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Applied Sciences
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Application of Mobile Systems in Smart Vehicles and Smart Roads
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Application of Mobile Systems in Smart Vehicles and Smart Roads

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 9081

Special Issue Editor

Prof. Dr. Giuseppe Guido

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Calabria, 87036 Rende, Italy
Interests: intelligent transportation systems; road safety; smart cities; smart devices; transit systems; traffic control; traffic flow model; driver behavior
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The way of conceiving mobility and transport systems is undergoing a substantial change, mainly due to the development of technology and mobile communication protocols.

Consumers, companies and administrators have access to new markets for transport services, which make more opportunities to create green, efficient modes of transport, with the aim of reducing congestion and improving quality of life.

Under these assumptions, mobile systems are increasingly used for smart vehicles and smart roads solutions. In this context, a very challenging task for researchers is implementing mobile technologies which allow the collection, sharing and analysis of information about vehicles, road infrastructures and drivers to make the driving experience safe and enjoyable for road users. To address this important objective, an interdisciplinary approach is needed, especially considering the new advancements in transportation technologies such as V2V, V2I and V2X communications.

The motivation behind this Special Issue is the sharing of concepts, approaches, and techniques useful for implementing smart vehicles and smart roads through the employment of mobile systems. We encourage original submissions of high quality and innovative researches providing significant contributions in the theory and practice of mobile systems applicationd for smart transportation systems.

Prof. Giuseppe Guido
Guest Editor

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. 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

  • Smart roads
  • Connected and autonomous vehicles (CAVs)
  • Floating Car Data (FCD)
  • Mobile devices
  • V2X communication

Published Papers (4 papers)

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Research

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22 pages, 7021 KiB  
Article
Enhancing the Reliability of Communication between Vehicle and Everything (V2X) Based on Deep Learning for Providing Efficient Road Traffic Information
by Radwa Ahmed Osman, Sherine Nagy Saleh, Yasmine N. M. Saleh and Mazen Nabil Elagamy
Appl. Sci. 2021, 11(23), 11382; https://doi.org/10.3390/app112311382 - 1 Dec 2021
Cited by 5 | Viewed by 2211
Abstract
Developing efficient communication between vehicles and everything (V2X) is a challenging task, mainly due to the characteristics of vehicular networks, which include rapid topology changes, large-scale sizes, and frequent link disconnections. This article proposes a deep learning model to enhance V2X communication. Various [...] Read more.
Developing efficient communication between vehicles and everything (V2X) is a challenging task, mainly due to the characteristics of vehicular networks, which include rapid topology changes, large-scale sizes, and frequent link disconnections. This article proposes a deep learning model to enhance V2X communication. Various channel conditions such as interference, channel noise, and path loss affect the communication between a vehicle (V) and everything (X). Thus, the proposed model aims to determine the required optimum interference power to enhance connectivity, comply with the quality of service (QoS) constraints, and improve the communication link reliability. The proposed model fulfills the best QoS in terms of four metrics, namely, achievable data rate (Rb), packet delivery ratio (PDR), packet loss rate (PLR), and average end-to-end delay (E2E). The factors to be considered are the distribution and density of vehicles, average length, and minimum safety distance between vehicles. A mathematical formulation of the optimum required interference power is presented to achieve the given objectives as a constrained optimization problem, and accordingly, the proposed deep learning model is trained. The obtained results show the ability of the proposed model to enhance the connectivity between V2X for improving road traffic information efficiency and increasing road traffic safety. Full article
(This article belongs to the Special Issue Application of Mobile Systems in Smart Vehicles and Smart Roads)
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17 pages, 2307 KiB  
Article
Exploring the Direct and Indirect Use of ICT Measurements in DODME (Dynamic OD Matrix Estimation)
by Xavier Ros-Roca, Lídia Montero and Jaume Barceló
Appl. Sci. 2021, 11(22), 10910; https://doi.org/10.3390/app112210910 - 18 Nov 2021
Viewed by 1429
Abstract
The estimation of the network traffic state, its likely short-term evolution, the prediction of the expected travel times in a network, and the role that mobility patterns play in transport modeling is usually based on dynamic traffic models, whose main input is a [...] Read more.
The estimation of the network traffic state, its likely short-term evolution, the prediction of the expected travel times in a network, and the role that mobility patterns play in transport modeling is usually based on dynamic traffic models, whose main input is a dynamic origin–destination (OD) matrix that describes the time dependencies of travel patterns; this is one of the reasons that have fostered large amounts of research on the topic of estimating OD matrices from the available traffic information. The complexity of the problem, its underdetermination, and the many alternatives that it offers are other reasons that make it an appealing research topic. The availability of new traffic data measurements that were prompted by the pervasive penetration of information and communications technology (ICT) applications offers new research opportunities. This study focused on GPS tracking data and explored two alternative modeling approaches regarding how to account for this new information to solve the dynamic origin–destination matrix estimation (DODME) problem, either including it as an additional term in the formulation model or using it in a data-driven modeling method to propose new model formulations. Complementarily, independently of the approach used, a key aspect is the quality of the estimated OD, which, as recent research has made evident, is not well measured by the conventional indicators. This study also explored this problem for the proposed approaches by conducting synthetic computational experiments to control and understand the process. Full article
(This article belongs to the Special Issue Application of Mobile Systems in Smart Vehicles and Smart Roads)
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24 pages, 7576 KiB  
Article
A Novel Adaptive Approach for Autonomous Vehicle Based on Optimization Technique for Enhancing the Communication between Autonomous Vehicle-to-Everything through Cooperative Communication
by Radwa Ahmed Osman and Ahmed Kadry Abdelsalam
Appl. Sci. 2021, 11(19), 9089; https://doi.org/10.3390/app11199089 - 29 Sep 2021
Cited by 6 | Viewed by 2114
Abstract
Recent autonomous intelligent transportation systems commonly adopt vehicular communication. Efficient communication between autonomous vehicles-to-everything (AV2X) is mandatory to ensure road safety by decreasing traffic jamming, approaching emergency vehicle warning, and assisting in low visibility traffic. In this paper, a new adaptive AV2X model, [...] Read more.
Recent autonomous intelligent transportation systems commonly adopt vehicular communication. Efficient communication between autonomous vehicles-to-everything (AV2X) is mandatory to ensure road safety by decreasing traffic jamming, approaching emergency vehicle warning, and assisting in low visibility traffic. In this paper, a new adaptive AV2X model, based on a novel optimization method to enhance the connectivity of the vehicular networks, is proposed. The presented model optimizes the inter-vehicle position to communicate with the autonomous vehicle (AV) or to relay information to everything. Based on the system quality-of-service (QoS) being achieved, a decision will be taken whether the transmitting AV communicates directly to the destination or through cooperative communication. To achieve the given objectives, the best position of the relay-vehicle issue was mathematically formulated as a constrained optimization problem to enhance the communication between AV2X under different environmental conditions. To illustrate the effectiveness of the proposed model, the following factors are considered: distribution of vehicles, vehicle density, vehicle mobility and speed. Simulation results show how the proposed model outperforms other previous models and enhances system performance in terms of four benchmark aspects: throughput (S), packet loss rate (PLR), packet delivery ratio (PDR) and average delivery latency (DL). Full article
(This article belongs to the Special Issue Application of Mobile Systems in Smart Vehicles and Smart Roads)
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Review

