmission preference to the vehicles farther away from the traffic signal controller, so that the other vehicles closer to the stop line give up transmissions. In this paper, we propose a new mechanism enhancing the previous mechanism by selecting the vehicles performing V2I communications based on the concept of road sectorization. In the mechanism, only the vehicles within specific areas, called sectors, perform V2I communications to reduce the message transmission overhead. For the performance comparison of our mechanisms, we carry out simulations by using the Veins vehicular network simulation framework and measure the message transmission overhead and the accuracy of the estimated vehicle queue length. Simulation results verify that our vehicular communication-based approach significantly reduces the message transmission overhead without losing the accuracy of the vehicle queue length estimation.
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HOSHINO, Munehisa, Takayuki MORI, Naoki TAKAGI, Kazuhiro IWAI, Yasuhiro YAMASHITA, and Hideo UCHIDA. "STUDY ABOUT FIELD MEASUREMENT OF AIR POLLUTION AND TRAFFIC SIGNAL CONTROL IN INTERSECTION AREA." Journal of Environmental Engineering (Transactions of AIJ) 72, no. 614 (2007): 49–55. http://dx.doi.org/10.3130/aije.72.49_2.
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Luo, Jing, and Qingnian Zhang. "Subdivision of Urban Traffic Area Based on the Combination of Static Zoning and Dynamic Zoning." Discrete Dynamics in Nature and Society 2021 (July 15, 2021): 1–17. http://dx.doi.org/10.1155/2021/9954267.
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In this paper, the traffic area of subzone division in urban road network is studied and a subzone division method based on the combination of static partition and dynamic partition is proposed. The static partition is carried out for the road network when the traffic flow is in a noncongested state, so as to provide the decision-making basis for the traffic green wave signal control strategy. At the same time, aiming at the road network when the traffic flow is congested, the dynamic partition is carried out on the basis of static partition to provide the decision-making basis for the traffic maximum flow signal control strategy. In view of the fact that it is difficult to determine the clustering center point during the initial division, this method proposes to determine the clustering center point according to the value of nodes of betweenness centrality. In order to solve the problem that it is difficult to collect traffic data, a method for estimating traffic flow density is proposed. In order to solve the problem of normalization of different probability distribution among various parameters, Mahalanobis distance is used as the fusion index of subzone division. Model verification shows that the method is feasible and effective.
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Oda, T., Y. Yano, and Y. Mashiyama. "Real Time Traffic Signal Optimization Control Method in Urban Areas." IFAC Proceedings Volumes 27, no. 12 (August 1994): 899–903. http://dx.doi.org/10.1016/s1474-6670(17)47587-2.
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Agrawal, Aditi, and Rajeev Paulus. "Improving traffic and emergency vehicle clearence at congested intersections using fuzzy inference engine." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (August 1, 2021): 3176. http://dx.doi.org/10.11591/ijece.v11i4.pp3176-3185.
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Traffic signals play an important role in controlling and coordinating the traffic movement in cities especially in urban areas. As the traffic is exponentially increasing in cities and the pre-timed traffic light control is insufficient in effective timing of the traffic lights, it leads to poor traffic clearance and ultimately to heavy traffic congestion at intersections. Even the Emergency vehicles like Ambulance and Fire brigade are struck at such intersections and experience a prolonged waiting time. An adaptive and intelligent approach in design of traffic light signals is desirable and this paper contributes in applying fuzzy logic to control traffic signal of single four-way intersection giving priority to the Emergency vehicle clearance. The proposed control system is composed of two parallel controllers to select the appropriate lane for green signal and also to decide the appropriate green light time as per the real time traffic condition. Performance of the proposed system is evaluated by using simulations and comparing with pre-timed control system in changing traffic flow condition. Simulation results show significant improvement over the pre-timed control in terms of traffic clearance and lowering of Emergency vehicle wait time at the intersection especially when traffic intensity is high.
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Lin, Xiaohui, and Jianmin Xu. "Feedforward feedback iterative learning control method for the multilayer boundaries of oversaturated intersections based on the macroscopic fundamental diagram." Archives of Transport 53, no. 1 (April 30, 2020): 67–87. http://dx.doi.org/10.5604/01.3001.0014.1745.
