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Journal articles on the topic 'Traffic signals'
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1
Woscholski, Rüdiger, and Peter J. Parker. "Inositol lipid 5-phosphatases-traffic signals and signal traffic." Trends in Biochemical Sciences 22, no. 11 (1997): 427–31. http://dx.doi.org/10.1016/s0968-0004(97)01120-1.
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Behzadi, Saeed. "AN INTELLIGENT LOCATION AND STATE REORGANIZATION OF TRAFFIC SIGNAL." Geodesy and cartography 46, no. 3 (2020): 145–50. http://dx.doi.org/10.3846/gac.2020.10806.
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In all geo-database related to traffic, beside storing roads data, the information associated to traffic signals such as location, types of traffic signals, side street name, and so on are also stored in that database. In reality, the reason of defining traffic signals for road is the situations and conditions which the roads have. So the existence of traffic signals in the network is related to the parameters of the road. In this paper, instead of storing traffic signal data in the database, a novel method is introduced which implemented on the road network. As a result, the spatial and non-spatial information of traffic signals in the network are extracted based on the location and attribute of the road network. The proposed method is implemented on the network; the result of the intelligent method is compared with the traffic signals information which stored in the database. By comparing the locations and states of proposed traffic signals and the real ones, the overall accuracy for recognizing locations of traffic signal is obtained 94% and the overall accuracy for recognizing states of traffic signal is obtained 89%.
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Patil, Vrushal. "Traffic Signal Pattern Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 126–28. http://dx.doi.org/10.22214/ijraset.2023.57249.
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Abstract: Every day we are witnessing a rapid increase in traffic volume on roads. Traffic signals are made to manage the traffic to get less disturbance during the journey and to avoid collisions. Sometimes these traffic signals might become a reason for a delay due to poor time management at signal timings. The old traffic signal patterns are the main cause of this issue and hence this project of new signalling patterns will help in using traffic signals more efficiently. In the traditional pattern at a crossover only one signal can be opened but using our pattern algorithm more than one signal can be opened and traffic could clear more easily. Even concepts of image processing are used to make the system more automated and intelligent.
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Cui, Naizhong. "Optimization Strategies for Traffic Signal and Identification Design." Frontiers in Science and Engineering 5, no. 2 (2025): 92–98. https://doi.org/10.54691/nvmq1d61.
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This article deeply studies how to improve the effectiveness of traffic signal and roadway signage design, pointing out some shortcomings in current design, including the lack of rationality in signal configuration, low recognition, and ineffective coordination with the surrounding road environment. In response to these issues, scientific layout and planning of traffic signals, enhancing the recognizability of signals and signs, improving the compatibility between signals and roads, and promoting the development and application of intelligent traffic signal systems have been proposed. Intended to increase traffic flow continuity, reduce traffic accident rates, and enhance road safety.
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Williams, Ruth. "Ubiquitin traffic signals." Journal of Cell Biology 185, no. 3 (2009): 372. http://dx.doi.org/10.1083/jcb.1853iti2.
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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 (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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Tomar, Ishu, Indu Sreedevi, and Neeta Pandey. "State-of-Art Review of Traffic Light Synchronization for Intelligent Vehicles: Current Status, Challenges, and Emerging Trends." Electronics 11, no. 3 (2022): 465. http://dx.doi.org/10.3390/electronics11030465.
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The effective control and management of traffic at intersections is a challenging issue in the transportation system. Various traffic signal management systems have been developed to improve the real-time traffic flow at junctions, but none of them have resulted in a smooth and continuous traffic flow for dealing with congestion at road intersections. Notwithstanding, the procedure of synchronizing traffic signals at nearby intersections is complicated due to numerous borders. In traditional systems, the direction of movement of vehicles, the variation in automobile traffic over time, accidents, the passing of emergency vehicles, and pedestrian crossings are not considered. Therefore, synchronizing the signals over the specific route cannot be addressed. This article explores the key role of real-time traffic signal control (TSC) technology in managing congestion at road junctions within smart cities. In addition, this article provides an insightful discussion on several traffic light synchronization research papers to highlight the practicability of networking of traffic signals of an area. It examines the benefits of synchronizing the traffic signals on various busy routes for the smooth flow of traffic at intersections.
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Rebezyuk, Leonid M., and Mykhailo Lytvynenko. "Expert information processing system for traffic light system decision-making with adaptive conditional tram priority." Management Information System and Devises, no. 179 (November 27, 2023): 17–24. http://dx.doi.org/10.30837/0135-1710.2023.179.017.
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The expert information processing system was proposed for a traffic signal system at a multimodal intersection with three types of traffic users: public transport (tram), pedestrians, and general traffic. The system considers the dynamics characteristics of the tram after it is detected by the entry detector of the intersection and is based on a set of fuzzy decision-making rules for decision-making on the time parameters adjustment of the traffic signal plan based on the compatibility of traffic direction signals and the importance of traffic requests. For general traffic signals, the solution is to extend or terminate the permissive signal for a certain group of traffic directions controlled by this signal. Decisions for tram signals, depending on the proximity of the planned permissive signal and the importance of the associated request, can affect both the time parameters of the immediate traffic signal plan and change the overall signal sequence and/or their duration. The conditional priority involves taking into account possible conflicts be-tween the decisions from the public transport signals or from public transport and general traffic signals, which are handled by the corresponding production rules proposed in the paper. The traffic light plan is changed iteratively in an acyclic manner, considering the degree of confidence of the decision obtained by a two-level fuzzy inference model based on the Mamdani algorithm, the second level of which is responsible for determining the signal sequence and the first level for their time parameters. The information processing model is implemented and validated using SUMO urban mobility simulation tool for moderate and saturated transport demands for a non-trivial intersection with synthetic traffic demand. The experimental results demonstrate the applicability of the system in conditions of a saturated transport demand, allowing to reduce the negative impact on the general traffic flows without a significant increase in the time losses of tram passengers compared to the adaptive unconditional way of implementing tram traffic light priority.
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DurgaDevi, Mrs S., Mr P. Senthil, and T. Keerthana. "Controlling Traffic Signals Through GPS for Ambulance." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-Active Galaxy (2018): 32–37. http://dx.doi.org/10.31142/ijtsrd14563.
