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Electronics
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Big Data and Machine Learning for Vehicles and Transportation
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Big Data and Machine Learning for Vehicles and Transportation

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 1823

Special Issue Editors

Prof. Dr. Ciprian Dobre

E-Mail Website
Guest Editor
Computer Science Department, University Politehnica of Bucharest, 060042 Bucharest, Romania
Interests: mobile computing; pervasive systems; monitoring tools; context awareness
Prof. Dr. Nuno M. Garcia

E-Mail Website
Guest Editor
Computer Science Department, University of Beira Interior, 6200-001 Covilhã, Portugal
Interests: next-generation networks; algorithms for bio-signal processing; distributed and cooperative protocols; predictive algorithms for health and well-being
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cities are areas where Big Data is having a real impact. Town planners and administration bodies need the right tools at their fingertips to consume all the data that a town or city generates and then be able to turn that into actions that improve peoples’ lives. Smart Cities already rely not only on sensors within the city infrastructure, but also on many devices that willingly sense and integrate their data into technological platforms used for introspection into the habits and situations of individuals and city-sized communities.

Every day we create an incredible amount of data—over 90% of the data in the world today has been created in the last two years alone. Efficiently handling such quantities of data is already a challenge. New technologies have finally reached a stage of development in which they can significantly improve the lives of any city’s inhabitants. Our cities are rapidly transforming into artificial ecosystems of interconnected, interdependent, intelligent digital ‘‘organisms’’. They are transforming into smart cities, as they benefit from intelligent applications designed to drive sustainable economic development and represent an incubator of innovation and transformation. However, we have barely begun to get a sense of the dimensions of this kind of data, of the privacy implications, of ways in which we can code it with respect to meaningful attributes in space and time. As we move into an era of unprecedented volumes of data and computing power, the benefits are not for business alone. Data can help citizens access government, hold it accountable and build new services to help themselves. In one sense, all this is part of a world that is fast becoming digital in all its dimensions. People will more easily develop their understanding and design ideas using digital representations and data. This vision will support the development of new ideas for the future of urban and social life.

This Special Issue is dedicated to showcasing advances Big Data and Machine Learning for Vehicles and Transportation. We invite prospective authors to showcase their recent work on creating solutions to support data processing or interpretation, learning, and embedding deep knowledge algorithms for the Smart Cities of tomorrow. The vast body of literature on the topic will be complemented with recent work, and the topic accommodates many different aspects related to data support for cities. As an example, over the last few decades, traffic congestion alone has become one of the biggest problems that the world is struggling to solve. Road traffic is endlessly increasing everywhere on the globe, causing expanding traffic jams and air pollution. To limit the pollution produced by cars and to streamline roads, this Special Issue is the perfect venue for presenting recent work at the combination of smart city architectures and town infrastructure with intelligent management systems relying on Big Data processing. We welcome solutions that will minimize the average waiting time in traffic, decongest intersections, promote public transportation, and prevent accidents from happening when violating the red-light interdiction, to name just a few.

Prof. Dr. Ciprian Dobre
Prof. Dr. Nuno M. Garcia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Electronics 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

  • smart city
  • vehicle networks
  • smart transportation
  • Big Data
  • deep learning
  • data processing

Published Papers (2 papers)

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Research

24 pages, 8182 KiB  
Article
On the Evaluation Framework of Comprehensive Trust for Data Interaction in Intermodal Transport
by Xin Geng, Yinghong Wen, Zhisong Mo, Peng Dong, Fanpeng Kong and Ke Xiong
Electronics 2024, 13(8), 1487; https://doi.org/10.3390/electronics13081487 - 14 Apr 2024
Viewed by 315
Abstract
Due to the necessity to realize “building a strong transportation nation”, the construction of intermodal transportation is based on the information resource integration of diverse transport systems. To ensure the data security during the interaction between different transport modes, as well as the [...] Read more.
Due to the necessity to realize “building a strong transportation nation”, the construction of intermodal transportation is based on the information resource integration of diverse transport systems. To ensure the data security during the interaction between different transport modes, as well as the effect of data application, the status of entities and data flow in the network should be supervised throughout. Therefore, an evaluation framework of comprehensive trust is proposed in this paper. With feature analysis of transportation big data, a quality assessment is conducted by three-dimensional metric sets, which is considered as a significant factor of trust measurement. Furthermore, a hierarchical trust structure is put forward to assess the trust of entities in different levels, in terms of the static and dynamic evidence. Furthermore, the visualization of a dynamic global information security state is discussed, based on temporal knowledge graphs. As shown in practical application and simulation analysis, this framework can meet the requirements of data security supervision and lay the foundation of further intelligent management. This research is of great significance to improve the data security level in intermodal transport, and to promote the utilization and sharing of public information resources. Full article
(This article belongs to the Special Issue Big Data and Machine Learning for Vehicles and Transportation)
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16 pages, 4271 KiB  
Article
Enhanced Information Graph Recursive Network for Traffic Forecasting
by Cheng Ma, Kai Sun, Lei Chang and Zhijian Qu
Electronics 2023, 12(11), 2519; https://doi.org/10.3390/electronics12112519 - 02 Jun 2023
Viewed by 1015
Abstract
Accurate traffic forecasting is crucial for the advancement of smart cities. Although there have been many studies on traffic forecasting, the accurate forecasting of traffic volume is still a challenge. To effectively capture the spatio-temporal correlations of traffic data, a deep learning-based traffic [...] Read more.
Accurate traffic forecasting is crucial for the advancement of smart cities. Although there have been many studies on traffic forecasting, the accurate forecasting of traffic volume is still a challenge. To effectively capture the spatio-temporal correlations of traffic data, a deep learning-based traffic volume forecasting model called the Enhanced Information Graph Recursive Network (EIGRN) is presented in this paper. The model consists of three main parts: a Graph Embedding Adaptive Graph Convolution Network (GE-AGCN), a Modified Gated Recursive Unit (MGRU), and a local information enhancement module. The local information enhancement module is composed of a convolutional neural network (CNN), a transposed convolutional neural network, and an attention mechanism. In the EIGRN, the GE-AGCN is used to capture the spatial correlation of the traffic network by adaptively learning the hidden information of the complex topology, the MGRU is employed to capture the temporal correlation by learning the time change of the traffic volume, and the local information enhancement module is employed to capture the global and local correlations of the traffic volume. The EIGRN was evaluated using the real datasets PEMS-BAY and PeMSD7(M) to assess its predictive performance The results indicate that the forecasting performance of the EIGRN is better than the comparison models. Full article
(This article belongs to the Special Issue Big Data and Machine Learning for Vehicles and Transportation)
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