Artificial Intelligence in Transportation Safety and Traffic Management

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 743

Special Issue Editor

Dr. Yuanbo Xu
E-Mail Website
Guest Editor
College of Computer Science and Technology, Jilin University, Changchun 130012, China
Interests: machine learning; artificial intelligence; abnormal data monitoring; smart city

Special Issue Information

Dear Colleagues,

A standardized and intelligent traffic system is necessary to improve transportation safety and traffic management efficiency. Artificial intelligence can predict and solve various types of problems such as intersection signal control, traffic scheduling, and vehicle motion planning, so as to achieve the regulation of the traffic system. This Special Issue aims to share innovative ideas on how artificial intelligence can improve transportation safety and traffic management efficiency.

This Special Issue therefore welcomes original application-focused research and investigations using AI to predict and solve traffic problems, such as graph neural networks, graph attention networks, and so on. It aims to promote communication and interaction between researchers in different fields. We invite high quality original research articles as well as review articles.

Dr. Yuanbo Xu
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent traffic
  • artificial intelligence
  • transportation safety
  • traffic prediction
  • graph neural network
  • graph attention network
  • machine learning

Published Papers (1 paper)

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Research

19 pages, 2082 KiB  
Article
Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
Appl. Sci. 2023, 13(23), 12696; https://doi.org/10.3390/app132312696 - 27 Nov 2023
Viewed by 469
Abstract
The rapid proliferation of handheld intelligent devices and the advent of 5G technology have brought about convenient and fast services for people. In perception-oriented application services, participating users will upload sensitive mobile data in order to obtain benefits. While devising privacy protection strategies [...] Read more.
The rapid proliferation of handheld intelligent devices and the advent of 5G technology have brought about convenient and fast services for people. In perception-oriented application services, participating users will upload sensitive mobile data in order to obtain benefits. While devising privacy protection strategies to ensure data security, it is crucial to accomplish task perception related to data collection to the fullest extent possible. To address this challenge, this paper proposes a personalized data privacy protection algorithm based on an adaptive dynamic adjustment grid and the minimum wage task allocation strategy. According to the different levels of users’ needs for privacy protection, combined with the privacy budget allocation strategy, we design a different-level differential privacy protection mechanism and consider the reward mechanism in task allocation to balance the effectiveness and security of the location data uploaded by users. Experiments show that the strategy proposed in this paper can not only protect the data but also enable users to freely choose the level of privacy protection. Full article
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