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The feedback control based on the model and method of iterative learning control, which in turn is based on the macroscopic fundamental diagram (MFD), mostly belongs to the classification of single-layer boundary control method. However, the feedback control method has the problem of time delay. Therefore, a feedforward feedback iterative learning control (FFILC) method based on MFD of the multi-layer boundary of single-area oversaturated intersections is proposed. The FFILC method can improve the effectiveness of boundary control and avoid the time-delay problem of feedback control. Firstly, MFD theory is used to determine the MFD of the control area; the congestion zone and the transition zone of the control area are identified; and the two-layer boundary of the control area is determined. Then, the FFILC controllers are established at the two-layer boundary of the control area. When the control area enters into a congestion state, the control ratio of traffic flow in and out of the two-layer boundary is adjusted. The cumulative number of vehicles in the control area continues to approach the optimal cumulative number of vehicles, and it maintains high traffic efficiency with high flow rates. Finally, The actual road network is taken as the experimental area, and the road network simulation platform is built. The controller of the feedforward iterative learning control (FILC) is selected as the comparative controller and used to analyse the iterative effect of FFILC. Improvements in the use of traffic signal control indicators for the control area are analysed after the implementation of the FFILC method. Results show that the FFILC method considerably reduces the number of iterations, and it can effectively improve convergence speed and the use of traffic signal evaluation indicators for the control area.
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NISHIKAWA, IKUKO, TAKESHI IRITANI, KAZUTOSHI SAKAKIBARA, and YASUAKI KUROE. "PHASE DYNAMICS OF COMPLEX-VALUED NEURAL NETWORKS AND ITS APPLICATION TO TRAFFIC SIGNAL CONTROL." International Journal of Neural Systems 15, no. 01n02 (February 2005): 111–20. http://dx.doi.org/10.1142/s0129065705000062.
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Complex-valued Hopfield networks which possess the energy function are analyzed. The dynamics of the network with certain forms of an activation function is decomposable into the dynamics of the amplitude and phase of each neuron. Then the phase dynamics is described as a coupled system of phase oscillators with a pair-wise sinusoidal interaction. Therefore its phase synchronization mechanism is useful for the area-wide offset control of the traffic signals. The computer simulations show the effectiveness under the various traffic conditions.
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Ma, Zhengfeng, Darong Huang, Changguang Li, and Jianhua Guo. "Travel Time Reliability-Based Signal Timing Optimization for Urban Road Traffic Network Control." Mathematical Problems in Engineering 2020 (December 1, 2020): 1–11. http://dx.doi.org/10.1155/2020/8898062.
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Due to increasing traffic demand, many metropolitan areas are experiencing extensive traffic congestion, which demands for efficient traffic signal timing and optimization. However, conventional efficiency measure-based signal optimization cannot handle the ubiquitous uncertainty in the road networks, demanding for the incorporation of reliability measures into signal optimization, which is still in its early stage. Therefore, targeting this issue, based on the recent studies on recognizing travel time reliability (TRR) as an important reliability measure of road networks, a travel time reliability-based urban road traffic network signal timing optimization model is proposed in this paper, with the objective function to optimize a TTR measure, i.e., buffer time index. The proposed optimization model is solved using the heuristic particle swarm optimization approach. A case study is conducted using microscopic traffic simulation for a road network in the City of Nanjing, China. Results demonstrate that the proposed optimization model can improve travel time reliability of the road traffic network and the efficiency of the road traffic network as well. Future studies are recommended to expand the integration of travel time reliability into traffic signal timing optimization.
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Wang, Haiwei, Huiying Wen, Feng You, Jianmin Xu, and Hailin Kui. "Motor Vehicle Emission Modeling and Software Simulation Computing for Roundabout in Urban City." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/312396.
