Smart Traffic Light System Using Machine Learning

Natafgi, Mohamad Belal; Osman, Mohamad; Haidar, Asser Sleiman; Hamandi, Lama

doi:10.1109/imcet.2018.8603041

Cited by 23 publications

(9 citation statements)

References 3 publications

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“…The time-series methods are based on historical data. Natafgi et al (2018) proposed an adaptive reinforcement learning approach. In which the multiple agents are assigned to a crossroad and learn optimal time and distance to travel based on reward and penalty of their actions.…”

Section: Literature Reviewmentioning

confidence: 99%

An amalgamation of YOLOv4 and XGBoost for next-gen smart traffic management system

Dave

Chandarana

Goel

et al. 2021

PeerJ Computer Science

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The traffic congestion and the rise in the number of vehicles have become a grievous issue, and it is focused worldwide. One of the issues with traffic management is that the traffic light’s timer is not dynamic. As a result, one has to remain longer even if there are no or fewer vehicles, on a roadway, causing unnecessary waiting time, fuel consumption and leads to pollution. Prior work on smart traffic management systems repurposes the use of Internet of things, Time Series Forecasting, and Digital Image Processing. Computer Vision-based smart traffic management is an emerging area of research. Therefore a real-time traffic light optimization algorithm that uses Machine Learning and Deep Learning Techniques to predict the optimal time required by the vehicles to clear the lane is presented. This article concentrates on a two-step approach. The first step is to obtain the count of the independent category of the class of vehicles. For this, the You Only Look Once version 4 (YOLOv4) object detection technique is employed. In the second step, an ensemble technique named eXtreme Gradient Boosting (XGBoost) for predicting the optimal time of the green light window is implemented. Furthermore, the different implemented versions of YOLO and different prediction algorithms are compared with the proposed approach. The experimental analysis signifies that YOLOv4 with the XGBoost algorithm produces the most precise outcomes with a balance of accuracy and inference time. The proposed approach elegantly reduces an average of 32.3% of waiting time with usual traffic on the road.

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Section: Literature Reviewmentioning

confidence: 99%

An amalgamation of YOLOv4 and XGBoost for next-gen smart traffic management system

Dave

Chandarana

Goel

et al. 2021

PeerJ Computer Science

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show abstract

“…RL and deep RL applications for TSC are mostly performed on traffic simulators due to life-threatening conditions in realworld experiments. Some authors also use real datasets for experimental study, but still they create a simulation environment based on the real data [50]. Microscopic individual vehiclebased simulators have been used throughout the years for ITS applications.…”

Section: E Simulation Environmentsmentioning

confidence: 99%

“…The same real-world data and a synthetic simulation data which generates traffic with uniform distribution is used on an isolated intersection for experiments. Another DQN-based study for traffic light control with a real dataset is presented in [50]. Data from a three-way non-homogeneous real intersection in Lebanon is used.…”

Section: B Deep Rl Applicationsmentioning

confidence: 99%

Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey

Haydari¹,

Yılmaz²

2020

Preprint

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Latest technological improvements increased the quality of transportation. New data-driven approaches bring out a new research direction for all control-based systems, e.g., in transportation, robotics, IoT and power systems. Combining data-driven applications with transportation systems plays a key role in recent transportation applications. In this paper, the latest deep reinforcement learning (RL) based traffic control applications are surveyed. Specifically, traffic signal control (TSC) applications based on (deep) RL, which have been studied extensively in the literature, are discussed in detail. Different problem formulations, RL parameters, and simulation environments for TSC are discussed comprehensively. In the literature, there are also several autonomous driving applications studied with deep RL models. Our survey extensively summarizes existing works in this field by categorizing them with respect to application types, control models and studied algorithms. In the end, we discuss the challenges and open questions regarding deep RLbased transportation applications.

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“…However, the environmental observation was not enough to decrease the overall congestion, and agents make their policy without looking at the neighboring signal's state. Natafgi et al [40] implemented an adaptive traffic light system for one isolated intersection considering queuing times and queue length. However, the real environment consists of multiple intersections.…”

Section: Intelligent Approachesmentioning

confidence: 99%

“…To reduce complexity, all intersections use aligned SPaT cycles of the same length (are synchronous). A decentralized and distributed (non-collaborated) multi-agent RL methodology, as proposed in [35,39,40] was also compared. Each decentralized intersection housed an individual RL Agent with S2 state space, only.…”

Section: E Comparison Of Centralized Decentralized and Collaborativmentioning

confidence: 99%

Reducing Congestion in an Intelligent Traffic System With Collaborative and Adaptive Signaling on the Edge

Jaleel¹,

Hassan

Mahmood³

et al. 2020

IEEE Access

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Smart Traffic Light System Using Machine Learning

Cited by 23 publications

References 3 publications

An amalgamation of YOLOv4 and XGBoost for next-gen smart traffic management system

An amalgamation of YOLOv4 and XGBoost for next-gen smart traffic management system

Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey

Reducing Congestion in an Intelligent Traffic System With Collaborative and Adaptive Signaling on the Edge

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