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Savitha, A. C., Kumar K. M. Madhu, T. S. Sangeetha, M. Shashank, L. B. Sinchana, and R. Tejashwini. "Traffic Signal Management and Control System based on Density of Vehicles and Emergency Vehicles." Journal of Scholastic Engineering Science and Management (JSESM), A Peer Reviewed Universities Refereed Multidisciplinary Research Journal 4, no. 5 (2025): 11–18. https://doi.org/10.5281/zenodo.15387842.
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Traffic lights play such an important role in traffic management to control the traffic on the road. The traffic is getting worse especially in the event of emergency cases. During traffic congestion, it is difficult for emergency vehicles to cross the road which involves many junctions. Emergency vehicles, like ambulances, have responsibility to reach patients or those who are met with accidents have to quickly transfer them to hospital. Due to traffic signals, they may be delayed for rescue operations. Traffic signal Control for Emergency Vehicles is designed and developed to detect emergency vehicles, how to manipulate the traffic lights and how to provide a free way to emergency vehicles. Emergency vehicles include Ambulance, Fire Engine, Police vehicles… etc. This system creates a connection between emergency vehicles and traffic signals. This system uses Radio Frequency Identification (RFID) as its key component to implement the control system. The RFID tag assists in confirming the passage of emergency vehicles from the traffic signal. Based on that movement of emergency vehicle near the traffic signal reflects in changing the signal, this helps the emergency vehicle to clear all the junctions without wasting time at the signals. This is continued until the emergency vehicle reaches its destination. All these processes happened without any human intervention. The quantified output that this system is going to provide with the display of the following parameters are all the four ultrasonic sensor readings continuously, the timer count and the lane in which the emergency vehicle is detected. Along with the reading the system is going to change the signals accordingly. The timer readings display in milliseconds and the ultrasonic sensor readings, that is the distance, will be displayed in centimeters.
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Zhao, Ling, Juan Cao, and Bo Mi. "Fractal Characteristics of Mountain Cities' Traffic Flow Based on EMD and Multifractal." Advanced Materials Research 831 (December 2013): 430–34. http://dx.doi.org/10.4028/www.scientific.net/amr.831.430.
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Aim at the characteristics of the mountain cities road traffic network, the short-time data signals in the congestion state of the road network traffic is analyzed. Fractal characteristics of traffic data signal is in research based on the self-similarity of the traffic data signals. The non-stationary property of the traffic flow signal in the congestion state is known through the calculation of the multifractal spectrum of the traffic flow signal based on EMD. The experimental results show the feasibility of the method, which also can provide theoretical support for the traffic flow control of the mountain city road network in the sub-health state.
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Snehal Chaudhary, Et al. "Use of Convolutional Neural Network and SVM Classifiers for Traffic Signals Detection." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 490–93. http://dx.doi.org/10.17762/ijritcc.v11i9.8834.
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Road signals are crucial for preserving a safe and effective flow of traffic. They give directions to cars, warn them of potential dangers, and notify them of the conditions of the road ahead. Road signs make roadways safer for both vehicles and pedestrians by regulating traffic and reducing accidents. Failure to obey traffic signals can be harmful and result in collisions. Drivers must always be conscious of their surroundings and pay attention to traffic signs. If a driver misses a signal, they should proceed with caution and safety to prevent injuring themselves or others, and they should seek assistance to reroute themselves. Through the use of machine learning techniques, this project will create a traffic signal recognition system that will identify the traffic signals that are present on the road and inform the driver if the system determines that the motorist has missed a traffic signal or is thus violating traffic laws.
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Noland, Robert B. "Pedestrian Travel Times and Motor Vehicle Traffic Signals." Transportation Research Record: Journal of the Transportation Research Board 1553, no. 1 (1996): 28–33. http://dx.doi.org/10.1177/0361198196155300104.
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Traffic signals generally have been installed to maximize the flow of motor vehicle traffic by reducing the average travel delay time. Under free-flow conditions, motor vehicle travel delay is very sensitive to the amount of green phase and the total cycle of the traffic signal. Average pedestrian delay at traffic signals is not taken into account. Some simple examples are used to demonstrate that travel time delay costs to pedestrians caused by existing signalization cycles may often result in increased travel time costs to society. In areas with heavy pedestrian traffic (or during peak pedestrian hours), traffic signals should trade off the costs of motor vehicle delay with pedestrian delay. This may suggest that in some areas, motor vehicle traffic should be banned or severely restricted. Narrower street widths that can allow reduced total signal cycle times can also reduce pedestrian delay.
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K. Pujitha, J. Indu, B. Sasi Vardhan, P. Sandeep Kumar, and Mrs. G. Ramadevi. "Traffic Signal Violation Detection System." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2766–71. https://doi.org/10.32628/cseit2511141.
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Traffic signal violations are a major cause of accidents and traffic congestion. This project presents an automated Traffic Signal Violation Detection System using Deep Learning-based Object Detection. The system leverages SSD MobileNet V1, a pre-trained Convolutional Neural Network (CNN), to detect and classify traffic signals in real-time. Using the TensorFlow Object Detection API, the model identifies traffic lights and determines violations based on detected signals. The approach integrates image processing, real-time object detection, and violation recognition, providing an intelligent traffic monitoring solution. The proposed system enhances road safety, reduces human intervention, and supports smart city traffic management systems.
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Prof. C.Y. Patil, Pallavi Mandhare, Dr Jyoti Yadav, Prof Vilas Kharat,. "Control and Coordination of Self-Adaptive Traffic Signal Using Deep Reinforcement Learning." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (2021): 373–79. http://dx.doi.org/10.17762/itii.v9i1.141.
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The most observable obstacle to sustainable mobility is traffic congestions. These congestions cannot effectively be fixed by traditional control of traffic signals. Safe and smooth movement of traffic is ensured by a self-controlled traffic signal. As such, to coordinate the traffic flow it is necessary to implement dynamic traffic signal subsequences. Primarily, Traffic Signal Controllers (TSC) provides sophisticated control and coordination of vehicles. The control and coordination of traffic signal control systems can be effectively achieved by implementing the Deep Reinforcement Learning (DRL) approaches.
 The decision-making capabilities at intersections are improved by having variations of traffic signal timing using an adaptive TSC. Alternatively, the actual traffic demand is nothing but managing the traffic systems. It analyses the incoming number and type of vehicles and gives a real-time response at intersection geometrics and controls the traffic signals accordingly.