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In urban road traffic systems, roundabout is considered as one of the core traffic bottlenecks, which are also a core impact of vehicle emission and city environment. In this paper, we proposed a transport control and management method for solving traffic jam and reducing emission in roundabout. The platform of motor vehicle testing system and VSP-based emission model was established firstly. By using the topology chart of the roundabout and microsimulation software, we calculated the instantaneous emission rates of different vehicle and total vehicle emissions. We argued that Integration-Model, combing traffic simulation and vehicle emission, can be performed to calculate the instantaneous emission rates of different vehicle and total vehicle emissions at the roundabout. By contrasting the exhaust emissions result between no signal control and signal control in this area at the rush hour, it draws a conclusion that setting the optimizing signal control can effectively reduce the regional vehicle emission. The proposed approach has been submitted to a simulation and experiment that involved an environmental assessment in Satellite Square, a roundabout in medium city located in China. It has been verified that setting signal control with knowledge engineering and Integration-Model is a practical way for solving the traffic jams and environmental pollution.
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Wang, Li, Lu Wei, Yong Zhong Zhang, and Zheng Xi Li. "Node Importance Assessment of Traffic Complex Network Based on C-Means Clustering." Advanced Materials Research 211-212 (February 2011): 963–67. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.963.
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Hub nodes of urban traffic complex network are very important for region traffic signal control. Traditionally, region traffic signal control system like SCOOT/SCATS use traffic flow, vehicle queue and distance between junctions as reference in sub-control-area selection practice, in which process engineers’ experience should play importance roles. In this paper, node degree, node betweeness and high peak hour traffic flow are selected as indexes for traffic network node importance assessment. Moreover, C-Means clustering is applied to analysis which junction could be act as a hub node for regional traffic control. To test the effectiveness of this method, urban network around Chang’an Street in Beijing including almost fifty nodes, China is used as trail field. Data result shows Changchunjie, FuyoujieNankou and Hepingmen junction have high clustering characteristics when clustering number are 3, 4 and 5. And the clustering center shows very similar prosperities with real hub node in practice. In conclusion, the multi-index and clustering analysis could provide theoretical support for urban traffic complex network hub node assessment.
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Gong, Suning, Rakesh Kumar, and D. Kumutha. "Design of Lighting Intelligent Control System Based on OpenCV Image Processing Technology." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, Supp01 (March 26, 2021): 119–39. http://dx.doi.org/10.1142/s0218488521400079.
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The high growth of vehicular travel in urban areas, in particular, requires a traffic control system that optimizes traffic flow efficiency. Traffic congestion can also occur by large de-lays in Red Light etc. The delay in lighting is difficult to code and does not rely on real traffic density. It follows that traffic controls are simulated and configured to better meet this rising demand. So, in order to avoid the traffic control problem, the Adaptive Intelligent Traffic Light control system (AITLCS) has been proposed based on OpenCV and Image processing technique. The system proposed is designed to ensure smooth and efficient traffic flow for daily life as well as emergency and public transportation safety. Based on the road density instead of the levels set the proposed system provides the timing for the traffic light signal so that a highly loaded side switched on over long periods compared with the other lanes. It can also be used at an intersection with traffic signs, which controls the traffic light signal at the intersection. If timers are smart to predict the exact time, the system is more efficient because it reduces the time spent on unintended green signal significantly. With the help of OpenCV software, this paper aims to have a signal management SMART solution that will be cost-effective at the end. The system consists of a camera facing a lane taking pictures of the route we want to travel and then the density of the pedestrian and vehicle is taken and compared with each image employing image processing. Such images are processed effectively to learn the density of traffic.
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Chiabaut, Nicolas, Michael Küng, Monica Menendez, and Ludovic Leclercq. "Perimeter Control as an Alternative to Dedicated Bus Lanes: A Case Study." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 20 (July 15, 2018): 110–20. http://dx.doi.org/10.1177/0361198118786607.
