Since few of researches projects in the literature interested in solving traffic jam problem for emergency vehicles, the contribution of this paper is to introduces a novel approach to operate traffic light system. One key solution to solve traffic jam on intersections is the dynamic traffic lights, where traffic light operation adapts based on the intersection traffic conditions. This delay can cause a life threat in case of emergency vehicles, such as ambulance vehicles and police cars.
Traffic jam has a bad impact on drivers and road users due to the time delay it causes for road users to reach their destinations. Traditional traffic light system, which works based on fixed cycle can be a main reason for traffic jam, due to lack of adaptation to road conditions.
The experiments demonstrate that the PDF of our method is 0.90 for 10 malicious nodes, which is higher than in the TBM, TVR, and SAODV. This BA-CNN is compared with counterparts, including three different existing methods such as TBM, TVR, and SAODV. The performance evaluation of the BA-CNN algorithm is based on the following metrics: end-to-end delay (EED), throughput, and packet delivery fraction (PDF). The neural network is used for finding the shortest routes between source and destination. The offline data are then trained by utilizing a neural network. The bat algorithm provides offline data for a possible combination of different source and destination coordinates. Then, the shortest path (which leads to the fast reach time) to the hospital is calculated.
When the vehicle arrives at the accident location, the driver updates the algorithm with hospital and accident positions. The algorithm then obtains the shortest path to reach the location of the accident by the driver. The driver feeds the data that contain the ambulance vehicle’s node position and the accident location to the BA-CNN vehicle routing algorithm. In the beginning, information about the accident place is received by the control station and forwarded to both the hospital and the ambulance. The node method is responsible for creating the city map. The type of CNN used in this research is a residual network (ResNet). It aims to take transfer the patients confidentially, accurately, and quickly. The approach is based on the bat algorithm and convolutional neural network (BA-CNN). This article proposes an ambulance vehicle routing approach in smart cities.
Moreover, a portable controller device is designed to solve the problem of emergency vehicles stuck in the overcrowded roads. We propose a system based on PIC microcontroller that evaluates the traffic density using IR sensors and accomplishes dynamic timing slots with different levels. This leads to traffic jam and congestion. In addition, the mutual interference between adjacent traffic light systems, the disparity of cars flow with time, the accidents, the passage of emergency vehicles, and the pedestrian crossing are not implemented in the existing traffic system. Conventional systems do not handle variable flows approaching the junctions. However, the synchronization of multiple traffic light systems at adjacent intersections is a complicated problem given the various parameters involved. They aim to realize smooth motion of cars in the transportation routes. Traffic light control systems are widely used to monitor and control the flow of automobiles through the junction of many roads.