 The proposed DRL algorithm observes traffic data and operates optimum management plans for the regulation of the traffic flow. Furthermore, an existing traffic simulator is used to help provide a realistic environment to support the proposed algorithm.
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Arikumar, Kochupillai Selvaraj, Sahaya Beni Prathiba, Shakila Basheer, Rajalakshmi Shenbaga Moorthy, Ankur Dumka, and Mamoon Rashid. "V2X-Based Highly Reliable Warning System for Emergency Vehicles." Applied Sciences 13, no. 3 (2023): 1950. http://dx.doi.org/10.3390/app13031950.
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Vehicle-to-everything (V2X) in networks is a communication technology that allows vehicles to communicate with their surroundings. Traffic congestion and unawareness of the travel of emergency vehicles (EVs) lead to delays in reaching the destination of the EV. In order to overcome this time delay, we propose a jitter-to-highly reliable (J2H) approach of customizing the traffic signals and an alert passer mechanism to alert other vehicles. Once an EV is started, its source, destination and level of emergency will be updated on the network, and based on the traffic density, the fastest route to reach the destination is determined. The V2X system in the J2H approach passes an alert to all the traffic signals on that route. The traffic signals will continuously monitor the position of the vehicle by using the Global Positioning System (GPS). Based on the position of the vehicle, the distance between the vehicle and the traffic signal on that route is periodically updated. Once the vehicle comes within the range of the closest traffic signal, based on constraints such as number of lanes, emergency level, types of roads, traffic density, number of EVs approaching, and time of arrival of the vehicles, the traffic signal will be customized. The V2X then passes the information to all the traffic signals that are available in the route of the EV. The alert passer mechanism warns about the approaching EV to other vehicles on that route. Thus, by adapting the J2H technique, EVs can overcome the time delay to reach the destination. Traffic congestion is overcome by customizing the traffic signals. Path blockage can be cleared by vehicle-to-vehicle (V2V) communication.
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Niittymäki, Jarkko, and Matti Pursula. "Saturation Flows at Signal-Group-Controlled Traffic Signals." Transportation Research Record: Journal of the Transportation Research Board 1572, no. 1 (1997): 24–32. http://dx.doi.org/10.3141/1572-04.
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The main goal of this research was to update the basic saturation flow values of signalized intersections. The secondary goal was to analyze the effects of certain external factors (such as weather, road, and traffic conditions) on saturation flow. The updating is based on extensive field measurements and simulations. Altogether, about 39,000 queues were observed in this study. Field measurements at 30 locations were made according to the method described in the Highway Capacity Manual and simulations were done with the Helsinki University of Technology HUT-SIM simulator, which was calibrated and carefully validated for Finnish road conditions. A summary of calibration parameters is also presented. The new base value for straight-through lanes is 1, 940 vehicles per hour; the previous value was 1, 700 vehicles per hour. In general, the updated saturation flow values of different lane types are 5 to 20 percent larger than the previous base values. The saturation flow models of different lane types are described. The effects of geometric and traffic composition factors, such as percentage of turning vehicles, traffic composition, lane width, and approach grade, were examined and modeled. Effects of weather, road surface, light conditions, and speed level were also analyzed. The drop in saturation flow was about 20 to 30 percent under slippery road and snowy conditions. In rainy conditions, the drop was smaller, about 10 percent. The effect of speed on saturation flow is also described. The most important results of this 2-year project are the saturation flow values for different lane types, knowledge of the effect of external factors (especially during winter), and the large database, which can be used for other purposes. The possibility of using special signal control programs under bad road conditions is discussed. With these kinds of programs, better safety and higher capacity can be achieved.
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Gourav, Desh. "Smart Traffic System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 2638–42. http://dx.doi.org/10.22214/ijraset.2021.37038.
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The objective of this project is to control the traffic signal with help of solar energy. This project has been developed as a model of Traffic light controller. The signals can be controlled through software programs and can be varied depending upon the location. For example some places needs green signal to glow for long time. And some directions need red signals to glow for long time. This can be achieved simply by varying the delay in the software. Solar power is used to provide the power to the solar lights. So this project is very useful to the government to save the power.The solar panel is solar photovoltaic modules use solar cells to convert light from the sun into electricity. Now-a-days, instead of using the power from the supply line for various operations, most of them are going for solar energy source, as it is cheapest.
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Lytvynenko, Mykhailo, and Leonid M. Rebezyuk. "Mathematical model of acyclic fuzzy control for traffic signal system with adaptive unconditional tram priority." Management Information System and Devises, no. 178 (December 23, 2022): 23–32. http://dx.doi.org/10.30837/0135-1710.2022.178.023.
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The problem of considering the dynamics of the tram as it ap-proaches the intersection to provide unconditional traffic signal priority is considered. A mathematical model of fuzzy control of the intersection traffic signals system has been developed. Rules have been proposed for the inference of traffic signal importance values for a tram vehicle and for decision on the adaptation of the traffic signal plan time parameters based on the compatibility of signals and their importance. Mathematical model implemented and validated using SUMO urban mobility simulation tool for moderate and saturated transport demands onartificial intersection.
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Nichat, Mr Onkar, Mr Suraj Kulkarni, Mr Aditya Shripad Mane, Mr Siddhesh Naik, and Mr Shubham Bhandari. "Traffic Signal Scheduling using Machine Learning." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 6 (2023): 112–17. http://dx.doi.org/10.35940/ijrte.f7489.0311623.
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Within the past few years the number of vehicles increased drastically and therefore the traffic of vehicles became a major issue in urban as well as in rural areas. Major traffic is happening in the area where many roads do intersect with each other. Our existing traffic signal is not real-time and it is run according to how it is programmed earlier irrespective of traffic. To avoid traffic, traffic signals should give the priority to the road that has the maximum density of vehicles. By doing this we can pass the maximum number of vehicles in a certain period of time. This type of signal acts according to the real-time situation, and take a decision smartly. Hence this system is also called a smart traffic light system. The purpose of this study is to get the traffic situation on the roads in real-time and acts accordingly. Using a web camera that should be mounted on the signals, we can get real-time footage of the roads and by using image processing methods, we can determine the densities of vehicles on each road. Signals which are programmed priorly or wrong signal scheduling was found to play the greatest role in causing vehicle traffic. This smart traffic signal scheduling system is definitely a better option in comparison with existing traffic signal scheduling as it is taking the decision according to the traffic situations.
