Dynamic traffic management system using infrared (IR) and Internet of Things (IoT)

Rani, Lalita; Kumar, Mandeep; Naresh, K. S.; Vignesh, S.V.

doi:10.1109/iconstem.2017.8261308

Cited by 30 publications

(7 citation statements)

References 11 publications

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“…Traffic light control heavily relies on accurate traffic parameters such as the vehicles' position and speed. Conventional techniques measure them using sensors [28]- [30], e.g., infrared sensors and inductive loop detectors. To collect the traffic data and compute policies for plenty of intersections in real-time, high data transmission rate and enough computing resource are required.…”

Section: A Conventional Methodsmentioning

confidence: 99%

Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems

Liu¹,

Zhu²,

Borst³

et al. 2023

Preprint

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Smart traffic lights in intelligent transportation systems (ITSs) are envisioned to greatly increase traffic efficiency and reduce congestion. Deep reinforcement learning (DRL) is a promising approach to adaptively control traffic lights based on the real-time traffic situation in a road network. However, conventional methods may suffer from poor scalability. In this paper, we investigate deep reinforcement learning to control traffic lights, and both theoretical analysis and numerical experiments show that the intelligent behavior "greenwave" (i.e., a vehicle will see a progressive cascade of green lights, and not have to brake at any intersection) emerges naturally a grid road network, which is proved to be the optimal policy in an avenue with multiple cross streets. As a first step, we use two DRL algorithms for the traffic light control problems in two scenarios. In a single road intersection, we verify that the deep Q-network (DQN) algorithm delivers a thresholding policy; and in a grid road network, we adopt the deep deterministic policy gradient (DDPG) algorithm. Secondly, numerical experiments show that the DQN algorithm delivers the optimal control, and the DDPG algorithm with passive observations has the capability to produce on its own a high-level intelligent behavior in a grid road network, namely, the "greenwave" policy emerges. We also verify the "greenwave" patterns in a 5 × 10 grid road network. Thirdly, the "greenwave" patterns demonstrate that DRL algorithms produce favorable solutions since the "greenwave" policy shown in experiment results is proved to be optimal in a specified traffic model (an avenue with multiple cross streets). The delivered policies both in a single road intersection and a grid road network demonstrate the scalability of DRL algorithms.

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Section: A Conventional Methodsmentioning

confidence: 99%

Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems

Liu¹,

Zhu²,

Borst³

et al. 2023

Preprint

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“…For an algorithm to assign time and increase time in case of collision or injuries, signals using a ZigBee sender with a range of 100 meters and Vmax = 40km/hr and Vmin = 20km/hr are considered, and increasing time in case of collision or accidents. However, vehicles slow down when making a turn, which is also essential at intersections, so S. Vignesh, K. S. Naresh, et al [15] built a system of infrared sensors mounted on either side of the road that detects vehicle motion and sends the information to a Raspberry Pi connected to signals. This Raspberry Pi changes the signs with a more extended green time allocation when the number of vehicles detected by the IR sensor is high.…”

Section: Literature Reviewmentioning

confidence: 99%

AI-based YOLO V4 Intelligent Traffic Light Control System

Boppuru,

Kukatlapalli,

Chowdary

et al. 2023

JAMRIS

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With the growing number of city vehicles, traffic management is becoming one of the most persistent challenges. Traffic bottlenecks cause more significant disturbances in our everyday lives and raise stress levels, negatively impacting the environment by increasing carbon emissions. Due to the population increase, megacities are experiencing severe challenges and significant delays in their day-to-day activities related to transportation. An intelligent traffic management system is required to assess traffic density regularly and take appropriate action. Even though separate lanes are available for various vehicle types, the effective result of wait times for the Commuters at the traffic signal point is not reduced. The proposed methodology employs Artificial Intelligence to collect live images from signals to address this issue in the current system. This approach calculates traffic density, utilizing the image processing technique YOLOv4 for effective traffic congestion management. The YOLOv4 algorithm produces better accuracy in the detection of multiple vehicles. Intelligent monitoring technology uses a signal-switching algorithm at signal intersections to coordinate time distribution and alleviate traffic congestion, resulting in lower vehicle waiting times.

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“…Preangkat IoT dapat dikoneksikan dengan perangkat elektronik diantaranya Kipas Angin dan Lampu rumah dengan jalur Relay [18]. Untuk perangkat elektronik yang memiliki sensor infra merah dapat dikoneksikan dengan sensor Infra merah [19]. Pemrograman yang digunakan yaitu Phyton dan PHP.…”

Section: Observeunclassified

Desain Prototype IoT Menggunakan Bot Telegram Berbasis Text Recognition

Sasongko¹,

Sucipto²

2021

RESEARCH

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Menjaga kestabilan energi nasional melalui program penghematan energi harus dilakukan oleh seluruh lapisan masyarakat. Perilaku penghematan energe salah satunya yaitu energy listrik. Penguna listrik rumah tangga terutama pada perumahan cenderung kurang dalam pengelolaan listrik, misalnya dalam alat-alat eletronik rumah tangga seperti lampu. Penghuni perumahan X seringkali membiarkan lamp uterus menyala sepanjang hari. Hal tesebut dapat menyebabkan pemborosan energi listrik. Penelitian ini menekankan penghematan energy listrik dengan IoT (Internet of Thing). Desain prototype yang digunakan membantu pengguna untuk mengelola aliran listrik terhadap barang elekronik terutama lampu dapat bekerja sesuai dengan waktu dan dapat dikontrol secara dinamis dengan bahasa natural menggunakan bantuan Bot Telegram berbasis Text Recognition. Perangkat IoT dihubungkan dengan server Cloud sehingga dapat diakses dimanapun dan memberikan notifikasi melalui pesan chat kepada penghuni perumahan. Desain Asisten perangkat IoT dibuat dengan bahasa natural sehingga pengguna dapat berkomunikasi layaknya dengan manusia. Adanya penelitian ini diharapkan dapat mengurangi tagihan listrik bulanan akibat pemakaian listrik yang tidak perlu.

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Dynamic traffic management system using infrared (IR) and Internet of Things (IoT)

Cited by 30 publications

References 11 publications

Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems

Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems

AI-based YOLO V4 Intelligent Traffic Light Control System

Desain Prototype IoT Menggunakan Bot Telegram Berbasis Text Recognition

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