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Dedicated bus lanes (DBLs) are a common traffic management strategy in cities as they improve the efficiency of the transit system by preventing buses from getting trapped in traffic jams. Nevertheless, DBLs also have certain disadvantages: they consume space, reduce available capacity for general traffic, and can thus lead to even more congested car traffic situations. It is appealing to find more efficient alternatives that maintain a sufficient network supply for general traffic while guaranteeing high commercial speeds for the bus system. This paper investigates whether perimeter control (gating) could be such an alternative to DBL strategies. This solution aims at controlling the traffic conditions of a given area by monitoring vehicle accumulations and adapting traffic signal parameters to reach the targeted conditions. If free-flow states can be maintained within the zone, then DBLs become superfluous. This hypothesis is examined through a simulation case study with an urban arterial acting as the targeted area. A dual-objective control approach was applied to allow for not only the vehicle accumulation inside the area but the queue lengths at its perimeter, thereby addressing one of the main issues associated with gating schemes. Due to the gating strategy, traffic performance in the arterial, measured through vehicle accumulation plus mean speed and density, improved significantly. Moreover, results showed that bus operations reach almost the same efficiency level when DBLs are replaced by perimeter control. Furthermore, the availability of an additional lane for general traffic in the control case significantly increased the arterial capacity for cars.
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Fu, Li Bi, and Kil To Chong. "Development of a Neural Network Based Q Learning Algorithm for Traffic Signal Control." Advanced Engineering Forum 2-3 (December 2011): 91–95. http://dx.doi.org/10.4028/www.scientific.net/aef.2-3.91.
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As one kind of reinforcement learning method, Q learning algorithm has already been proved to achieve many significant results in traffic signal control area. However, when the state of Markov Decision Process is very big or continuous, the computation load and the memory load will become very big and can not be solved then. Therefore, this paper proposed a neural network based Q learning algorithm to solve this problem known as “Curse of Dimensionality”. This new method realized generalization of conventional Q learnig algorithm in huge and continuous state space as neural network is a very effective value function approximator. Experiment has been implemented upon an isolated intersection and simulation results show that the proposed method can improve the traffic efficiency significantly than the conventional Q learning algorithm.
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Lu, Yang Carl, Holly Krambeck, and Liang Tang. "Use of Big Data to Evaluate and Improve Performance of Traffic Signal Systems in Resource-Constrained Countries." Transportation Research Record: Journal of the Transportation Research Board 2620, no. 1 (January 2017): 20–30. http://dx.doi.org/10.3141/2620-03.
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Deployment of an adaptive area traffic control system is expensive; physical sensors require installation, calibration, and regular maintenance. Because of the high level of technical and financial resources required, area traffic control systems found in developing countries often are minimally functioning. In Cebu City, Philippines, for example, the Sydney Coordinated Adaptive Traffic System was installed before 2000, and fewer than 35% of detectors were still functioning as of January 2015. To address this challenge, a study was designed to determine whether taxi company GPS data are sufficient to evaluate and improve traffic signal timing plans in resource-constrained environments. If this work is successful, the number of physical sensors required to support those systems may be reduced and thereby substantially lower the costs of installation and maintenance. Taxi GPS data provided by a regional taxi-hailing app were used to design and implement methodologies for evaluating the performance of traffic signal timing plans and for deriving updated fixed-dynamic plans, which are fixed plans (with periods based on observable congestion patterns rather than only time of day) iterated regularly until optimization is reached. To date, three rounds of iterations have been conducted to ensure the stability of the proposed signal timings. Results of exploratory analysis indicate that the algorithm is capable of generating reasonable green time splits, but cycle length adjustment must be considered in the future.
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Yu, Yi, Hui Gong, and Xianglun Mo. "Analysis of Traffic Flow Equilibrium in Urban Road Network Based on Floating Vehicle Data." E3S Web of Conferences 261 (2021): 03026. http://dx.doi.org/10.1051/e3sconf/202126103026.
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Based on the floating vehicle data, this paper analyzes the equilibrium of urban road network traffic flow. This can guide traffic flow distribution and provide reliable basis for traffic control. This paper starts with the quantitative analysis of the traffic network equilibrium, on the basis of verifying the validity of the floating car data, divides the urban road network into regions, and constructs an analysis model of the traffic flow equilibrium of the urban road network. The urban road traffic distribution model is constructed in accordance with the number of road traffic segments. On this basis, gini coefficient index is introduced to judge the road network flow balance, which is used to analyze the balance of each sub-region. By means of traffic guidance, signal control and other traffic control means, the traffic flow in each sub-area is balanced, and the traffic flow in the whole road network becomes balanced.
