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

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A Correction to this article was published on 18 August 2023

A Correction to this article was published on 13 June 2023

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Abstract

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.

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The data and material used to support the findings of this study are available from the corresponding author upon request.

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  1. Mukesh Kumar author contributed equally to this work.

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  1. Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, 800005, Bihar, India

    Raushan Kumar Singh & Mukesh Kumar

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“The original online version of this article was revised:” Author Raushan Kumar Singh removes his second affiliation; his correct affiliation is: Department of Computer Science and Engineering, National Institute of Technology, Ashok Rajpath, Patna 800005, Bihar, India. The original article has been corrected.

“The original online version of this article was revised: ” Author Raushan Kumar Singh’s correct affiliation is: Department of Computer Science and Engineering, National Institute of Technology Patna, Bihar, India. The original article has been corrected.

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Singh, R.K., Kumar, M. Future trends of path planning framework considering accident attributes for smart cities. J Supercomput 79, 16884–16913 (2023). https://doi.org/10.1007/s11227-023-05305-0

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