Correction to: Future trends of path planning framework considering accident attributes for smart cities

Residents and visitors throughout the globe respect sophisticated route-guiding systems. Due to India’s complicated city design and transportation infrastructure, an efficient and intelligent RG is required. Path planning is a crucial issue with route guiding systems for the road network. Because the time spent due to an accident occurring at an intersection or on the route is more relevant than the actual situation for path planning, it is helpful to investigate the path planning issue and incorporate accident characteristics. To address this problem, in this research, we developed a system that gives better routes by considering accident information. In our suggested framework, we addressed the future design and requirements of the prediction and route planning models for smart cities. We deployed an intelligent camera coupled with a machine learning module to forecast an accident at any junction or on the road. We update our database regularly after receiving accident information and use the route planning algorithm to get the different pathways with distance and accident information. These pathways enable the user to choose the best course for them. It is a dynamic and continuous 24x7 task. Thus, we have deployed VGG16, VGG19, and RESNET DL Architecture to detect the accident images with accuracies of 99.72%, 98.78%, and 82.06%, respectively. In the 2-class dataset, we have taken-accident and non-accident pictures from Kaggle. The number of training images was 900 and 200 for testing in both classes. Based on the Accident detection, the Dijkstra Algorithm was continuously up-to-date to get the path. It has been discovered that enhancing the intelligent RG system with deep learning can significantly improve prediction accuracy. Even though the experiment has some flaws, it offers practical guidance for the subsequent stages of the evolution of the transportation system.