Traffic on Indian roads is highly mixed in nature with wide variations in the static and dynamic characteristics of vehicles. These vehicles do not follow strict lane discipline and occupy any available lateral positi...Traffic on Indian roads is highly mixed in nature with wide variations in the static and dynamic characteristics of vehicles. These vehicles do not follow strict lane discipline and occupy any available lateral position on the road space. Overtaking is one of the most complex and important manoeuvre on undivided roads where the vehicles use the opposing lane to overtake the slower vehicles with the presence of oncoming vehicles from opposite direction. They are unavoidable especially in the case of mixed traffic conditions where there is always a speed difference between the fast and slow moving vehicles. Overtaking process involves lane-changing manoeuvres, acceleration and deceleration actions and estimation of relative speed of overtaking and overtaken vehicles, and also, estimation of speed and distance of the oncoming vehicle. The main objective of the present study is to study the overtaking characteristics of vehicles on undivided roads under mixed traffic conditions. For this purpose, details of overtaking data were collected on a two-lane two-way undivided road using moving car observer method and registration plate method. The overtaking characteristics of all types of vehicles under mixed traffic conditions were observed and mathematically modelled. The data extracted and analysed were the acceleration characteristics, speeds of the overtaking vehicles, overtaking time, overtaking distances, safe opposing gap required for overtaking, flow rates, overtaking frequencies, types of overtaking strategy, and types of overtaking and overtaken vehicles. Two types of overtaking strategies were observed in the field such as flying overtaking and accelerative overtaking. Graphs were plotted between the relative speed of the overtaking and overtaken vehicles against the overtaking time and negative correlation was found between the speed differential and total overtaking time for all categories of vehicles. It was observed that the number of overtaking increases with increase in the flow rate in the on-going dire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Traffic on Indian roads is highly mixed in nature with wide variations in the static and dynamic characteristics of vehicles. These vehicles do not follow strict lane discipline and occupy any available lateral position on the road space. Overtaking is one of the most complex and important manoeuvre on undivided roads where the vehicles use the opposing lane to overtake the slower vehicles with the presence of oncoming vehicles from opposite direction. They are unavoidable especially in the case of mixed traffic conditions where there is always a speed difference between the fast and slow moving vehicles. Overtaking process involves lane-changing manoeuvres, acceleration and deceleration actions and estimation of relative speed of overtaking and overtaken vehicles, and also, estimation of speed and distance of the oncoming vehicle. The main objective of the present study is to study the overtaking characteristics of vehicles on undivided roads under mixed traffic conditions. For this purpose, details of overtaking data were collected on a two-lane two-way undivided road using moving car observer method and registration plate method. The overtaking characteristics of all types of vehicles under mixed traffic conditions were observed and mathematically modelled. The data extracted and analysed were the acceleration characteristics, speeds of the overtaking vehicles, overtaking time, overtaking distances, safe opposing gap required for overtaking, flow rates, overtaking frequencies, types of overtaking strategy, and types of overtaking and overtaken vehicles. Two types of overtaking strategies were observed in the field such as flying overtaking and accelerative overtaking. Graphs were plotted between the relative speed of the overtaking and overtaken vehicles against the overtaking time and negative correlation was found between the speed differential and total overtaking time for all categories of vehicles. It was observed that the number of overtaking increases with increase in the flow rate in the on-going dire
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.