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Thompson, Sam R., Christopher M. Monsere, Miguel Figliozzi, Peter Koonce, and Gary Obery. "Bicycle-Specific Traffic Signals." Transportation Research Record: Journal of the Transportation Research Board 2387, no. 1 (2013): 1–9. http://dx.doi.org/10.3141/2387-01.
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Van Epps, Heather L. "T cell traffic signals." Journal of Experimental Medicine 202, no. 4 (2005): 460. http://dx.doi.org/10.1084/jem.2024fta.
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In 1990, Charles Mackay and colleagues combined classical physiology with modern molecular biology to provide the first concrete evidence that naive and memory T cells follow distinct migratory routes out of the bloodstream— a discovery that helped invigorate the field of lymphocyte homing.
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Smith, M. J. "Traffic signals in assignment." Transportation Research Part B: Methodological 19, no. 2 (1985): 155–60. http://dx.doi.org/10.1016/0191-2615(85)90021-9.
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Raj, Kumar Shrivastava, and Yadav Rakesh. "Automated Traffic Signals using Real-Time Traffic Densities." Journal of Information Technology and Sciences 4, no. 3 (2018): 28–36. https://doi.org/10.5281/zenodo.1693994.
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The ever-increasing problem of traffic is taking a toll on the commuters’ daily routine wherein a significant amount of time of the day is consumed in travelling itself. With expanding movement, the suburbanites are required to stop for longer lengths at activity intersections sitting tight for the green signs bringing about loss of time and fuel. To check this issue, this paper proposes a brilliant activity control framework which powerfully changes the signs continuously by breaking down and looking at the movement at different intersections. The proposed methodology utilizes Hadoop to process vast measure of information. The densities at different sides are compared using theK-Means Clustering algorithm. The comparison output is obtained and fed back to the traffic signal devices which respond by changing signals in an optimized manner.
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Roc, A. Vinidha, P. R. Banuprakash, G. Paul Asir Nixon Raj, and L. Prasad. "Smart Traffic Light Systems." International Journal of Emerging Research in Management and Technology 6, no. 7 (2018): 243. http://dx.doi.org/10.23956/ijermt.v6i7.219.
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Traffic signals are the most efficient way of controlling traffic in a busy junction. But, we can see that these signals fail to control the traffic effectively when a particular lane has got more traffic than the other lanes. The idea behind this project is to implement a system which would easily control the traffic and helps for the emergency vehicles to reach at their destination easily and quickly. In our project, a system of cameras are used to regulate traffic. They obtain information in their respective places and coordinate with other cameras in the system to change traffic signals and suggest green signal for that route to avoid maximum traffic. Emergency vehicle can be detected with the help of sound sensors placed in the junction, which coordinates with the microcontroller and makes the particular Lane free.
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Baskar Ravi, Et al. "Method of Assortment Control for Sector Boundary Traffic Signals Using Organic Computing." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (2023): 366–69. http://dx.doi.org/10.17762/ijritcc.v11i4.9841.
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The research focuses on developing anassortment control procedure for traffic signals at sector boundaries using organic computing principles. This study lies at the intersection of urban traffic signal control and artificial intelligence. The proposed procedure comprises various modules, including traffic flow monitoring, self-optimization, self-modification, evolutionary learning, self-assessment, and self-adaptation. The objective is to achieve efficient assortment between traffic signals at sector boundaries, thus preventing congestion and traffic blockages in the intersecting areas.
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Bonthu, Bhulakshmi, A. Vishal Narayanan, and J. Jabanjalin Hilda. "Intelligent Traffic Management by Synchronized Signalling." Asian Journal of Computer Science and Technology 5, no. 1 (2016): 17–20. http://dx.doi.org/10.51983/ajcst-2016.5.1.1763.
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Traffic congestion is one of the major problems faced in our day-to-day life. The objective of this paper is to provide an innovative method to solve traffic congestion. In the present day scenario, there are numerous Traffic signals which delay the time taken to reach a destination. In order to overcome this problem, we need to synchronize the signals. The goal of this project is to develop a system which synchronizes the signals so that congestion is managed in better manner. Here, signals across neighboring junctions are synchronized in cooperative method and congestion will be cleared in accordance to the traffic density as well as direction of traffic flows. This paper also uses vehicle speed patterns to predict the traffic flow between two nearby junctions. The traffic flow patterns across all four directions will be analyzed and the same is mapped with various fuzzy rules. In accordance to such mapping, a relevant fuzzy rule will fire required module in real time traffic signal. Hence, the traffic congestion will be controlled in dynamic as well as adaptive manner. A vehicle should travel at a particular speed from one junction to another and we calculate the time limit for changing the signal based on that speed. The core objective of this paper is to provide a cost-effective way to manage the traffic and making hassle free driving.
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Kamal, Md Abdus Samad, Jun-ichi Imura, Tomohisa Hayakawa, Akira Ohata, and Kazuyuki Aihara. "Network-Wide Optimization of Traffic Signals Using Mixed Integer Programming." Journal of Robotics and Mechatronics 26, no. 5 (2014): 607–15. http://dx.doi.org/10.20965/jrm.2014.p0607.
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<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00260005/09.jpg"" width=""300"" />Network with four intersections</div> In this paper a network-wide traffic signal control scheme in a model predictive control framework using mixed integer programming is presented. A concise model of traffic is proposed to describe a signalized road network considering conservation of traffic. In the model, the traffic of two sections that belong to a traffic signal group of a junction are represented by a single continuous variable. Therefore, the number of variables required to describe traffic in the network becomes half compared with the models that describe section wise traffic flows. The traffic signal at the junction is represented by a binary variable to express a signal state either green or red. The proposed model is transformed into a mixed logical dynamical system to describe the traffic flows in a finite horizon, and traffic signals are optimized using mixed integer linear programming (MILP) for a given performance index. The scheme simultaneously optimizes all traffic signals in a network in the context of model predictive control by successively extending or terminating a green or red signal of each junction. Consequently, traffic signal patterns with the optimal free parameters, i.e., the cycle times, the split times and the offsets, are realized. Use of the proposed concise traffic model significantly reduces the computation time of the scheme without compromising the performance as it is evaluated on a small road network and compared with a previously proposed scheme. </span>
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Zhu, Wen-Xing, and Li-Dong Zhang. "An original traffic flow model with signal effect for energy dissipation." International Journal of Modern Physics C 25, no. 07 (2014): 1450018. http://dx.doi.org/10.1142/s0129183114500181.
