Due to excessive car usage,pollution and traffic have increased.In urban cities in Saudi Arabia,such as Riyadh and Jeddah,drivers and air quality suffer from traffic congestion.Although the government has implemented ...Due to excessive car usage,pollution and traffic have increased.In urban cities in Saudi Arabia,such as Riyadh and Jeddah,drivers and air quality suffer from traffic congestion.Although the government has implemented numerous solutions to resolve this issue or reduce its effect on the environment and residents,it still exists and is getting worse.This paper proposes an intelligent,adaptive,practical,and feasible deep learning method for intelligent traffic control.It uses an Internet of Things(IoT)sensor,a camera,and a Convolutional Neural Network(CNN)tool to control traffic in real time.An image segmentation algorithm analyzes inputs from the cameras installed in designated areas.This study considered whether CNNs and IoT technologies could ensure smooth traffic flow in high-speed,high-congestion situations.The presented algorithm calculates traffic density and cars’speeds to determine which lane gets high priority first.A real case study has been conducted on MATLAB to verify and validate the results of this approach.This algorithm estimates the reduced average waiting time during the red light and the suggested time for the green and red lights.An assessment between some literature works and the presented algorithm is also provided.In contrast to traditional traffic management methods,this intelligent and adaptive algorithm reduces traffic congestion,automobile waiting times,and accidents.展开更多
A critical component of the smart grid (SG) infrastructure is the embedded communications network, where an important objective of the latter is the expansion of its throughput, in conjunction with the satisfaction of...A critical component of the smart grid (SG) infrastructure is the embedded communications network, where an important objective of the latter is the expansion of its throughput, in conjunction with the satisfaction of specified latency and accuracy requirements. For the effective design of the communications network, the user and traffic profiles, such as known-user vs. unknown-user populations and bursty vs. non-bursty data traffics, must be carefully considered and subsequently modeled. This paper relates user and traffic models to the deployment of effective multiple access transmission algorithms in the communications network of the SG.展开更多
文章提出了一种基于机器学习和控制算法的智慧城市交通信号灯优化方法。通过使用长短期记忆(Long Short Term Memory,LSTM)网络进行交通流量预测,捕捉了交通流的时空关系,为信号灯控制提供了准确的输入数据。采用比例积分微分(Proportio...文章提出了一种基于机器学习和控制算法的智慧城市交通信号灯优化方法。通过使用长短期记忆(Long Short Term Memory,LSTM)网络进行交通流量预测,捕捉了交通流的时空关系,为信号灯控制提供了准确的输入数据。采用比例积分微分(Proportion Integration Differentiation,PID)控制器,通过实时调整信号灯来实现对交通流的精准控制。仿真实验结果表明,该方法在不同交通场景下均表现出色,有效提高了交叉口的通行效率,减少了拥堵现象,为城市交通的智能化管理提供了可行的解决方案。展开更多
基金This research work was funded by Institutional Fund Projects under Grant No.(IFPIP:707-829-1443)The authors gratefully acknowledge technical and financial support provided by theMinistry of Education and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘Due to excessive car usage,pollution and traffic have increased.In urban cities in Saudi Arabia,such as Riyadh and Jeddah,drivers and air quality suffer from traffic congestion.Although the government has implemented numerous solutions to resolve this issue or reduce its effect on the environment and residents,it still exists and is getting worse.This paper proposes an intelligent,adaptive,practical,and feasible deep learning method for intelligent traffic control.It uses an Internet of Things(IoT)sensor,a camera,and a Convolutional Neural Network(CNN)tool to control traffic in real time.An image segmentation algorithm analyzes inputs from the cameras installed in designated areas.This study considered whether CNNs and IoT technologies could ensure smooth traffic flow in high-speed,high-congestion situations.The presented algorithm calculates traffic density and cars’speeds to determine which lane gets high priority first.A real case study has been conducted on MATLAB to verify and validate the results of this approach.This algorithm estimates the reduced average waiting time during the red light and the suggested time for the green and red lights.An assessment between some literature works and the presented algorithm is also provided.In contrast to traditional traffic management methods,this intelligent and adaptive algorithm reduces traffic congestion,automobile waiting times,and accidents.
文摘A critical component of the smart grid (SG) infrastructure is the embedded communications network, where an important objective of the latter is the expansion of its throughput, in conjunction with the satisfaction of specified latency and accuracy requirements. For the effective design of the communications network, the user and traffic profiles, such as known-user vs. unknown-user populations and bursty vs. non-bursty data traffics, must be carefully considered and subsequently modeled. This paper relates user and traffic models to the deployment of effective multiple access transmission algorithms in the communications network of the SG.
文摘文章提出了一种基于机器学习和控制算法的智慧城市交通信号灯优化方法。通过使用长短期记忆(Long Short Term Memory,LSTM)网络进行交通流量预测,捕捉了交通流的时空关系,为信号灯控制提供了准确的输入数据。采用比例积分微分(Proportion Integration Differentiation,PID)控制器,通过实时调整信号灯来实现对交通流的精准控制。仿真实验结果表明,该方法在不同交通场景下均表现出色,有效提高了交叉口的通行效率,减少了拥堵现象,为城市交通的智能化管理提供了可行的解决方案。