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18 pages, 3058 KiB  
Review
A Scientometric-Based Review of Traffic Signal Control Methods and Experiments Based on Connected Vehicles and Floating Car Data (FCD)
by Vittorio Astarita, Vincenzo Pasquale Giofrè, Giuseppe Guido and Alessandro Vitale
Appl. Sci. 2021, 11(12), 5547; https://doi.org/10.3390/app11125547 - 15 Jun 2021
Cited by 1 | Viewed by 2377
Abstract
This paper reviews the state of the art in traffic signal control methods that are based on data coming from onboard smartphones or connected vehicles. The review of the state of the art is carried out by applying analytical scientometric tools (topic visualization, [...] Read more.
This paper reviews the state of the art in traffic signal control methods that are based on data coming from onboard smartphones or connected vehicles. The review of the state of the art is carried out by applying analytical scientometric tools (topic visualization, co-citation analysis to establish influential journals and references, country analysis based on coauthorship, trending-topics analysis carried out by overlay visualization). The introduction of autonomous and connected vehicles will allow city management organizations to introduce new intersection management systems that rely on real-time positional data coming from instrumented vehicles. Traditional vehicles also could benefit from these new technologies by profiting from better-regulated intersections. This paper using a scientometric approach frames all the scientific contributions aimed at the field of traffic signal methods and experiments based on connected vehicles and floating car data. The applied scientometric approach reveals trending ideas and concepts and identifies the relevant documents that can be consulted in order for scientists and professionals to develop further this field with the implementation of new traffic signal control systems that can “give the green light” to drivers. Full article
(This article belongs to the Special Issue Application of Mobile Systems in Smart Vehicles and Smart Roads)
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