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Li, Rui, Changjiang Zheng, and Wenquan Li. "Optimization Model of Transit Signal Priority Control for Intersection and Downstream Bus Stop." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/9487190.
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Transit signal priority has a positive effect on improving traffic condition and level of transit service in the urban area. In this paper, a passenger-based transit signal priority (TSP) optimization model is formulated to optimize intersection signal phasing based on minimizing accessibility-based passenger delay at the intersection and increased waiting-delay at the downstream bus stop simultaneously. Genetic Algorithm is utilized to calculate passenger-based optimization model that is calibrated by evening rush hour actual traffic data (17:30–18:30, October 13th–October 15th, 2015) along Shuiximen Boulevard in Nanjing, China. The performance of the proposed optimization model in decreasing delay and improving system reliability is simulated and evaluated by VISSIM-based simulation platform, and the results illustrate that the proposed optimization model presents promising outcomes in decreasing accessibility-based passenger delay at intersection (average reduction of 12%) and passenger waiting-delay at downstream bus service stop (average reduction of 18%) compared with traditional vehicle-based TSP optimization method in rush hour.
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de Luca, Stefano, Roberta Di Pace, Silvio Memoli, and Luigi Pariota. "Sustainable Traffic Management in an Urban Area: An Integrated Framework for Real-Time Traffic Control and Route Guidance Design." Sustainability 12, no. 2 (January 19, 2020): 726. http://dx.doi.org/10.3390/su12020726.
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This paper focuses on the presentation of an integrated framework based on two advanced strategies, aimed at mitigating the effect of traffic congestion in terms of performance and environmental impact. In particular, the paper investigates the “operational benefits” that can be derived from the combination of traffic control (TC) and route guidance (RG) strategies. The framework is based on two modules and integrates a within-day traffic control method and a day-to-day behavioral route choice model. The former module consists of an enhanced traffic control model that can be applied to design traffic signal decision variables, suitable for real-time optimization. The latter designs the information consistently with predictive user reactions to the information itself. The proposed framework is implemented to a highly congested sub-network in the city center of Naples (Italy) and different scenarios are tested and compared. The “do nothing” scenario (current; DN) and the “modeled compliance” (MC) scenario, in which travelers’ reaction to the information (i.e., compliance) is explicitly represented. In order to evaluate the effectiveness of the proposed strategy and the modeling framework, the following analyses are carried out: (i) Network performance analysis; (ii) system convergence and stability analysis, as well as the compliance evolution over time; (iii) and emissions and fuel consumption impact analysis.
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Odeh, S. M., A. M. Mora, M. N. Moreno, and J. J. Merelo. "A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System." Advances in Fuzzy Systems 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/378156.
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This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC.
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Paul, Ananya, Kiton Ghosh, and Mitra Sulata. "Three Fog Computing Based Variants of Congestion Control in ITS." International Journal of Recent Technology and Engineering 10, no. 1 (May 30, 2021): 333–48. http://dx.doi.org/10.35940/ijrte.a5966.0510121.
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The growth of vehicles and inadequate road capacity in the urban area trigger traffic congestion and raise the frequency of road accident. Therefore the need of drastically reducing traffic congestion is a significant concern. Advancement in the technology like fog computing, Internet of Things (IoT)in Intelligent Transportation Systems (ITS) aid in the more constructive management of traffic congestion. Three IoT basedFog computing oriented models are designed in the present work for mitigating traffic congestion. The first two schemes are vehicledependent as they control traffic congestion depending upon thenumber of vehicles and their direction of movement across the intersections. The third scheme is environment dependent as theagent senses the environment and controls the sequence of green signal at different routes dynamically. The performances of thethree schemes in ITS are analyzed along with the comparison ofstorage, communication and computation overhead. The efficacy of the schemes is studied theoretically and quantitatively. The quantitative performance of the three schemes is compared with five existing schemes. On the basis of the result of thecomparison, it can be concluded that the proposed schemes are capable of alleviating congestion more optimally than existing schemes due to the substantial reduction in vehicle waiting time.
