Atlas Analysis of the Impact of the Interval Changes in Yellow Light Signals on Driving Behavior

Zheng, Haoran; Qin, Yaqin; Du, Zhigang

doi:10.1109/access.2021.3067167

Cited by 2 publications

(1 citation statement)

References 22 publications

(21 reference statements)

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“…Joyo et al (2020) proposed an intelligent traffic-lights management system by exploiting relationship between traffic lights' duration and transition pattern [8]. Zheng et al (2021) proposed a reasonable transition method for yellow light signals and help formulate customized training programs for different types of drivers [9]. Mussa and Selekwa (2021) presented a signal timing transition period based on the quadratic optimization method [10].…”

Section: Introductionmentioning

confidence: 99%

OAS Deep Q-Learning-Based Fast and Smooth Control Method for Traffic Signal Transition in Urban Arterial Tidal Lanes

Dong,

Xie,

et al. 2024

Sensors

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To address traffic flow fluctuations caused by changes in traffic signal control schemes on tidal lanes and maintain smooth traffic operations, this paper proposes a method for controlling traffic signal transitions on tidal lanes. Firstly, the proposed method includes designing an intersection overlap phase scheme based on the traffic flow conflict matrix in the tidal lane scenario and a fast and smooth transition method for key intersections based on the flow ratio. The aim of the control is to equalize average queue lengths and minimize average vehicle delays for different flow directions at the intersection. This study also analyses various tidal lane scenarios based on the different opening states of the tidal lanes at related intersections. The transitions of phase offsets are emphasized after a comprehensive analysis of transition time and smoothing characteristics. In addition, this paper proposes a coordinated method for tidal lanes to optimize the phase offset at arterial intersections for smooth and rapid transitions. The method uses Deep Q-Learning, a reinforcement learning algorithm for optimal action selection (OSA), to develop an adaptive traffic signal transition control and enhance its efficiency. Finally, a simulation experiment using a traffic control interface is presented to validate the proposed approach. This study shows that this method leads to smoother and faster traffic signal transitions across different tidal lane scenarios compared to the conventional method. Implementing this solution can benefit intersection groups by reducing traffic delays, improving traffic efficiency, and decreasing air pollution caused by congestion.

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Section: Introductionmentioning

confidence: 99%

OAS Deep Q-Learning-Based Fast and Smooth Control Method for Traffic Signal Transition in Urban Arterial Tidal Lanes

Dong,

Xie,

et al. 2024

Sensors

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Estimation of the net photosynthetic rate for waterlogged winter wheat based on digital image technology

Gao

et al. 2023

Agronomy Journal

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The net photosynthetic rate (Pn) is one of the important indicators to measure photosynthetic capacity of crops. Therefore, it is critically important to find real-time methods for accurately estimating Pn of winter wheat (Triticum aestivum L.). This information could provide guidance on the management of waterlogging stress. To explore the optimal monitoring method for Pn of winter wheat under waterlogging stress, the correlations between Pn and 16 characteristic image indices were analyzed in irrigated and drained microplot experiments. Then, based on the indices values, Pn was estimated using the multiple linear regression (MLR), support vector machine (SVM), backpropagation neural network (BP), and random forest (RF) models, which were constructed based on the optimal monitoring image indices. Water logging when compared to no waterlogged wheat had similar Pn values <6 days.After 12 days, waterlogged wheat plants had a lower Pn value than in no waterlogged plants. All indices were correlated with the Pn (p < 0.05), and Pn estimation accuracy was lower at the winter wheat flowering and complete ripeness periods than at the milky and waxy ripe maturity periods. Based on the results of the models tested, the RF model had higher R 2 (0.904) values than the other models. These findings suggest that machine learning models could be used to accurately predict Pn, and the random forest algorithm was the best.

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Atlas Analysis of the Impact of the Interval Changes in Yellow Light Signals on Driving Behavior

Cited by 2 publications

References 22 publications

OAS Deep Q-Learning-Based Fast and Smooth Control Method for Traffic Signal Transition in Urban Arterial Tidal Lanes

OAS Deep Q-Learning-Based Fast and Smooth Control Method for Traffic Signal Transition in Urban Arterial Tidal Lanes

Estimation of the net photosynthetic rate for waterlogged winter wheat based on digital image technology

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