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We proposed an original traffic flow model with a consideration of signal effect based on Bando's optimal velocity model. The optimal velocity function was improved more realistically in describing the motion process of vehicles moving on a road with signals. Based on the improved model, we derived the mathematical expression for energy dissipation. Simulations are conducted to verify the energy dissipation laws in traffic flow with signals. Numerical results show that energy dissipation (rate) can be affected not only by traffic density, but also traffic signal control parameters: split and cycle.
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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 (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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Anilkumar, G., Jagmohan, P. Karunakar, V. Rakesh, Shaik Rahimpasha, and Murugaperumal Krishnamoorthy. "Enhancing Road Safety at Traffic Signals Using IoT: A Smart Solution for Preventing Signal Violations and Reducing Accidents." E3S Web of Conferences 616 (2025): 03004. https://doi.org/10.1051/e3sconf/202561603004.
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This work aims to enhance road safety at traffic signals by leveraging IoT technology. Traffic signal violations, such as signal jumping, are a major cause of accidents, leading to injuries and loss of life. Our proposed system focuses on minimizing these risks by introducing a smart IoT-based solution that ensures compliance with traffic rules and promotes safe driving behavior. The system utilizes IoT-enabled actuators and sensors to monitor traffic conditions and enforce signal adherence. When a signal is violated, the system automatically triggers alerts and takes necessary actions to prevent potential collisions. By effectively managing traffic flow and improving the reliability of traffic signals, this project seeks to reduce the likelihood of accidents at intersections. In addition to improving safety, the system also enhances traffic management efficiency, making roads safer for drivers and pedestrians. The integration of IoT technology ensures real-time monitoring and adaptability to dynamic traffic conditions. Overall, this project underscores the importance of modernizing traffic signal systems to protect lives and foster a safer commuting environment.
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Mulung, Bibi Rawiyah, and Andino Maseleno. "Proposed SMART Traffic Control Signal in Brunei Darussalam." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (2015): 277. http://dx.doi.org/10.11591/tijee.v15i2.1540.
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This paper presents proposed SMART (Systematic Monitoring of Arterial Road Traffic Signals) traffic control signal in Brunei Darussalam. Traffic congestion due to stops and delays at traffic light signals has much been complained about in Brunei Darussalam as well as across the world during the recent years. There are primarily two types of traffic signal controls in Brunei Darussalam. The most common one is the fixed or pre-timed signal operation traffic light and the other one is the actuated signal operation traffic light. Although the actuated signal control is more efficient than the fixed or pre-fixed signal control in the sense that it provides fewer stops and delays to traffic on the major arteries, the best option for Brunei Darussalam would be to introduce smart traffic control signal. This type of traffic signal uses artificial intelligence to take the appropriate action by adjusting the times in real time to minimise the delay in the intersection while also coordinating with intersections in the neighbourhood. SMART Signal simultaneously collects event-based high-resolution traffic data from multiple intersections and generates real-time signal performance measures, including arterial travel time, number of stops, queue length, intersection delay, and level of service. In Brunei Darussalam, where we have numerous intersections where several arterial roads are linked to one another, The SMART signal traffic control method should be implemented.
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Rambabu, K., M. Kranthi Kumar, V. Naveen, et al. "The Design of Traffic Signals at Intersection." International Journal of Innovative Research in Engineering and Management 9, no. 6 (2022): 137–39. http://dx.doi.org/10.55524/ijirem.2022.9.6.24.
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The traffic volume at intersections has been arising a problem which leads to accidents, conflicts and congestions. An efficient traffic control at intersections helps in reduction of delays of all vehicles and the probability of crashes. The movement of vehicles efficiency is increased by providing traffic signals, roundabouts, channelized islands at an intersection. Signal timing is most important if we use traffic signals in which green time decides the efficiency of movement of vehicles along with pedestrian walk timing. At an intersection, traffic volume studies are done with the movement and classification of vehicles along with the geometric elements of the road. The video graphic method is used to collect the volume of vehicles for every 15- minutes interval and converted into PCU units. Webster’s and IRC code method are used for signal design. Saturation flow, lost times and passenger car units, width of the road are the significant parameters used to optimize the signal timing in planning, design and control of signalized intersection.
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Nashikkar, Siddharth, Aryaa Hanamar, Nuren Pathan, and Heena Mulla. "Traffic Signal Synchronization and Dynamic Control." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 1000–1001. http://dx.doi.org/10.22214/ijraset.2023.56156.
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Abstract: During the past decade, there has been a significant increase in popu- lation combined with ever-increasing automobile sales, resulting in increased traffic around the world. Although modern city infrastructure is designed to han-dle this increasing traffic, it is very difficult to deal with traffic in unplanned partsof cities or cities that were developed hundreds of years ago where congestion was not taken into account. During our analysis, we found that it is not possible to synchronize all the signals in such parts of cities as the distance between each traffic signal is different, with some intersections having very high traffic and the very next having low traffic. We came to the conclusion that although it is im- possible to synchronize all the traffic signals, we can form clusters of 2-3 inter- sections that can be synchronized and the intersection with the highest amount oftraffic will be dynamic i.e., the lane with the highest traffic will get more time. These traffic signals can then be connected through the internet and linked to maps which can show upcoming traffic signals and their real-time status
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Jaybhaye, Sangita, Vrushabh Patil, Dewanshu Gakhare, Deepak More, and Yash Deshmukh. "Advanced Traffic Management System." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5221–23. http://dx.doi.org/10.22214/ijraset.2024.61458.
