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
Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada...Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.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Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.
