Infrared Sensor based Self–Adaptive Traffic Signal System using Arduino Board

Patel, Dasharath M.; Rohilla, Yogesh

doi:10.1109/cicn49253.2020.9242560

Cited by 9 publications

(8 citation statements)

References 11 publications

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“…The O/P provides the output signal, as sensed via the sensor to the Arduino Uno, while the VCC supplies the power to the sensor. The Arduino GND is connected to the GND of the sensor [21]. A typical IR sensor can be seen in [21].…”

Section: Infrared (Ir) Sensorsmentioning

confidence: 99%

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Development of a Density-Based Traffic Light Signal System

Abubakar,

Shuaibu,

Haruna

et al. 2023

Asec 2023

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Section: Infrared (Ir) Sensorsmentioning

confidence: 99%

“…The Arduino GND is connected to the GND of the sensor [21]. A typical IR sensor can be seen in [21].…”

Section: Infrared (Ir) Sensorsmentioning

confidence: 99%

Development of a Density-Based Traffic Light Signal System

Abubakar,

Shuaibu,

Haruna

et al. 2023

Asec 2023

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show abstract

“…These systems don't consider the flow of traffic when opening and closing the traffic light; instead, they rely mostly on a static, continuous time. To forecast the quantity of automobiles or the state of the traffic jam in each direction, several techniques were created [1]. An introduction to the use of fuzzy logic for traffic light control is provided by Study [2].…”

Section: Introductionmentioning

confidence: 99%

Faults and Solutions for Smart Traffic Lights

Ahmad,

Musa,

Ohiani

et al. 2024

Middle East Res J Engr Technol

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Ambulances, fire departments, and police cars are examples of emergency vehicles (EVs) that greatly benefit communities by saving lives and deterring theft and criminal activity. Automatic control systems are a novel kind of advanced system that function as a driving system using hardware controllers and computers. We require automatic adaptive controllers to manage the traffic light system's open and shut functions since an increase in the number of cars while maintaining the same infrastructure led to a significant traffic issue. In addition to the difficulty emergency vehicles have reaching their location due to traffic congestion caused by heavy cars, traffic problems also include red-light traffic violations, which typically result in accidents on the roadways. Traffic conditions worsen as a result of more cars on the road, making it more difficult to make room for emergency vehicles like ambulances, rescue fire engines, and emergence vehicles. An IoT sensor-based system that detects the arrival of an ambulance at the hotspots using the signals the ambulance sends out can be built to prevent problems waiting for traffic signals in an emergency. The system would then maintain the signals appropriately.

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“…They achieved high results compared with traditional fixed time. Patel and Rohilla (2020) proposed an adaptive control traffic system using Arduino mega and IR sensor. The model has been used to measure only the density of vehicles the road lanes and give green signal based on lane density to reduce traffic congestion.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Logic-Based Smart Traffic Management Systems

Abdou¹,

Farrag²,

Tolba³

2022

Journal of Computer Science

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Traffic congestion is a serious problem in many developing countries like Egypt. In the presence of the traditional traffic light system, it creates health risks, wastes time and fuel, and pollutes the air. Therefore, there is a big need for a smart traffic management system. This study contributes in solving this problem by introducing an artificial intelligence-based smart traffic light system using fuzzy logic to ensure the smooth flow of traffic in cities. Research results indicate that smart traffic light management is very crucial in smart cities to reduce their carbon footprint to save the world and save energy. Other possible application could be implemented with minor system upgrades such as Detection and Management of traffic Congestion, Automatic Billing of Toll Charges, Automatic detection of speed limit Violation, Route planning, Intelligent Internet of Vehicles, and Prevention of Road Accidents. Four traffic light approaches (fixed-time, smart-time, fixedtime-fuzzy logic, and smart-time-fuzzy-logic) are developed with different eight traffic scenarios. The four approaches are applied with two tools, the first tool is a real Marquette which consists of 4 roads with only one or two vehicles. The second is a simulation software which consists of 4 roads with more than 100 vehicles through 10 min. Many sensors such as (IR sensors, rain sensors, and LEDs to control traffic light) are used to collect data and then the fuzzy algorithm is used to ensure the smooth flow of traffic in cities. The traffic data acquired from the vehicles is fed into the proposed model to maximize the duration of the green light based on the road state. According to experimental results, the proposed smart fuzzy technique simulation software reduced the average waiting time of the vehicles from 769 to 289 sec in heavy rain condition. It also reduced the total time for all vehicles from 2490 to 1154 sec.

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Infrared Sensor based Self–Adaptive Traffic Signal System using Arduino Board

Cited by 9 publications

References 11 publications

Development of a Density-Based Traffic Light Signal System

Development of a Density-Based Traffic Light Signal System

Faults and Solutions for Smart Traffic Lights

A Fuzzy Logic-Based Smart Traffic Management Systems

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