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Abstract: Traffic Management is one of the major issues which is arising rapidly because of significant increase in number of vehicles. To address this there is the need of a smart traffic management system which will enable the smooth traffic flow. Today there are traffic signals which work on the timeframe setting and switch signals after the certain fixed time frame. This system works but a problem arises that is if within the timeframe if the lane becomes empty before the time ends then the signal will not immediately switch as it will complete the timeframe and then switch according to its default setting
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T M, Inba Malar, Bharatha Sreeja G, Amala Justus Selvam M, et al. "Intelligent Traffic Control System Using Deep Learning." ECS Transactions 107, no. 1 (2022): 2783–90. http://dx.doi.org/10.1149/10701.2783ecst.
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Traffic congestion and regulating traffic in traffic signals are major issues in cities. Nowadays, in most of the cities, traffic management centers installed numerous cameras all over the roads and traffic signals. Such cameras can be effectively used for the automation of traffic signals. The objective is to develop a real time system that can automatically monitor real time traffic and make the system intelligent using artificial intelligence techniques. Specifically, Deep Convolutional Neural Networks are employed to perform the task. From statistical traffic data, it determines count, type of vehicle, average speed, distance between vehicles, etc. Based on traffic, the algorithm instructs to stop vehicle or queue or move. It can also record a wrong-way driver. Using license plate recognition, security applications such as unauthorized vehicles are identified. If there is violation of traffic rules, they are recorded with registration number. It can detect ambulances and give first preference. The proposed algorithm identifies VIP vehicles and clear traffics in automated ways. Ambulances are given priority to pass the road. The entire system have been developed using a standalone-Graphical User Interface (GUI). We have implemented successfully and the proposed framework performs satisfactorily.
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Ghodke, Yogesh. "Real Time Traffic Management System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 108–12. http://dx.doi.org/10.22214/ijraset.2024.64790.
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The increasing quantity of automobiles on the road presents difficulties for conventional traffic control strategies. With heavy traffic levels, these traditional methods find it difficult to handle. They only work well in light traffic situations; when there is a large difference in traffic volume on one side of the road or when vehicle density increases on one side, they are unable to adjust. Therefore, by adding dynamic signal switching capabilities, we want to modernise the static signal system. The updated system will continuously check and modify the timing of the signals in real time. In this project, we want to use real-time image detection to precisely measure traffic density and figure out the best times to switch signals, especially when traffic is heavy. Putting this strategy into practice should effectively reduce traffic jams and accelerate traffic flow.
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Laroche, C., T. Leroux, C. Giguère, and P. Poirier. "Field Evaluation of Audible Traffic Signals for Blind Pedestrians." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 22 (2000): 730–33. http://dx.doi.org/10.1177/154193120004402261.
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The project aims to single out an audible traffic signal or a pair of audible traffic signals that would enable blind pedestrians to cross intersections safely. This is carried out in three phases: 1) measuring walking alignment and crossing time of blind pedestrian subjects within a simulated corridor, with four different simultaneous traffic signals (cuckoo, peep-peep, 4-note melody and neo-cuckoo) presented in a quiet environment, 2) measuring alignment with audible traffic signals alternating on each end of the simulated corridor, in a quiet environment and 3) measuring alignment at a busy real intersection. This paper reports on the results of the first two phases. The 4-note melody originally proposed by Hall et al. (1996) and modified in a recent study (Laroche et al., 1999) gave better results in terms of localization and perception of safety than any other signals specified in the Canadian standard (Transportation Association of Canada, 1992). Audible traffic signals alternating back and forth on each side of the corridor gave better results than signals presented simultaneously from both sides.
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Nuri, Sura, Sara M. Mahmood, Omar A. Abdulrazzaq, and Ahmed A. Abdullah. "Design and Fabrication of Smart Traffic Signal Using Arduino Card." Iraqi Journal of Industrial Research 9, no. 3 (2022): 23–32. http://dx.doi.org/10.53523/ijoirvol9i3id251.
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Traffic jam is becoming a headache in the big cities all over the world, which causes a significant delay for drivers and passengers. A smart on/off traffic signal optimization based on a motion IR Sensor (Infrared) is now a necessity to overcome this problem. This work is a design and implementation of a smart traffic signal (STS) that controls the time of the traffic signals (Red–Yellow–Green) according to the traffic congestion on the road. The STS is designed to imitate a side road (with a low traffic move) with a highway road (with a high traffic move). A motion IR sensor along with an Arduino PIC were installed to automatically control the traffic signals on/off delay times based on the existence of the vehicles on the side road. When the side road is empty, the highway traffic signal is always green (highway–always–on mode). However, when a vehicle reaches the traffic signal in the side road, the motion IR sensor sends a signal to the Arduino card, so that the highway traffic signal turns red, while the side road traffic signal turns green letting the vehicle to pass the intersection. The system will then automatically set back to the highway–always–on mode. The entire system is designed and simulated using Proteus workbench.
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Kumar, T. Anil. "IoT Based Signal Violated Vehicle Detector." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (2022): 187–97. http://dx.doi.org/10.22214/ijraset.2022.40221.
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Abstract: The number of new vehicles on the road is increasing rapidly, which in turn causes highly congested roads and due to the long awaiting red signals, few vehicle drivers ignore traffic rules by violating traffic signals. This leads to a high number of road accidents and hence it is essential to detect the signal violated vehicles automatically. Traffic violation detection systems using IOT technology can able to send violated vehicle information to the concern traffic police station automatically by which the vehicle can be penalized immediately. The proposed system can be implemented using vehicle ID card installed in the vehicle itself such that if the vehicle moves further during presence of red signal, automatically, vehicle information in the form of vehicle registration number, type of vehicle (weather it is two-wheeler, car, truck, auto, etc.), owner name, vehicle colour, etc, can be forwarded to the concern authorities through concern mobile phones. To prove the concept practically, all vehicles must be equipped with these low-cost wireless ID cards such that the vehicle data can be transmitted continuously. The demo module contains a mini automatic traffic signal post along with simulated road. The system is designed such that when red signal is energized and during this period, if any vehicle crosses the zebra lines, the system receives the vehicle data and transmits the same through Wi-Fi module. If anybody violates the traffic signal, immediately alarm will be energized to alert the nearest traffic police. The main processor must be installed near the traffic signals, which regrets to acquire vehicle information during the presence of yellow and green signals. The demo module contains one toy car which will be equipped with its wireless ID card. Major building blocks: Simulation of single lane road constructed with traffic signal post & zebra crossing, main processor designed with Arduino MCU, wireless ID card designed with 89C2051 controller chip, IR signal decoder (TSOP1738), Wi – Fi module, Alarm, IR LED, 5v Power supply unit, Toy car, etc.
