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Intelligent Transportation Systems and Sustainable Multimodal Transport

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

Deadline for manuscript submissions: 1 May 2026 | Viewed by 1191

Special Issue Editors


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Guest Editor
Centre for Research and Technology Hellas (CERTH)/Hellenic Institute of Transport (HIT), GR-57001 Thessaloniki, Greece
Interests: road safety; driving simulation; field trials; accessibility; smart cities; electrification; micromobility; CCAM; (C-)ITS; MaaS
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Center for Research and Technology Hellas, 57001 Thessaloniki, Greece
Interests: road safety; driving simulation; field trials; accessibility; CCAM; (C-)ITS; MaaS; embedded and integrated solutions; smart infrastructure
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Radical technological progress and increasing deployment of intelligent transportation systems have so far revealed the significant benefits of such technologies in terms of transport, society and industry. Despite significant recent advancements in interrelated fields—such as artificial intelligence (AI), big data, digital twins, connectivity and cooperative connected and automated mobility (CCAM) systems and embedded solutions addressing both vessels and infrastructure and aiming for safety and energy efficiency—there are still shortcomings and grounds for further research and innovation seeking to ensure sustainability in multimodal transport. These research approaches mainly tackle optimal cross-modal data integration and (cyber)security, resilient cloud-edge architectures with hybrid connectivity and leveraged AI for cooperative intelligent transport systems (ITSs) and remote operation in CCAM as well as other value added services—all enabled by digital twins and the digitalisation of infrastructure—for multimodal transport value chain actors and supporting monitoring, assessment and decision-making in multi-layered traffic management and efficiently managing different modes, vehicles and user cohorts.

This Special Issue covers different topics that address the most recent advancements in ITSs and sustainable multimodal transport, their validation and assessment and the analysis of their effects in application. Thus, the topics of interest for this Special Issue include, but are not limited to:

  1. AI and big data applications in (C-)ITSs and CCAM;
  2. Infrastructure digitalisation and value added services;
  3. Cross-mode data exchange;
  4. Cloud-edge and cybersecurity solutions.

Dr. Evangelos Bekiaris
Dr. Maria Gkemou
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. 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

  • artificial intelligence
  • digital twins
  • embedded solutions
  • digitalisation of infrastructure
  • cooperative connected automated driving
  • physical and digital infrastructure
  • sustainable multimodal transport

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Published Papers (1 paper)

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Research

15 pages, 395 KB  
Article
Multimodal Transport Optimization from Doorstep to Airport Using Mixed-Integer Linear Programming and Dynamic Programming
by Evangelos D. Spyrou, Vassilios Kappatos, Maria Gkemou and Evangelos Bekiaris
Sustainability 2025, 17(17), 7937; https://doi.org/10.3390/su17177937 - 3 Sep 2025
Viewed by 562
Abstract
Efficient multimodal transportation from a passenger’s doorstep to the airport is critical for ensuring timely arrivals, reducing travel uncertainty, and optimizing overall travel experience. However, coordinating different modes of transport—such as walking, public transit, ride-hailing, and private vehicles—poses significant challenges due to varying [...] Read more.
Efficient multimodal transportation from a passenger’s doorstep to the airport is critical for ensuring timely arrivals, reducing travel uncertainty, and optimizing overall travel experience. However, coordinating different modes of transport—such as walking, public transit, ride-hailing, and private vehicles—poses significant challenges due to varying schedules, traffic conditions, and transfer times. Traditional route planning methods often fail to account for real-time disruptions, leading to delays and inefficiencies. As air travel demand grows, optimizing these multimodal routes becomes increasingly important to minimize delays, improve passenger convenience, and enhance transport system resilience. To address this challenge, we propose an optimization framework combining Mixed-Integer Linear Programming (MILP) and Dynamic Programming (DP) to generate optimal travel routes from a passenger’s location to the airport gate. MILP is used to model and optimize multimodal trip decisions, considering time windows, cost constraints, and transfer dependencies. Meanwhile, DP allows for adaptive, real-time adjustments based on changing conditions such as traffic congestion, transit delays, and service availability. By integrating these two techniques, our approach ensures a robust, efficient, and scalable solution for multimodal transport routing, ultimately enhancing reliability and reducing travel time variability. The results demonstrate that the MILP solver converges within 20 iterations, reducing the objective value from 15.2 to 7.1 units with an optimality gap of 8.5%; the DP-based adaptation maintains feasibility under a 2 min disruption; and the multimodal analysis yields a total travel time of 9.0 min with a fare of 3.0 units, where the bus segment accounts for 6.5 min and 2.2 units of the total. In the multimodal transport evaluation, DP adaptation reduced cumulative delays by more than half after disruptions, while route selection demonstrated balanced trade-offs between cost and time across walking, bus, and train segments. Full article
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