Commonly,the standards for the geometric design of roads refer to a given set of values for the friction coefficient(longitudinal and transverse friction).These"reference"values imply corresponding visibilit...Commonly,the standards for the geometric design of roads refer to a given set of values for the friction coefficient(longitudinal and transverse friction).These"reference"values imply corresponding visibility sights,curvature radii,and speed limits.Unfortunately,not only do these reference values not correspond to a given standard to measure them,but nothing is said about the decrease of the posted speed limit(variable speed limits)when roads become slippery and lanes for autonomous vehicle(AV)are concerned.Furthermore,the same assessment of the friction coefficient has plenty of uncertainties due to measurement device,temperature,location,time passed from the construction,alignment-related variables(e.g.,curve,tangent,transition curve,convexity/crests or concavity/sags,longitudinal slope,superelevation,and ruling gradient),and supplementary singularities such as joints and bridge approaches.All the issues above may harm road safety and the complexity of forensic investigations of pavements.Consequently,this study's objectives were confined to(1)carrying out friction measurements and analyzing the problem of friction decay over time;(2)setting up a method to lower the speed limits where friction decays are detected;(3)setting up a method to handle friction decays for autonomous vehicles.Results demonstrate that:(1)a power law describes how the speed limits are affected by friction;(2)for speeds up to 170 km/h,due to the lower reaction time,AV reaction distance is lower,which benefits AV traffic(lower stopping distance);(3)on the contrary,for higher values of friction and higher speeds,under the hypothesis of having the same reaction time law for non-AV(NAV)(i.e.,decreasing with the initial speed),AV speed limits become lower than NAV speed limits;(4)not only do comfort-based speed profiles for AVs bring higher braking distances,but also,in the median part(of the deceleration process),this could pose safety issues and reduce the distance between the available and the needed friction.展开更多
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
文摘Commonly,the standards for the geometric design of roads refer to a given set of values for the friction coefficient(longitudinal and transverse friction).These"reference"values imply corresponding visibility sights,curvature radii,and speed limits.Unfortunately,not only do these reference values not correspond to a given standard to measure them,but nothing is said about the decrease of the posted speed limit(variable speed limits)when roads become slippery and lanes for autonomous vehicle(AV)are concerned.Furthermore,the same assessment of the friction coefficient has plenty of uncertainties due to measurement device,temperature,location,time passed from the construction,alignment-related variables(e.g.,curve,tangent,transition curve,convexity/crests or concavity/sags,longitudinal slope,superelevation,and ruling gradient),and supplementary singularities such as joints and bridge approaches.All the issues above may harm road safety and the complexity of forensic investigations of pavements.Consequently,this study's objectives were confined to(1)carrying out friction measurements and analyzing the problem of friction decay over time;(2)setting up a method to lower the speed limits where friction decays are detected;(3)setting up a method to handle friction decays for autonomous vehicles.Results demonstrate that:(1)a power law describes how the speed limits are affected by friction;(2)for speeds up to 170 km/h,due to the lower reaction time,AV reaction distance is lower,which benefits AV traffic(lower stopping distance);(3)on the contrary,for higher values of friction and higher speeds,under the hypothesis of having the same reaction time law for non-AV(NAV)(i.e.,decreasing with the initial speed),AV speed limits become lower than NAV speed limits;(4)not only do comfort-based speed profiles for AVs bring higher braking distances,but also,in the median part(of the deceleration process),this could pose safety issues and reduce the distance between the available and the needed friction.
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.
