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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 6921

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Special Issue Editors

Dr. Maria Gkemou

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Guest Editor

Centre for Research and Technology Hellas (CERTH), Hellenic Institute of Transport (HIT), 57001 Thessaloniki, Greece
Interests: CCAM (connected cooperative automated transport); C-ITS (cooperative intelligent transportation systems); road safety; driving simulation; sustainable and clean mobility; training and accessibility in transport field

Dr. Evangelos Bekiaris

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Guest Editor

Center for Research and Technology Hellas/Hellenic Institute of Transport, CERTH/HIT, 6th Km Charilaou—Thermi Rd., Thermi, Thessaloniki, Macedonia, 57001 Hellas, Greece
Interests: reseach and innovation in all multimodal transport fields
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The current special issue focuses on emerging technologies/products and advances in (near) future generations of transportation systems that aim to enable and assess the robustness, sustainability, safety and security of multimodal transport chains of tomorrow.

The scope of the special issue is to descripe the readiness, robustness and user acceptance of technological solutions and products that exploit the advancements in cooperative intelligent transport systems (C-ITS), cooperative, connected and automated transport mobility (CCAM) technological domains as well as Mobility as a Service operational and business paradigms.

Novel approaches demonstrating progress beyond State of the Art specifically in use of Physical and Digital (and Communication) Infrastructure, Artificial Intelligence as well as augmented and mixed reality in order to tackle in specific safety, trust and security, sustainability and resilience of multimodal road surface transport chains are of high interest.

Solutions addressing one or more of the backbone actors of the cooperative multimodal transport network - namely the vehicles, the (other) road users, the mobility operators, the transport authorities and the applicable participating mobility service providers - are applicable.

The purpose of the special issue is to reveal, by evidence, how close we are to a safe, trusted and sustainable multimodal road surface transport system.

Dr. Maria Gkemou
Dr. Evangelos Bekiaris
Guest Editors

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. Sustainability 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

C-ITS
CCAM
MaaS
risk assessment
traffic safety
traffic efficiency, environmental footprint
multimodal
PDI
AI
mixed reality
augmented reality

Published Papers (6 papers)

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Research

13 pages, 1438 KiB

Open AccessArticle

The Analysis and AI Simulation of Passenger Flows in an Airport Terminal: A Decision-Making Tool

by Afroditi Anagnostopoulou, Dimitrios Tolikas, Evangelos Spyrou, Attila Akac and Vassilios Kappatos

Sustainability 2024, 16(3), 1346; https://doi.org/10.3390/su16031346 - 05 Feb 2024

Viewed by 1101

Abstract

In this paper, a decision-making tool is proposed that can utilize different strategies to deal with passenger flows in airport terminals. A simulation model has been developed to investigate these strategies, which can be updated and modified based on the current requirements of an airport terminal. The proposed tool could help airport managers and relevant decision makers proactively mitigate potential risks and evaluate crowd management strategies. The aim is to eliminate risk factors due to overcrowding and minimize passenger waiting times within the terminal to provide a seamless, safe and satisfying travel experience. Overcrowding in certain areas of the terminal makes it difficult for passengers to move freely and increases the risk of accidents (especially in the event of an emergency), security problems and service interruptions. In addition, long queues can lead to frustration among passengers and increase potential conflicts or stress-related incidents. Based on the derived results, the optimized routing of passengers using modern technological solutions is the most promising crowd management strategy for a sample airport that can handle 800 passengers per hour. Full article

(This article belongs to the Special Issue Emerging Technologies/Products and Advances in Future Transportation Systems)

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26 pages, 18757 KiB

Open AccessArticle

Multi-Layered Local Dynamic Map for a Connected and Automated In-Vehicle System

by Sebastiano Taddei, Filippo Visintainer, Filippo Stoffella and Francesco Biral

Sustainability 2024, 16(3), 1306; https://doi.org/10.3390/su16031306 - 03 Feb 2024

