Special Issues
Table of Content

Transport Resilience and Emergency Management in the Era of Artificial Intelligence

Submission Deadline: 15 December 2022 (closed) View: 31

Guest Editors

Dr. Gabriel Gomes de Oliveira, UNICAMP - State University of Campinas, Brazil.
Dr. S.Kannadhasan, Study World College of Engineering, India.
Prof. Yuzo Iano, UNICAMP - State University of Campinas, Brazil.
Prof. Rangel Arthur, UNICAMP - State University of Campinas, Brazil.
Prof. Euclides Lourenço Chuma, UNICAMP - State University of Campinas, Brazil.
Prof. Navid Razmjooy, Azad University, Iran.

Summary

The objective of this special edition is to enable the search for excellent works in the area of Data-driven Sustainable Mobility. In innovative concepts that are reality and future for the entire world society, such as Artificial Intelligence, Big Data, Sensors, IoT, and 5G. Concepts that will bring a new paradigm to the entire planet in terms of Sustainable Mobility.


Keywords

Artificial Intelligence, Big Data, IoT, Sensors, Smart City, Sustainable Mobility and 5G

Published Papers


  • Open Access

    ARTICLE

    Traffic Scene Captioning with Multi-Stage Feature Enhancement

    Dehai Zhang, Yu Ma, Qing Liu, Haoxing Wang, Anquan Ren, Jiashu Liang
    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2901-2920, 2023, DOI:10.32604/cmc.2023.038264
    (This article belongs to the Special Issue: Transport Resilience and Emergency Management in the Era of Artificial Intelligence)
    Abstract Traffic scene captioning technology automatically generates one or more sentences to describe the content of traffic scenes by analyzing the content of the input traffic scene images, ensuring road safety while providing an important decision-making function for sustainable transportation. In order to provide a comprehensive and reasonable description of complex traffic scenes, a traffic scene semantic captioning model with multi-stage feature enhancement is proposed in this paper. In general, the model follows an encoder-decoder structure. First, multi-level granularity visual features are used for feature enhancement during the encoding process, which enables the model to learn… More >

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