Special Issue "Future Transportation of People and Goods"

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 February 2022.

Special Issue Editors

Prof. Dr. Giovanni Randazzo
E-Mail Website
Guest Editor
Department of Mathematic and Informatics Sciences, University of Messina, Messina, Italy
Interests: geography; geomorphology; geology
Dr. Anselme Muzirafuti
E-Mail Website
Guest Editor
Interreg Italia–Malta–Progetto: Pocket Beach Management and Remote Surveillance System, University of Messina, Via F. Stagno d’Alcontres, 31–98166 Messina, Italy
Interests: remote sensing; unmanned aerial vehicles (UAVs); image processing; farming by satellite; geographic information system (GIS); applied geophysics; coastal studies; climate change; land use/cover change; anthropogenic impact; landscape planning; engineering geology; ecological studies
Special Issues, Collections and Topics in MDPI journals
Dr. Dimitrios S. Paraforos
E-Mail Website
Guest Editor
Institute of Agricultural Engineering, University of Hohenheim, Garben Str. 9, 70599 Stuttgart, Germany
Interests: agricultural machinery automation; ISOBUS technologies; unmanned ground and aerial vehicles; decentralized and resilient digital farming systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Transportation of people and goods is operated in the air, on the land, on the sea, and recently to the edge of the space. Transportation relies on effective vehicles, good infrastructures, as well as good environmental conditions. Transportation contributes significantly to the ongoing changes in climate and is at the same time affected by the global crises which lead to global supply chain shutdowns. The changing climate is creating new waterways in the Arctic, triggering coastal erosion, and is affecting maritime traffic systems. In time, further exciting advancements are expected to emerge in the field of transportation as the USA and the EU are investing billions of USD and EUR, respectively, on new infrastructures, most of them connected with transportation. The challenge is to give the chance to the territories to start their own economies again and to create new systems of soft mobility for new generations of users. With the continuously evolving technologies, transportation of people and goods is becoming smart and intelligent. New autonomous and electrical vehicles are being produced, and government and private sector actors are producing and installing sensors and platforms aiming at providing crucial information on the state of the air, land, and sea. A number of satellites and unmanned aerial vehicle sensors provide crucial information on the state of maritime, land, and air conditions. On-ground sensors and cameras provide important information on the current state of the abovementioned environments. Future transportation will rely heavily on information provided by these sensors, platforms, and their connectivity to transport vehicles. It will greatly benefit from smooth flow of information between these platforms, sensors, and vehicles enabled by the internet and other kinds of modern communications. However, efficiency and the security of that flow and transfer of information is the most important aspect of future transportation of people and goods.

This Special Issue will focus on the latest advances in technologies aiming at future transportation of people and goods. Authors are invited to submit original manuscripts on topics including (but not limited to): 

  • Smart cities and smart logistics;
  • The environmental effects of transport;
  • Green transport;
  • Blockchain and the Internet of Things;
  • Artificial Intelligence and machine and deep learning for the mining of raw materials;
  • Bicycle sharing;
  • Photogrammetry for coastal areas, maritime and land monitoring;
  • New active and passive sensors for autonomous vehicles;
  • Geological mapping of nickel, copper, cobalt, and platinum;
  • Climate and energy technologies;
  • Evaluation of the impact of transportation on coastal cities and coastal morphological evolution;
  • New sensors and their applications for fault movements and seismicity analyses and sediment management;
  • Satellite technologies for transportation of people and goods;
  • New harbors, new bridges, and tunnel constructions;
  • Micromobility;
  • Bathymetry mapping of uncharted and hard-to-reach waters for marine transportation and sunk obsolete ships detection.

Prof. Dr. Giovanni Randazzo
Dr. Anselme Muzirafuti
Dr. Dimitrios S. Paraforos
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 papers will be 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 2000 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

  • satellite technologies
  • green transport
  • space tourism
  • automatic control
  • remote sensing
  • unmanned aerial vehicles (UAVs)
  • image processing
  • geographic information system (GIS)
  • railways
  • TGV
  • Electric Vehicles
  • Bicycle Sharing
  • 5G
  • Galileo
  • Copernicus Sentinel satellites
  • applied geophysics
  • climate change
  • land use/cover change
  • landscape planning
  • 2D imaging, 3D imaging
  • machine learning
  • deep learning
  • multispectral data analysis
  • hyperspectral data analysis
  • LIDAR data analysis
  • RADAR data analysis
  • coastal environment
  • tourism
  • aerial photogrammetry
  • automation and robotics
  • wireless sensor networks
  • Autonomous vehicles
  • traffic monitoring
  • drones
  • GPS
  • port shutdown
  • containers and shipping rates
  • coastal geomorphologist
  • bathymetry mapping
  • point cloud acquisition and analysis
  • environmental impact