Cited by 1 | Viewed by 691

Abstract

Automated Driving (AD) has been receiving considerable attention from industry, the public, and researchers for its ability to reduce accidents, emissions, and congestion. The purpose of this study is to extend the standardized Local Dynamic Map (LDM) by adding two new layers, and develop efficient and accurate algorithms designed to enhance AD by exploiting the LDM coupled with Cooperative Perception (CP). The LDM is implemented as a Neo4j graph database and extends the standard four-layer structure by adding a detection layer and a prediction layer. A custom Application Programming Interface (API) manages all incoming data, generates the LDM, and runs the algorithms. Currently, the API can match detected entities coming from different sources, correctly position them on the map even in the presence of high uncertainties in the data, and predict their future actions. We tested the developed LDM with real-world data, which we collected using a prototype vehicle from Centro Ricerche FIAT (CRF) Trento Branch—the supporting research center for this work—in urban, suburban, and highway areas of Trento, Italy. The results show that the developed solution is capable of accurately matching and predicting detected entities, is robust to high uncertainties in the data, and is efficient, achieving real-time performance in all scenarios. From these results we can conclude that the LDM and CP have the potential to be core parts of AD, bringing improvements to the development process. Full article

(This article belongs to the Special Issue Emerging Technologies/Products and Advances in Future Transportation Systems)

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16 pages, 6913 KiB

Open AccessEditor’s ChoiceArticle

Safety Performance Assessment via Virtual Simulation of V2X Warning Triggers to Cyclists with Models Created from Real-World Testing

by Lars Schories, Nico Dahringer, Udo Piram, Anay Raut, Stella Nikolaou, Ioannis Gragkopoulos, Ioannis Tsetsinas and Maria Panou

Sustainability 2024, 16(2), 610; https://doi.org/10.3390/su16020610 - 10 Jan 2024

Viewed by 745

Abstract

The overall crash statistics in the EU still show a very significant number of car–cyclist crashes. Within the Horizon 2020 project Safe-Up, countermeasures have been developed to reduce this number. One of these countermeasures involves a V2X-enhanced on-board unit for cycles, which can provide on-time warning triggers. The research assumption was based on studying the benefits of connectivity in enhancing cyclists’ safety. This study assessed the performance of this potential technology both qualitatively by analyzing volunteer feedback during physical testing and quantitatively by virtual simulations. The volunteers’ study showed positive findings on system’s safety relevance, user experience, and user acceptance. The method applied for the virtual simulation is a prospective safety performance assessment with reconstructed accident scenarios based on the GIDAS database and cyclist behavior models, obtained from physical testing. The results using a warning trigger 4 s prior to the collision showed a potential safety benefit of approximately 98%. It should be noted that this trigger time was found to be quite early in both physical testing and virtual simulation. Further research is required to evaluate the system’s performance in more complex urban scenarios, as well as to design the human–machine interaction strategies for optimal accident avoidance. Full article

(This article belongs to the Special Issue Emerging Technologies/Products and Advances in Future Transportation Systems)

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31 pages, 2323 KiB

Open AccessArticle

Assessing Impact Factors That Affect School Mobility Utilizing a Machine Learning Approach

by Stylianos Kolidakis, Kornilia Maria Kotoula, George Botzoris, Petros Fotios Kamberi and Dimitrios Skoutas

Sustainability 2024, 16(2), 588; https://doi.org/10.3390/su16020588 - 09 Jan 2024

Viewed by 781

Abstract

The analysis and modeling of parameters influencing parents’ decisions regarding school travel mode choice have perennially been a subject of interest. Concurrently, the evolution of artificial intelligence (AI) can effectively contribute to generating reliable predictions across various topics. This paper begins with a comprehensive literature review on classical models for predicting school travel mode choice, as well as the diverse applications of AI methods, with a particular focus on transportation. Building upon a published questionnaire survey in the city of Thessaloniki (Greece) and the conducted analysis and exploration of factors shaping the parental framework for school travel mode choice, this study takes a step further: the authors evaluate and propose a machine learning (ML) classification model, utilizing the pre-recorded parental perceptions, beliefs, and attitudes as inputs to predict the choice between motorized or non-motorized school travel. The impact of potential changes in the input values of the ML classification model is also assessed. Therefore, the enhancement of the sense of safety and security in the school route, the adoption of a more active lifestyle by parents, the widening of acceptance of public transportation, etc., are simulated and the impact on the parental choice ratio between non-motorized and motorized school commuting is quantified. Full article