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Kamble, Rupali, and Deepali Shah. "APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HUMAN LIFE." International Journal of Research -GRANTHAALAYAH 6, no. 6 (June 30, 2018): 178–88. http://dx.doi.org/10.29121/granthaalayah.v6.i6.2018.1363.
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Artificial Intelligence (AI) has revolutionized in information technology.AI is a subfield of computer science that includes the creation of intelligent machines and software that work and react like human beings. AI and its Applications gets used in various fields of life of humans as expert system solve the complex problems in various areas as science, engineering, business, medicine, video games and Advertising. But “Do any traffic lights use Artificial Intelligence??”, I thought a lot of this when waiting in a red light.
This paper gives an overview of Artificial Intelligence and its applications used in human life. This will explore the current use of Artificial Intelligence technologies in Network Intrusion for protecting computer and communication networks from intruders, in the medical area-medicine, to improve hospital inpatient care, for medical image classification, in the accounting databases to mitigate the problems of it, in the computer games, and in Advertising. Also, it will show artificial intelligence principle and how they were applying in traffic signal control, how they solve some traffic problem in actual. This paper gives an introduction to a self-learning system based on RBF neural network and how the system can simulate the traffic police’s experience. This paper is focusing on how to evaluate the effect of the control with the changing of the traffic and adjust the signal with the different techniques of Artificial Intelligence.
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Wu, Zhizhou, Li Zhao, and Jian Sun. "A Study of Dynamic Right-Turn Signal Control Strategy at Mixed Traffic Flow Intersections." PROMET - Traffic&Transportation 26, no. 6 (December 22, 2014): 449–58. http://dx.doi.org/10.7307/ptt.v26i6.1357.
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Traffic conflicts among right-turn vehicles (RTVs), non-motorized vehicles (NMVs) and pedestrians were examined for urban signalized intersections with exclusive right-turn lane. This study proposed an approach to dynamically calculate the duration of the prohibited right-turn for vehicles by using a measure called the Degree of Clustered Conflict (DCC). The process of DCC control includes: 1) quantitative calculation of DCC value in the conflict area; 2) establishing the general cost model that combines the delay and conflict indicators; and 3) applying the DCC-control time model to control RTV in real time. Based on these, the paper presented a general approach of detailed dynamic on-line signal control process of RTV. Finally, the RTV control process was programmed based on VISSIM simulation to evaluate the control effectiveness. The results showed that the general cost (weighted summation of delay and conflict) of the RTV control decreases rapidly compared with non-control, fixed control and full control (drop of 58%, 35% and 42% under small flow conditions and 70%, 59% and 17% in the large flow conditions, respectively). The method not only improved the operation efficiency, but also reduced the potential safety risks among traffic participants when vehicles turn right at intersections.
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Gao, Qian, Shuyang Zhang, Guojun Chen, and Yuchuan Du. "Two-Way Cooperative Priority Control of Bus Transit with Stop Capacity Constraint." Sustainability 12, no. 4 (February 14, 2020): 1405. http://dx.doi.org/10.3390/su12041405.
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Signal priority control and speed guidance are effective ways to reduce the delay of buses at intersections. Previous work generally focused on the optimization strategy at the intersection area, without simultaneously considering the influence on adjacent downstream bus stops. This probably leads to the size of the passed bus platoon exceeding the capacity of berths and queuing, which in turn causes additional delay to the overall bus travel time. Focusing on this problem, this paper proposes a two-way cooperative control strategy that constrains the size of the upstream platoon. Besides this, to avoid bus bunching, no more than two buses from the same route can be admitted in the same platoon. Based on these principles, we modeled how to make buses pass without stopping by simultaneously considering the signal control and speed guidance. Finally, the effectiveness was validated by simulation in Verkehr in Städten Simulation (VISSIM, German for “Traffic in cities—simulation”), a microscopic traffic simulator. The results show that compared to the existing methods, which only use signal control, the cooperative strategy reduces the total delay at the intersection and the downstream stop. It alleviates the queuing phenomenon at the downstream bus stop greatly, and the bus arrivals tend to be more uniform, which helps improve the reliability and sustainability of bus services.
