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Applied Sciences
Special Issues
Connected and Automated Mobility for Future Transportation
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Connected and Automated Mobility for Future Transportation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1659

Special Issue Editors

Dr. Fernando García

E-Mail Website
Guest Editor
Autonomous Mobile & Perception Lab AMPL, Universidad Carlos III de Madrid, 28911 Madrid, Spain
Interests: autonomous vehicles; computer vision; human machine interface
Dr. Abdulla Al-Kaff

E-Mail Website
Guest Editor
Autonomous Mobile & Perception Lab AMPL, Universidad Carlos III de Madrid, 28911 Madrid, Spain
Interests: autonomous vehicles; computer vision; drones
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the field of transportation has been undergoing a transformative shift with the rapid advancement of connected and automated mobility technologies. These technologies, collectively referred to as CAM (Connected and Automated Mobility), hold the potential to revolutionize the way people and goods are moved within our urban and interurban landscapes. As we stand at the crossroads of technological innovation and transportation evolution, there is a growing need to comprehensively explore, understand, and address the multifaceted implications of CAM for the future of transportation.

The "Connected and Automated Mobility for Future Transportation" Special Issue aims to delve into the complex interplay between cutting-edge technologies, urban planning, policy frameworks, societal impacts, and the overall mobility ecosystem. This Special Issue serves as a platform to gather and disseminate research, insights, and critical analyses that shed light on the various dimensions of CAM and its potential to reshape transportation systems.

This Special Issue will publish high-quality, original research papers on the following topics:

  • Communication-based solutions: including vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I).
  • Autonomous driving solutions: from perception technologies to advanced control techniques.
  • Human-Centered Mobility: CAM has implications not only for vehicles and infrastructure but also for the people who use them. Researchers in this area will investigate user experiences, behavioral shifts, and societal acceptance of automated vehicles, addressing questions related to trust, comfort, and user interfaces.
  • Environmental and Energy Impacts: The environmental benefits of CAM, such as reduced congestion and improved traffic flow, need to be weighed against potential downsides, such as increased energy consumption due to data processing. This theme explores the net environmental impact of CAM and strategies for minimizing negative effects.
  • Economic and Business Models: CAM is poised to disrupt traditional business models within the transportation sector. Articles in this theme will explore the potential economic opportunities, challenges, and shifts in business paradigms brought about by CAM technologies and services.
  • Safety and Security: Ensuring the safety and security of automated vehicles and their interactions is paramount. This theme examines the cybersecurity risks associated with CAM, as well as strategies for safeguarding vehicles and infrastructure from potential threats.
  • Use case and practical examples. As an evolving technology, CAM is bringing a new paradigm that should be tested. This topic aims to provide information about these new business opportunities or practical applications of these new technologies.

By addressing these key themes, the "Connected and Automated Mobility for Future Transportation" Special Issue seeks to provide a comprehensive understanding of the ongoing CAM revolution and its implications for the future of transportation. Researchers, policymakers, industry stakeholders, and urban planners will find valuable insights within this collection of articles to guide their decisions and actions in the evolving landscape of connected and automated mobility.

Dr. Fernando García
Dr. Abdulla Al-Kaff
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. 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

  • autonomous vehicles
  • smart control
  • perception in automated driving
  • computer vision
  • deep learning
  • vehicle to infrastructure (V2i)
  • vehicle to vehicle (V2V)

Published Papers (2 papers)

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Research

33 pages, 8924 KiB  
Article
Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo Bike Fleets
by Malte Kania, Vasu Dev Mukku, Karen Kastner and Tom Assmann
Appl. Sci. 2024, 14(1), 180; https://doi.org/10.3390/app14010180 - 25 Dec 2023
Viewed by 578
Abstract
Bike sharing systems have become a sustainable alternative to motorized private transport in urban areas. However, users often face high costs and availability issues due to the operational effort required to redistribute bicycles between stations. For addressing those issues, the AuRa (Autonomes Rad, [...] Read more.
Bike sharing systems have become a sustainable alternative to motorized private transport in urban areas. However, users often face high costs and availability issues due to the operational effort required to redistribute bicycles between stations. For addressing those issues, the AuRa (Autonomes Rad, Eng. Autonomous Bicycle) project introduces a new mobility offer in terms of an on-demand, shared-use, self-driving cargo bikes service (OSABS) that enables automated redistribution. Within the project, we develop different order management and rebalancing strategies and validate them using simulation models. One prerequisite for this is sound demand scenarios. However, due to the novelty of OSABS, there is currently no information about its utilization. Consequently, the objective of this study was to develop an approach for defining OSABS demand scenarios in a temporally and spatially disaggregated manner as an input for simulation models. Therefore, we first derived city-wide usage potentials of OSABS from a survey on mobility needs. We then spatially and temporally disaggregated the determined usage likelihood using travel demand matrices and usage patterns from a conventional bike-sharing system, respectively. Finally, we performed cluster analyses on the resulting annual demand to summarize sections of the yearly profile into representative units and thus reduce the simulation effort. As we applied this approach as a case study to the city of Magdeburg, Germany, we could show that our methodology enables the determination of reasonable OSABS demand scenarios from scratch. Furthermore, we were able to show that annual usage patterns of (conventional) bike sharing systems can be modeled by using demand data for only eight representative weeks. Full article
(This article belongs to the Special Issue Connected and Automated Mobility for Future Transportation)
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21 pages, 2581 KiB  
Article
Exploring the Evolution of Autonomous Vehicle Acceptance through Hands-On Demonstrations
by Rodrigo Encinar, Ángel Madridano, Miguel Ángel de Miguel, Martín Palos, Fernando García and John Bolte
Appl. Sci. 2023, 13(23), 12822; https://doi.org/10.3390/app132312822 - 29 Nov 2023
Viewed by 873
Abstract
This article delves into the acceptance of autonomous driving within society and its implications for the automotive insurance sector. The research encompasses two different studies conducted with meticulous analysis. The first study involves over 600 participants involved with the automotive industry who have [...] Read more.
This article delves into the acceptance of autonomous driving within society and its implications for the automotive insurance sector. The research encompasses two different studies conducted with meticulous analysis. The first study involves over 600 participants involved with the automotive industry who have not yet had the opportunity to experience autonomous driving technology. It primarily centers on the adaptation of insurance products to align with the imminent implementation of this technology. The second study is directed at individuals who have had the opportunity to test an autonomous driving platform first-hand. Specifically, it examines users’ experiences after conducting test drives on public roads using an autonomous research platform jointly developed by MAPFRE, Universidad Carlos III de Madrid, and Universidad Politécnica de Madrid. The study conducted demonstrates that the user acceptance of autonomous driving technology significantly increases after firsthand experience with a real autonomous car. This finding underscores the importance of bringing autonomous driving technology closer to end-users in order to improve societal perception. Furthermore, the results provide valuable insights for industry stakeholders seeking to navigate the market as autonomous driving technology slowly becomes an integral part of commercial vehicles. The findings reveal that a substantial majority (96% of the surveyed individuals) believe that autonomous vehicles will still require insurance. Additionally, 90% of respondents express the opinion that policies for autonomous vehicles should be as affordable or even cheaper than those for traditional vehicles. This suggests that people may not be fully aware of the significant costs associated with the systems enabling autonomous driving when considering their insurance needs, which puts the spotlight back on the importance of bringing this technology closer to the general public. Full article
(This article belongs to the Special Issue Connected and Automated Mobility for Future Transportation)
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