(This article belongs to the Special Issue Emerging Technologies/Products and Advances in Future Transportation Systems)

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23 pages, 4532 KiB

Open AccessArticle

Machine Learning Insights on Driving Behaviour Dynamics among Germany, Belgium, and UK Drivers

by Stella Roussou, Thodoris Garefalakis, Eva Michelaraki, Tom Brijs and George Yannis

Sustainability 2024, 16(2), 518; https://doi.org/10.3390/su16020518 - 07 Jan 2024

Viewed by 1041

Abstract

The i-DREAMS project has a core objective: to establish a comprehensive framework that defines, develops, and validates a context-aware ‘Safety Tolerance Zone’ (STZ). This zone is crucial for maintaining drivers within safe operational boundaries. The primary focus of this research is to conduct a detailed comparison between two machine learning approaches: long short-term memory networks and shallow neural networks. The goal is to evaluate the safety levels of participants as they engage in natural driving experiences within the i-DREAMS on-road field trials. To accomplish this objective, the study gathered a series of trips from a sample group consisting of 30 German drivers, 43 Belgian drivers, and 26 drivers from the United Kingdom. These trips were then input into the aforementioned machine learning methods to reveal the factors contributing to unsafe driving behaviour across various experiment stages. The results obtained highlight the significant positive impact of i-DREAMS’ real-time interventions and post-trip assessments on enhancing driving behaviour. Furthermore, it is worth noting that neural networks demonstrated superior performance compared to other algorithms considered within this research context. Full article

(This article belongs to the Special Issue Emerging Technologies/Products and Advances in Future Transportation Systems)

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22 pages, 1895 KiB

Open AccessArticle

Examining ICT Innovation for Sustainable Terminal Operations in Developing Countries: A Case Study of the Port of Radès in Tunisia

by Ahmed Sahraoui, Nguyen Khoi Tran, Youssef Tliche, Ameni Kacem and Atour Taghipour

Sustainability 2023, 15(11), 9123; https://doi.org/10.3390/su15119123 - 05 Jun 2023

Cited by 1 | Viewed by 1632

Abstract

There is a lack of technology innovation studies in the maritime sector focusing on developing countries. Generally, these countries present various limitations due to their own social, economic, and political contexts. Moreover, the lack of leadership support, stakeholder involvement, training, resources, and financial and academic support affects successful implementation of technological innovation. The objective of this paper is to emphasize the implementation of Information and Communication Technology (ICT) in the maritime sector and port companies of developing countries by investigating the impact of an ICT solution on port operations from berth to gate through yard operations. Our case study consists of the implementation of a Terminal Operating System (TOS) in the Port of Radès, the main port in Tunisia. An examination of the port operations before and after the implementation of the TOS is carried out. Then, the effects of TOS implementation on terminal operations are studied through a survey based on Key Performance Indicators (KPI) and submitted to managers of three port stakeholders. Key findings indicate that TOS allows an increase in the level of productivity from the quay crane to the gate, allowing decisions to be made based on real-time data and ensuring that the terminal is operating at its full potential. More specifically, berthing and delivery service times are improved thanks to the Electronic Data Interchange (EDI) and the streamlining of the gate and yard activities system. The results also indicate that reputation is progressively improving due to the ability to locate and monitor hazardous goods flowing through the port, and the ability to dispatch engine movement inside the port using the new terminal layout. However, in contrast with the port authority, the results highlight a lack of adaptability on the part of the stevedoring company, which requires time to progressively adapt to the new rules and constraints. Full article

(This article belongs to the Special Issue Emerging Technologies/Products and Advances in Future Transportation Systems)

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