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Gu, Tian Hong, and Hui Jian Cao. "Research on Calculating the Parameters of Signal Timing for TSP Based on Enumeration Method." Advanced Materials Research 756-759 (September 2013): 3094–98. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3094.
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It is of importance to calculate the parameters of signal timing for TSP (Transit Signal Priority). However, most studies computing the delay are provided based on formula of triangle area. With communication technology developing precisely calculating the delay time of buses can be achieved. The kernel algorithm of TSP still has room for improvement. In this paper, the algorithmic flow of the most of functions is presented based on Enumeration Method.Meanwhile the study uses the VISSIM simulation model to evaluate the impact of a number of alternative priority strategies on both the prioritized buses and general traffic. The priority logic that is considered in the study provides signal timing parameters within a real-time traffic signal control environment. A case study was conducted to validate the model results. Simulation results shows that this method effectively reduces average delay time of the travelers.
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Quan, Nguyen Van. "A Passive Radar System for Monitoring of Coastal Areas Ship Traffic Using Satellite Illumination Signals." Journal of the Russian Universities. Radioelectronics 23, no. 3 (July 21, 2020): 41–52. http://dx.doi.org/10.32603/1993-8985-2020-23-3-41-52.
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Introduction. Increasing requirements for improving of information systems for ensuring navigation safety in coastal areas of marine waters determine the search of new engineering and scientific solutions. The creation of a passive coherent location systems (PCL), based on existing sources of electromagnetic radiation (in particular, global navigation satellite system (GNSS) signals) as radar illumination of the monitored space is of particular interest. During development and implementation of the systems, there are a number of problems related to the search of highly efficient processing algorithms, to the optimization of structure and functioning modes when the system is a part of a complex multi-position monitoring system in coastal areas. Aim. Rationale of the structure of bistatic PCL system with GNSS illumination signal, analysis of methods for increasing of the level of reflected signals, development of a general signal processing algorithm of the system receiver unit, formation of proposals for the creation of multi-position radar system (MP radar) for coastal areas navigation monitoring. Materials and methods. Mathematical modeling, theory of signals, methods of digital signal processing. Results. The structure of the bistatic PCL with GNSS illumination signal for monitoring in coastal areas of marine waters to ensure navigation safety has been developed. Methods for increasing the power level of satellite signals at the input of the receiving device have been proposed. General signal processing algorithm and the algorithm of CAF calculation in the bistatic PCL system using GPS C/A code satellite signal for sea surface coastal areas monitoring have been developed. Conclusion. The considered bistatic PCL system with GNSS illumination may be applied as a part of MP radar for monitoring in areas of heavy vessel traffic to ensure the safety, for operational control of marine operations in the high seas, for quick analysis of the situation at sea in an emergency.
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Lin, Xiaohui, Jianmin Xu, Peiqun Lin, Chengtao Cao, and Jiahui Liu. "Improved Road-Network-Flow Control Strategy Based on Macroscopic Fundamental Diagrams and Queuing Length in Connected-Vehicle Network." Mathematical Problems in Engineering 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/8784067.
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Connected-vehicles network provides opportunities and conditions for improving traffic signal control, and macroscopic fundamental diagrams (MFD) can control the road network at the macrolevel effectively. This paper integrated proposed real-time access to the number of mobile vehicles and the maximum road queuing length in the Connected-vehicles network. Moreover, when implementing a simple control strategy to limit the boundary flow of a road network based on MFD, we determined whether the maximum queuing length of each boundary section exceeds the road-safety queuing length in real-time calculations and timely adjusted the road-network influx rate to avoid the overflow phenomenon in the boundary section. We established a road-network microtraffic simulation model in VISSIM software taking a district as the experimental area, determined MFD of the region based on the number of mobile vehicles, and weighted traffic volume of the road network. When the road network was tending to saturate, we implemented a simple control strategy and our algorithm limits the boundary flow. Finally, we compared the traffic signal control indicators with three strategies: (1) no control strategy, (2) boundary control, and (3) boundary control with limiting queue strategy. The results show that our proposed algorithm is better than the other two.
