Optimized Intersection Signal Timing: An Intelligent Approach-Based Study for Sustainable Models

An, Hong Ki; Javeed, Muhammad Awais; Bae, Gi-Mok; Zubair, Nimra; Metwally, Ahmed Sayed M.; Bocchetta, Patrizia; Ning, Fan; Javed, Muhammad Sufyan

doi:10.3390/su141811422

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…Using convex (quadratic) programming, [15] optimized traffic signal timings at an isolated intersection. In [16], a new algorithm for traffic control optimization was proposed using the number of vehicles in the queue. A comparison of this algorithm with GA and PSA algorithms has shown a 10 percent and 16 percent improvement in queue length reduction, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

A Resilient Intelligent Traffic Signal Control Scheme for Accident Scenario at Intersections via Deep Reinforcement Learning

2023

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Deep reinforcement learning methods have shown promising results in the development of adaptive traffic signal controllers. Accidents, weather conditions, or special events all have the potential to abruptly alter the traffic flow in real life. The traffic light must take immediate and appropriate action based on a reasonable understanding of the environment. In this way, traffic congestion would be prevented. In this paper, we develop a reliable controller for such a highly dynamic environment and investigate the resilience of these controllers to a variety of environmental disruptions, such as accidents. In this method, the agent is provided with a complete understanding of the environment by discretizing the intersection and modifying the state space. The proposed algorithm is independent of the location and time of accidents. If the location of the accident changes, the agent does not need to be retrained. The agent is trained using deep Q-learning and experience replay. The model is evaluated in the traffic microsimulator SUMO. The simulation results demonstrate that the proposed method is effective at shortening queues when there is disruption.

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Section: Literature Reviewmentioning

confidence: 99%

A Resilient Intelligent Traffic Signal Control Scheme for Accident Scenario at Intersections via Deep Reinforcement Learning

2023

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“…It should be also noted that long idling times in queues are one of the major causes of extra fuel consumption at signalized intersections. Some of the solutions include the estimation of the maximum queue size at the signal-controlled intersection approach (so-called: maximum back-of-queue) [8] and optimize traffic signal control modeling considering the number of vehicles in a queue [9].…”

Section: Introductionmentioning

confidence: 99%

Traffic Signal Optimization to Improve Sustainability: A Literature Review

Alshayeb¹,

Stevanovic

Mitrović³

et al. 2022

Energies

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Optimizing traffic signals to improve traffic progression relies on minimizing mobility performance measures (e.g., delays and stops). However, delay and stop minimizations do not necessarily lead to minimal sustainability measures (e.g., fuel consumption and emissions). For that reason, researchers have focused, for decades, on integrating traffic models, signal optimization models, and fuel consumption and emissions models to minimize sustainability metrics while keeping acceptable levels of mobility metrics. Therefore, this paper reviews, classifies, and analyzes studies found in the literature regarding optimizing sustainable traffic signals. This paper provides researchers with a good starting point to further develop solutions which can address sustainable traffic control. To achieve that, this study details the most notable sustainable signal timing optimization studies from six perspectives: traffic models, fuel consumption and emissions models, optimization methods, objective functions, operating conditions, and reported sustainability savings. Outcomes of this research show that the previous studies deployed many combinations of elements from the six-perspective mentioned above, leading to a wide range of fuel consumption and emissions savings. The study also concludes that the available fuel consumption and emissions models are relatively old. Hence, future research is needed to develop new fuel consumption and emissions models based on recently collected data.

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Evaluation signalized intersection in Najaf city

Hussain,

Shubber

2024

AIP Conference Proceedings

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Optimized Intersection Signal Timing: An Intelligent Approach-Based Study for Sustainable Models

Cited by 4 publications

References 41 publications

A Resilient Intelligent Traffic Signal Control Scheme for Accident Scenario at Intersections via Deep Reinforcement Learning

A Resilient Intelligent Traffic Signal Control Scheme for Accident Scenario at Intersections via Deep Reinforcement Learning

Traffic Signal Optimization to Improve Sustainability: A Literature Review

Evaluation signalized intersection in Najaf city

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