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Altamimi, Husam, István Varga, and Tamás Tettamanti. "Urban Platooning Combined with Dynamic Traffic Lights." Machines 11, no. 9 (2023): 920. http://dx.doi.org/10.3390/machines11090920.
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Platooning is generally known as a control method for driving a group of connected and automated vehicles in motorway context. Nevertheless, platoon control might also work on urban roads. One possible strategy to increase overall road traffic performance and to reduce congestion in urban traffic networks is to combine platooning with traffic signal control at intersections. The traffic flow can be maximized with coordinated scheduling of traffic signals together with platooning activities, resulting in decreased travel times and fuel consumption. This paper investigates several aspects of this combined control, such as the procedures for coordination and communication between platooning vehicles and traffic signals. Efficient algorithms are suggested to optimize platoon formation and dissolution at junctions and to change traffic signal phases depending on platoon arrival and departure times. The proposed solutions have been tested and verified with SUMO, a high-fidelity microscopic traffic simulator.
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Vieira, Manuela, Manuel Augusto Vieira, Gonçalo Galvão, Paula Louro, Mário Véstias, and Pedro Vieira. "Enhancing Urban Intersection Efficiency: Utilizing Visible Light Communication and Learning-Driven Control for Improved Traffic Signal Performance." Vehicles 6, no. 2 (2024): 666–92. http://dx.doi.org/10.3390/vehicles6020031.
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This paper introduces an approach to enhance the efficiency of urban intersections by integrating Visible Light Communication (VLC) into a multi-intersection traffic control system. The main objectives include the reduction in waiting times for vehicles and pedestrians, the improvement of overall traffic safety, and the accommodation of diverse traffic movements during multiple signal phases. The proposed system utilizes VLC to facilitate communication among interconnected vehicles and infrastructure. This is achieved by utilizing streetlights, headlamps, and traffic signals for transmitting information. By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established. A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively. Agents placed at each intersection control traffic lights by incorporating information from VLC-ready cars, including their positions, destinations, and intended routes. The agents devise optimal strategies to improve traffic flow and engage in communication to optimize the collective traffic performance. An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times. The reinforcement learning approach effectively schedules traffic signals, and the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios. The discussion emphasizes the possibility of applying reinforcement learning in everyday traffic scenarios, showcasing the potential for the dynamic identification of control actions and improved traffic management.
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R, Prof Lakshmi. "Literature Survey on Smart Ambulance Traffic Control System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31380.
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This study introduces a Smart Traffic Signal Control System designed to streamline ambulance transit through congested urban areas. Integrating RFID, GPS, and LTE technologies, this system establishes a direct link between ambulances and traffic signals, enabling prioritized passage for emergency vehicles. GPS tracking triggers real- time signal adjustments as ambulances approach intersections, granting immediate green signals through cloud-based communication. RFID confirms passage, while predictive algorithms optimize upcoming signal timings based on ambulance speed and estimated arrival, effectively preempting traffic congestion. Initial trials demonstrate reduced transit times during peak traffic, ensuring prompt arrival at medical facilities. The system's automation minimizes delays without human intervention, highlighting its potential to revolutionize emergency services. However, seamless integration with existing infrastructure, data security, and scalability remains focal points for further development. This Smart Traffic Signal System signifies a crucial step toward optimizing emergency response in challenging urban settings, promising improved healthcare accessibility through efficient ambulance navigation. Key Words: Smart Traffic Signal Control System, RFID, GPS, LTE technologies, ambulance transit, real-time signal adjustments, cloud-based communication, emergency services optimization, automation, data security, scalability.
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Dr., S. Vijaya Kumar, Samdan Basha Patan, Vishnu Vardhan Reddy Mulaka, and Anjaneya Yadav Avula. "Automatic Gear Lock System forMotorcycles at Traffic Signals." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 2 (2024): 4. https://doi.org/10.5281/zenodo.10716088.
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With the ever-increasing traffic congestion in urban areas, efficient and safe management of vehicles at traffic signals has become paramount. Motorcycles, being one of the most popular modes of transportation, pose unique challenges at traffic signals, especially when riders fail to engage neutral or shut off the engine, leading to accidental throttle engagement and safety hazards. This abstract presents an innovative solution :an Automatic Gear Lock System(AGLS)for motorcycles designed to engage the gear lock when the traffic signal turns red, enhancing safety, reducing fuel wastage, and minimizing air pollution. The AGLS comprises a microcontroller, sensors, and an actuator system integrated into the motorcycle's trans mission system. When the motorcycle approaches a traffic signal and the red light is detected by the system's sensors, the microcontroller sends a signal to engage the gear lock mechanism. This mechanism prevents the motorcycle from changing gears and ensures it remains in neutral. The gear lock disengages automatically when the signal turns green or when the rider applies throttle pressure. Moreover, the system incorporates safety features to prevent unintended activation and ensures rider control is prioritized. Keywords:- Micro Controller, Sensors And Actuators, Embedded And Calibration Software, MountingBrackets.
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Labib, S. M., Hossain Mohiuddin, Irfan Mohammad Al Hasib, Shariful Hasnine Sabuj, and Shrabanti Hira. "Integrating Data Mining and Microsimulation Modelling to Reduce Traffic Congestion: A Case Study of Signalized Intersections in Dhaka, Bangladesh." Urban Science 3, no. 2 (2019): 41. http://dx.doi.org/10.3390/urbansci3020041.
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A growing body of research has applied intelligent transportation technologies to reduce traffic congestion at signalized intersections. However, most of these studies have not considered the systematic integration of traffic data collection methods when simulating optimum signal timing. The present study developed a three-part system to create optimized variable signal timing profiles for a congested intersection in Dhaka, regulated by fixed-time traffic signals. Video footage of traffic from the studied intersection was analyzed using a computer vision tool that extracted traffic flow data. The data underwent a further data-mining process, resulting in greater than 90% data accuracy. The final data set was then analyzed by a local traffic expert. Two hybrid scenarios based on the data and the expert’s input were created and simulated at the micro level. The resultant, custom, variable timing profiles for the traffic signals yielded a 40% reduction in vehicle queue length, increases in average travel speed, and a significant overall reduction in traffic congestion.