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Ballis, Haris, and Loukas Dimitriou. "Evaluation of Reinforcement Learning Traffic Signalling Strategies for Alternative Objectives: Implementation in the Network of Nicosia, Cyprus." Transport and Telecommunication Journal 21, no. 4 (December 1, 2020): 295–302. http://dx.doi.org/10.2478/ttj-2020-0024.
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AbstractSmart Cities promise to their residents, quick journeys in a clean and sustainable environment. Despite, the benefits accrued by the introduction of traffic management solutions (e.g. improved travel times, maximisation of throughput, etc.), these solutions usually fall short on assessing the environmental impact around the implementation areas. However, environmental performance corresponds to a primary goal of contemporary mobility planning and therefore, solutions guaranteeing environmental sustainability are significant. This study presents an advanced Artificial Intelligence-based (AI) signal control framework, able to incorporate environmental considerations into the core of signal optimisation processes. More specifically, a highly flexible Reinforcement Learning (RL) algorithm has been developed towards the identification of efficient but-more importantly-environmentally friendly signal control strategies. The methodology is deployed on a large-scale micro-simulation environment able to realistically represent urban traffic conditions. Alternative signal control strategies are designed, applied, and evaluated against their achieved traffic efficiency and environmental footprint. Based on the results obtained from the application of the methodology on a core part of the road urban network of Nicosia, Cyprus the best strategy achieved a 4.8% increase of the network throughput, 17.7% decrease of the average queue length and a remarkable 34.2% decrease of delay while considerably reduced the CO emissions by 8.1%. The encouraging results showcase ability of RL-based traffic signal controlling to ensure improved air-quality conditions for the residents of dense urban areas.
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Ren, Chuanxiang, Jinbo Wang, Lingqiao Qin, Shen Li, and Yang Cheng. "A Novel Left-Turn Signal Control Method for Improving Intersection Capacity in a Connected Vehicle Environment." Electronics 8, no. 9 (September 19, 2019): 1058. http://dx.doi.org/10.3390/electronics8091058.
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Setting up an exclusive left-turn lane and corresponding signal phase for intersection traffic safety and efficiency will decrease the capacity of the intersection when there are less or no left-turn movements. This is especially true during rush hours because of the ineffective use of left-turn lane space and signal phase duration. With the advantages of vehicle-to-infrastructure (V2I) communication, a novel intersection signal control model is proposed which sets up variable lane direction arrow marking and turns the left-turn lane into a controllable shared lane for left-turn and through movements. The new intersection signal control model and its control strategy are presented and simulated using field data. After comparison with two other intersection control models and control strategies, the new model is validated to improve the intersection capacity in rush hours. Besides, variable lane lines and the corresponding control method are designed and combined with the left-turn waiting area to overcome the shortcomings of the proposed intersection signal control model and control strategy.
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Wang, Dan Ping, and Kun Yuan Hu. "Research on Path Optimization of Urban Traffic Guidance System." Applied Mechanics and Materials 624 (August 2014): 520–23. http://dx.doi.org/10.4028/www.scientific.net/amm.624.520.
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With the rapid development of economics and technology; the number of vehicles has largely increased. In this paper, traffic guidance and traffic control systems were researched as well as the Internet of Things (IOT). The author tried to combine these three parts to send traffic data to road users so as to let them choose the best route to travel. Meanwhile, traffic network optimization has been realized to reduce traffic congestion areas. This paper has optimized regional traffic signal control systems based on IOT, traffic guidance as well as traffic assignment, involved data sources, IOT design patterns, data collection as well as the relationship between guidance obeisance rate and traffic jam. It also involved the definition of ideal traffic shortest routes, planning and designing of traffic control systems. Results and researches could hope to combine with reality in order to reduce traffic congestion.