Published Papers (5 papers)

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Research

Article
Identifying the Importance of Criteria for Passenger Choice of Sustainable Travel by Train Using ARTIW and IHAMCI Methods
Appl. Sci. 2021, 11(23), 11503; https://doi.org/10.3390/app112311503 - 04 Dec 2021
Viewed by 175
Abstract
Nowadays, travelers can use different modes of transport, and they usually choose the most suitable and reliable mode available. The choice of one mode of transport as an alternative to another is subjective. It is usually built upon passenger attitude toward the advantages [...] Read more.
Nowadays, travelers can use different modes of transport, and they usually choose the most suitable and reliable mode available. The choice of one mode of transport as an alternative to another is subjective. It is usually built upon passenger attitude toward the advantages and disadvantages of using a particular mode. This article proposes analytical methods for and research results on passenger choices for sustainable train journeys as an alternative to traveling by bus. The rank averages of all criteria and their normalized subjective weights were calculated with reference to new linear (ARTIW-L) and nonlinear (ARTIW-N) methods of average rank transformation into weight. A correlation between sub-criteria rank averages and normalized weights is presented, based on the minimum number of passengers required to be interviewed to provide reliable results. The average ranks assigned by passengers to the evaluation sub-criteria and their global weights were used for determining and describing the most and least important key criteria by applying the inverse hierarchy for assessment of main criteria importance (IHAMCI) method. The analysis shows that the most important key criterion belonged to the sub-criteria characterizing economy, while the less important key criteria included ride comfort. The least important key criteria described safety and environmental protection, whose normalized subjective overall weights were the lowest. Rail transport authorities and companies involved in transporting passengers can make this mode of transport more attractive to people by giving priority to improving the services they provide to passengers. Full article
(This article belongs to the Special Issue Future Transportation of People and Goods)
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Article
Semi-Markov Model of the System of Repairs and Preventive Replacements by Age of City Buses
Appl. Sci. 2021, 11(21), 10411; https://doi.org/10.3390/app112110411 - 05 Nov 2021
Viewed by 267
Abstract
The paper presents a mathematical model of the system of repairs and preventive replacements by age of city buses. The mathematical model was developed using the theory of semi-Markov processes. In the model developed, four types of city bus renewal processes are considered [...] Read more.
The paper presents a mathematical model of the system of repairs and preventive replacements by age of city buses. The mathematical model was developed using the theory of semi-Markov processes. In the model developed, four types of city bus renewal processes are considered and three types of corrective repairs and preventive replacement. Corrective repairs are considered in two types: minimal repairs (repairs carried out by the Technical Service units) and perfect repairs (repairs carried out at the stations of the Service Station). The models of restoration systems that use semi-Markov processes in which minimal repairs, perfect repairs, and preventive replacements by age, have been examined in the literature to a limited extent. The system under consideration is analysed from the point of view of two criteria: profit per time unit and availability of city buses to carry out the assigned transport tasks. Conditions of criterion functions’ extremum (maximum) existence were formulated for the adopted assumptions. The considerations presented in the paper are illustrated by exemplary results of calculations. Full article
(This article belongs to the Special Issue Future Transportation of People and Goods)
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Article
Long-Range Dependence and Multifractality of Ship Flow Sequences in Container Ports: A Comparison of Shanghai, Singapore, and Rotterdam
Appl. Sci. 2021, 11(21), 10378; https://doi.org/10.3390/app112110378 - 05 Nov 2021
Viewed by 460
Abstract
The prediction of ship traffic flow is an important fundamental preparation for layout and design of ports as well as management of ship navigation. However, until now, the temporal characteristics and accurate prediction of ship flow sequence in port are rarely studied. Therefore, [...] Read more.
The prediction of ship traffic flow is an important fundamental preparation for layout and design of ports as well as management of ship navigation. However, until now, the temporal characteristics and accurate prediction of ship flow sequence in port are rarely studied. Therefore, in this study, we investigated the presence of long-range dependence in container ship flow sequences using the Multifractal Detrended Fluctuation Analysis (MF-DFA). We considered three representative container ports in the world—including Shanghai, Singapore, and Rotterdam container ports—as the study sample, from 1 January 2013 to 31 December 2017. Empirical results suggested that the ship flow sequences are deviated from normal distribution, and the sequences with different time scales exhibited varying degrees of long-range dependence. Furthermore, the ship flow sequences possessed a multifractal nature, where the larger the time scale of ship flow time series, the stronger the multifractal characteristics are. The weekly ship flow sequence in the port of Singapore owned the highest degree of multifractality. Furthermore, the multifractality presented in the ship flow sequences of container ports are due to the correlation properties as well as the probability density function of the ship flow sequences. The study outlines the importance of adopting these features for an accurate modeling and prediction for maritime ship flow series. Full article