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Khliefat, Ibrahim, Mohammed Naser, Fadi Alhomaidat, and Shadi Hanandeh. "Improving the Capacity and Level of Service at the Sixth Circle in Jordan by Using Traffic Signals and Roundabout Metering Approach." Open Transportation Journal 15, no. 1 (2021): 69–80. http://dx.doi.org/10.2174/1874447802115010069.
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Background and Objective: This study aims to improve the capacity and level of service at the sixth circle in Jordan by using traffic signals and a roundabout metering approach. Methods: VISSIM software and C++ program were used to improve the capacity and level of service. The site area (Sixth circle) was located near Crown Plaza and the Jordan Gate Towers project, which causes congestion due to heavy traffic flow. Therefore, this paper assesses the traffic in the sixth circle and presents the possible solutions to reduce the daily traffic flow for the users of this circle. Results: Four signals were placed at the roundabout in the second scenario, which was connected to each other with different cycle lengths. 90-second cycle length gives D value of the level of service. The third scenario was placed adaptive signals on the roundabout, which connects traffic signals with ground detectors placed at a certain distance before the signal and based on the queue length of the flow cars that pass the detector. Conclusion: The adaptive signals worked on the opening and closing and were designed according to the language simulation and the adoption of the language C++. The first two signals were opened considering the Swefieh road with the reference of King Faisal for a period of 45 seconds. It was then closed for 45 seconds to open the signal from the next street from the fifth circle with Zahran street.
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Prasad Bollini, Akhila Mood, Sumanth Kumar Chinna Paga, and Sumanth Kumar Chinna Paga. "Comparative study of traffic signal design using Webster and IRC method." International Journal of Science and Research Archive 11, no. 2 (2024): 1824–31. http://dx.doi.org/10.30574/ijsra.2024.11.2.0662.
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In India, the growth of population plays an important role in increasing the congestion at intersections because every person uses a separate vehicle for movement due to this reason we know every day many people die in road accidents due to lack of traffic control system because all intersections are not well signalized. So, it is not possible to control the movement of vehicles by traffic police due to the increase in the number of vehicles at intersections. Therefore, all intersections should be well signalized and traffic signals are specially designed for the emergency vehicles like Ambulance, Fire vehicles because emergency vehicles face delay at intersections when traffic is at red light and it is very dangerous for our society. In this project, Design of traffic signals is done with the help of the Indian Road Congress (IRC) and Webster's method at Gajularamaram service road intersection. Traffic signals are a better option for effective transportation. Traffic signal systems are used to control the flow of vehicles with the help of traffic lights, where Many roads meet together and make a junction. Traffic volume studies are used to lay down mainly how many vehicles are moving on the road at a particular section during a particular time. Traffic signals are the best way to control vehicle movement at intersections without any accidents and conflicts.
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Bullough, John D., Peter R. Boyce, Andrew Bierman, et al. "Response to Simulated Traffic Signals Using Light-Emitting Diode and Incandescent Sources." Transportation Research Record: Journal of the Transportation Research Board 1724, no. 1 (2000): 39–46. http://dx.doi.org/10.3141/1724-06.
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Simulated light-emitting diode (LED) traffic signals of different luminances were evaluated relative to incandescent signals of the same nominal color and at the luminances required by the specifications of the Institute of Transportation Engineers. Measurements were made of the reaction times to onset and the number of missed signals for red, yellow, and green incandescent and LED signals. Measurements also were made of subjects’ ability to correctly identify signal colors and of their subjective brightness and conspicuity ratings. All measurements were made under simulated daytime conditions. There were no significant differences in mean reaction time, percentage of missed signals, color identification, or subjective brightness and conspicuity ratings between simulated incandescent and LED signals of the same nominal color and luminance. Higher luminances were needed for the yellow and green signal colors to ensure that they produced the same reaction time, the same percentage of missed signals, and the same rated brightness and conspicuity as a red signal at a given luminance. Equations fitted to the reaction time data, the missed signals data, and the brightness and conspicuity ratings for the LED signals can be used to make quantitative predictions of the consequences of proposed changes in signal luminance for reaction time, brightness, and conspicuity.
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Bernas, Marcin, Bartłomiej Płaczek, and Jarosław Smyła. "A Neuroevolutionary Approach to Controlling Traffic Signals Based on Data from Sensor Network." Sensors 19, no. 8 (2019): 1776. http://dx.doi.org/10.3390/s19081776.
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The paper introduces an artificial neural network ensemble for decentralized control of traffic signals based on data from sensor network. According to the decentralized approach, traffic signals at each intersection are controlled independently using real-time data obtained from sensor nodes installed along traffic lanes. In the proposed ensemble, a neural network, which reflects design of signalized intersection, is combined with fully connected neural networks to enable evaluation of signal group priorities. Based on the evaluated priorities, control decisions are taken about switching traffic signals. A neuroevolution strategy is used to optimize configuration of the introduced neural network ensemble. The proposed solution was compared against state-of-the-art decentralized traffic control algorithms during extensive simulation experiments. The experiments confirmed that the proposed solution provides better results in terms of reduced vehicle delay, shorter travel time, and increased average velocity of vehicles.
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Skabardonis, Alexander, Robert L. Bertini, and Brian R. Gallagher. "Development and Application of Control Strategies for Signalized Intersections in Coordinated Systems." Transportation Research Record: Journal of the Transportation Research Board 1634, no. 1 (1998): 110–17. http://dx.doi.org/10.3141/1634-14.
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Modern controllers permit traffic signals in coordinated systems to operate as pretimed, semiactuated or fully actuated to improve the traffic performance at the particular intersection or over the total system. Criteria were established for selecting the type of control, and timing strategies were developed for signals in coordinated systems. The proposed strategies were evaluated through simulation on 14 representative real-world arterials and grid networks. Based on the analysis of the simulation results, guidelines were formulated to assist practicing traffic engineers in selecting the most appropriate control strategies at specific intersections in coordinated signal systems. The guidelines were then applied to select the type of signal control at several intersections in the City of Los Angeles. The results from before and after field studies indicate that the recommended control strategies improved traffic performance, and the study guidelines can be used as an operating tool for traffic signal management.