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Algorithms
Special Issues
Models and Technologies for Intelligent Transportation Systems
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Models and Technologies for Intelligent Transportation Systems

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (29 February 2020) | Viewed by 12556

Special Issue Editors

Assist. Prof. Dr. Rafał Kucharski

E-Mail Website
Guest Editor
Department of Transportation Systems, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, Poland
Interests: dynamic traffic assignment methods
Dr. Francesco Viti

E-Mail Website
Guest Editor
MobiLab Transport Research Group, Research Unit of Engineering Science, University of Luxembourg, 6, Avenue de la Fonte, Esch-sur-Alzette, L-4364 Luxembourg, Luxembourg
Interests: decision support systems for travelers and transport operators; intelligent transport systems; network modeling and control
Prof. Dr. Guido Gentile

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Civile Edile e Ambientale (DICEA), Università degli Studi di Roma “La Sapienza”, via Eudossiana 18, 00184 Rome, Italy
Interests: intelligent transport systems; assignment models and algorithms; network project; simulation of collective transport systems; demand models

Special Issue Information

Dear Colleagues,

Please visit https://mt-its2019.pk.edu.pl/ for a detailed description of this Special Issue. The Special Issue will mainly consist of selected papers presented at the “6th International Conference on Models and Technologies for Intelligent Transportation Systems”. Papers that are found to fit the scope of the journal and to be of sufficient quality after evaluation by the reviewers will be published free of charge. The main topics of this Special Issue are:

  • ITS-oriented traffic planning, operations and management;
  • Demand modeling and travel behavior under ITS;
  • Model calibration, simulation, and tools for ITS;
  • Case studies and assessment of ITS applications;
  • Future mobility data collection for passenger and freight;
  • Real-time traffic control, management and short-term predictions;
  • ITS, multimodal transportation, and freight systems;
  • ITS and Big Data;
  • Plug-in electric vehicles and impacts on mobility;
  • Vehicle-to-X: Vehicle (V2V), Infrastructure (V2I), and Grids (V2G);
  • Current issues in transportation energy and climate change;
  • Automated and intelligent vehicles;
  • Communication in ITS;
  • Infrastructure design, safety, and ITS;
  • Rail operations and management;
  • ITS and Smart Cities;
  •  Electromobility.

Assist. Prof. Dr. Rafał Kucharski
Assoc. Prof. Dr. Francesco Viti
Prof. Dr. Guido Gentile
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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.

Published Papers (3 papers)

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Research

16 pages, 3596 KiB  
Article
Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach
by Filippo Giammaria Praticò, Rosario Fedele, Vitalii Naumov and Tomas Sauer
Algorithms 2020, 13(4), 81; https://doi.org/10.3390/a13040081 - 31 Mar 2020
Cited by 40 | Viewed by 4434
Abstract
The current methods that aim at monitoring the structural health status (SHS) of road pavements allow detecting surface defects and failures. This notwithstanding, there is a lack of methods and systems that are able to identify concealed cracks (particularly, bottom-up cracks) and monitor [...] Read more.
The current methods that aim at monitoring the structural health status (SHS) of road pavements allow detecting surface defects and failures. This notwithstanding, there is a lack of methods and systems that are able to identify concealed cracks (particularly, bottom-up cracks) and monitor their growth over time. For this reason, the objective of this study is to set up a supervised machine learning (ML)-based method for the identification and classification of the SHS of a differently cracked road pavement based on its vibro-acoustic signature. The method aims at collecting these signatures (using acoustic-sensors, located at the roadside) and classifying the pavement’s SHS through ML models. Different ML classifiers (i.e., multilayer perceptron, MLP, convolutional neural network, CNN, random forest classifier, RFC, and support vector classifier, SVC) were used and compared. Results show the possibility of associating with great accuracy (i.e., MLP = 91.8%, CNN = 95.6%, RFC = 91.0%, and SVC = 99.1%) a specific vibro-acoustic signature to a differently cracked road pavement. These results are encouraging and represent the bases for the application of the proposed method in real contexts, such as monitoring roads and bridges using wireless sensor networks, which is the target of future studies. Full article
(This article belongs to the Special Issue Models and Technologies for Intelligent Transportation Systems)
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18 pages, 1911 KiB  
Article
Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting
by Dmitry Pavlyuk
Algorithms 2020, 13(2), 39; https://doi.org/10.3390/a13020039 - 13 Feb 2020
Cited by 3 | Viewed by 4194
Abstract
Transfer learning is a modern concept that focuses on the application of ideas, models, and algorithms, developed in one applied area, for solving a similar problem in another area. In this paper, we identify links between methodologies in two fields: video prediction and [...] Read more.
Transfer learning is a modern concept that focuses on the application of ideas, models, and algorithms, developed in one applied area, for solving a similar problem in another area. In this paper, we identify links between methodologies in two fields: video prediction and spatiotemporal traffic forecasting. The similarities of the video stream and citywide traffic data structures are discovered and analogues between historical development and modern states of the methodologies are presented and discussed. The idea of transferring video prediction models to the urban traffic forecasting domain is validated using a large real-world traffic data set. The list of transferred techniques includes spatial filtering by predefined kernels in combination with time series models and spectral graph convolutional artificial neural networks. The obtained models’ forecasting performance is compared to the baseline traffic forecasting models: non-spatial time series models and spatially regularized vector autoregression models. We conclude that the application of video prediction models and algorithms for urban traffic forecasting is effective both in terms of observed forecasting accuracy and development, and training efforts. Finally, we discuss problems and obstacles of transferring methodologies and present potential directions for further research. Full article
(This article belongs to the Special Issue Models and Technologies for Intelligent Transportation Systems)
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12 pages, 872 KiB  
Article
Exploring Travelers’ Characteristics Affecting their Intention to Shift to Bike-Sharing Systems due to a Sophisticated Mobile App
by Andreas Nikiforiadis, Katerina Chrysostomou and Georgia Aifadopoulou
Algorithms 2019, 12(12), 264; https://doi.org/10.3390/a12120264 - 7 Dec 2019
Cited by 11 | Viewed by 3399
Abstract
Many cities have already installed bike-sharing systems for several years now, but especially in recent years with the rise of micro-mobility, many efforts are being made worldwide to improve the operation of these systems. Technology has an essential role to play in the [...] Read more.
Many cities have already installed bike-sharing systems for several years now, but especially in recent years with the rise of micro-mobility, many efforts are being made worldwide to improve the operation of these systems. Technology has an essential role to play in the success of micro-mobility schemes, including bike-sharing systems. In this paper, it is examined if a state-of-the-art mobile application (app) can contribute to increasing the usage levels of such a system. It is also seeking to identify groups of travelers, who are more likely to be affected by the sophisticated app. With this aim, a questionnaire survey was designed and addressed to the users of the bike-sharing system of the city of Thessaloniki, Greece, as well as to other residents of the city. Through a descriptive analysis, the most useful services that an app can provide are identified. Most importantly, two different types of predictive models (i.e., classification tree and binary logit model) were applied in order to identify groups of users who are more likely to shift to or to use the bike-sharing system due to the sophisticated app. The results of the two predictive models confirm that people of younger ages and those who are not currently users of the system are those most likely to be attracted to the system due to such an app. Other factors, such as car usage frequency, education, and income also appeared to have slight impact on travelers’ intention to use the system more often due to the app. Full article
(This article belongs to the Special Issue Models and Technologies for Intelligent Transportation Systems)
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