(This article belongs to the Special Issue Future Transportation of People and Goods)
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Article
Forecasting Taxi Demands Using Generative Adversarial Networks with Multi-Source Data
Appl. Sci. 2021, 11(20), 9675; https://doi.org/10.3390/app11209675 - 17 Oct 2021
Viewed by 333
Abstract
As a popular transportation mode in urban regions, taxis play an essential role in providing comfortable and convenient services for travelers. For the sake of tackling the imbalance between supply and demand, taxi demand forecasting can help drivers plan their routes and reduce [...] Read more.
As a popular transportation mode in urban regions, taxis play an essential role in providing comfortable and convenient services for travelers. For the sake of tackling the imbalance between supply and demand, taxi demand forecasting can help drivers plan their routes and reduce waiting time and oil pollution. This paper proposes a deep learning-based model for taxi demand forecasting with multi-source data using Generative Adversarial Networks. Firstly, main features were extracted from multi-source data, including GPS taxi data, road network data, weather data, and points of interest. Secondly, Generative Adversarial Network, comprised of the recurrent network model and the conventional network model, is adopted for fine-grained taxi demand forecasting. A comprehensive experiment is conducted based on a real-world dataset of the city of Wuhan, China. The experimental results showed that our model outperforms state-of-the-art prediction methods and validates the usefulness of our model. This paper provides insights into the temporal, spatial, and external factors in taxi demand-supply equilibrium based on the results. The findings can help policymakers alter the taxi supply and the taxi lease rents for periods and increase taxi profit. Full article
(This article belongs to the Special Issue Future Transportation of People and Goods)
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Article
An Improved Multiple Features and Machine Learning-Based Approach for Detecting Clickbait News on Social Networks
Appl. Sci. 2021, 11(20), 9487; https://doi.org/10.3390/app11209487 - 13 Oct 2021
Viewed by 725
Abstract
The widespread usage of social media has led to the increasing popularity of online advertisements, which have been accompanied by a disturbing spread of clickbait headlines. Clickbait dissatisfies users because the article content does not match their expectation. Detecting clickbait posts in online [...] Read more.
The widespread usage of social media has led to the increasing popularity of online advertisements, which have been accompanied by a disturbing spread of clickbait headlines. Clickbait dissatisfies users because the article content does not match their expectation. Detecting clickbait posts in online social networks is an important task to fight this issue. Clickbait posts use phrases that are mainly posted to attract a user’s attention in order to click onto a specific fake link/website. That means clickbait headlines utilize misleading titles, which could carry hidden important information from the target website. It is very difficult to recognize these clickbait headlines manually. Therefore, there is a need for an intelligent method to detect clickbait and fake advertisements on social networks. Several machine learning methods have been applied for this detection purpose. However, the obtained performance (accuracy) only reached 87% and still needs to be improved. In addition, most of the existing studies were conducted on English headlines and contents. Few studies focused specifically on detecting clickbait headlines in Arabic. Therefore, this study constructed the first Arabic clickbait headline news dataset and presents an improved multiple feature-based approach for detecting clickbait news on social networks in Arabic language. The proposed approach includes three main phases: data collection, data preparation, and machine learning model training and testing phases. The collected dataset included 54,893 Arabic news items from Twitter (after pre-processing). Among these news items, 23,981 were clickbait news (43.69%) and 30,912 were legitimate news (56.31%). This dataset was pre-processed and then the most important features were selected using the ANOVA F-test. Several machine learning (ML) methods were then applied with hyper-parameter tuning methods to ensure finding the optimal settings. Finally, the ML models were evaluated, and the overall performance is reported in this paper. The experimental results show that the Support Vector Machine (SVM) with the top 10% of ANOVA F-test features (user-based features (UFs) and content-based features (CFs)) obtained the best performance and achieved 92.16% of detection accuracy. Full article
(This article belongs to the Special Issue Future Transportation of People and Goods)
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