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Intelligent Transportation Systems for Sustainable Mobility
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Intelligent Transportation Systems for Sustainable Mobility

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 October 2025 | Viewed by 3119

Special Issue Editor

Dr. Nicholas E. Lownes

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269-3037, USA
Interests: public transportation systems; network design; simulation

Special Issue Information

Dear Colleagues,

Sustainable mobility requires safe, equitable, and adaptable multi-modal transportation systems. Automated vehicle technology, smart infrastructure, and connected systems impact these systems in both their design and operations in sometimes new and unexpected manners. Often lost in the discussion of vehicle automation and smart infrastructure is the human element—as ultimately new transportation technology will interact with humans. Facing the next generation of transportation engineers, researchers, and scientists is the challenge of designing and operating sustainable mobility systems that are safe, effective, and equitable.

This Special Issue seeks papers focusing on research investigating the safety impacts of new automated vehicle technology on all modes of travel, the equitable design and operation of multi-modal systems in the context of smart infrastructure and human impacts, and interactions with the next generation of sustainable mobility infrastructure.

Dr. Nicholas E. Lownes
Guest Editor

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

  • transportation safety
  • smart infrastructure
  • sustainability
  • automated vehicles
  • equitable design
  • multi-modal transportation

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Published Papers (3 papers)

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Research

24 pages, 2264 KB  
Article
Heuristic, Hybrid, and LLM-Assisted Heuristics for Container Yard Strategies Under Incomplete Information: A Simulation-Based Comparison
by Mateusz Zajac
Appl. Sci. 2025, 15(18), 10033; https://doi.org/10.3390/app151810033 - 14 Sep 2025
Abstract
Efficient container stacking is a critical factor for the performance of intermodal terminals. This study evaluates how classical, hybrid, and LLM-assisted heuristic stacking strategies perform when terminals operate under incomplete or uncertain schedule information. A simulation model of a 4 × 5 × [...] Read more.
Efficient container stacking is a critical factor for the performance of intermodal terminals. This study evaluates how classical, hybrid, and LLM-assisted heuristic stacking strategies perform when terminals operate under incomplete or uncertain schedule information. A simulation model of a 4 × 5 × 3 yard was developed, comparing three strategies: a layer-based rule (LAY), a hybrid heuristic (SVD), and an adaptive heuristic supported by a large language model (ChatGPT-4), rather than a full ML/RL model. Each scenario (0%, 25%, 50%, and 100% schedule visibility) was repeated 10 times with controlled random seeds. Results show that under full schedule information, the LLM-assisted strategy reduced relocations by up to 35% and crane operating time by 28% compared to deterministic methods. However, its performance degraded with partial visibility, sometimes falling behind the hybrid strategy, which remained more stable across scenarios. Standard deviations confirmed that differences between methods were statistically significant. The findings highlight both the potential and the limitations of LLM-assisted heuristics: they can outperform classical approaches in data-rich environments but may overreact to incomplete inputs without explicit data quality assessment. This study should therefore be regarded as a simulation-based proof-of-concept, with further validation on real operational data required to confirm its applicability. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems for Sustainable Mobility)
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27 pages, 5473 KB  
Article
Advanced Sensor Integration and AI Architectures for Next-Generation Traffic Navigation
by Cosmina-Mihaela Rosca, Adrian Stancu and Ionuț-Adrian Gortoescu
Appl. Sci. 2025, 15(8), 4301; https://doi.org/10.3390/app15084301 - 13 Apr 2025
Cited by 3 | Viewed by 1041
Abstract
Traffic congestion represents an urban challenge that authorities are trying to solve through various means. Current traffic management systems do not solve these challenges, which is why the research presents a new proposal for a traffic optimization system. The proposed solution integrates small-sized [...] Read more.
Traffic congestion represents an urban challenge that authorities are trying to solve through various means. Current traffic management systems do not solve these challenges, which is why the research presents a new proposal for a traffic optimization system. The proposed solution integrates small-sized equipment (ESP32 equipped with accelerometers, gyroscopes, and cameras), cloud-based AI services (Azure Content Safety), and a multi-parametric analytical framework for real-time navigation. The system uses the Traffic Optimization Algorithm (TOA) proposed by the authors to calculate the Global Route Quality Indicator (GRQIk). It associates each route with a value based on which the degree of optimality is estimated. GRQIk is calculated based on the distance traveled, traffic delays, estimated travel time, road safety, and the individual’s sensitivity. Real-time data are collected using ESP32, with a pothole detection threshold set at 0.8 rad/s. Through the TomTom API, four alternative routes are identified. The performance evaluation showed that GRQIk differentiates route quality, with scores ranging from 26.40% for optimal routes to 100% for the least favorable ones. In addition, Azure’s Content Safety API achieved 100% accuracy in identifying violent incidents and accidents. The limitations of the research concern the small number of images available to test the Content Safety service. The research establishes new approaches for future developments in the field of smart transportation. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems for Sustainable Mobility)
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15 pages, 4786 KB  
Article
Development of Integrated Driving Evaluation Index by Proportion of Autonomous Vehicles for Future Intelligent Transportation Systems
by Minkyung Kim, Hoseon Kim and Cheol Oh
Appl. Sci. 2024, 14(20), 9322; https://doi.org/10.3390/app14209322 - 13 Oct 2024
Viewed by 1408
Abstract
As the market penetration rate (MPR) of autonomous vehicles increases, it is expected that the safety of mixed traffic situations will change due to interactions between vehicles. A proactive safety analysis of mixed traffic situations is needed for future intelligent transportation systems; thus, [...] Read more.
As the market penetration rate (MPR) of autonomous vehicles increases, it is expected that the safety of mixed traffic situations will change due to interactions between vehicles. A proactive safety analysis of mixed traffic situations is needed for future intelligent transportation systems; thus, it is necessary to determine the driving safety evaluation indicators that have a significant impact on identifying hazardous sections of actual roads by each MPR. The purpose of this study is to simulate autonomous vehicle behavior by analyzing real-world autonomous vehicle data and to derive a promising integrated driving safety evaluation index for mixed traffic. Autonomous vehicle driving data from an autonomous mobility testbed in Seoul were collected and analyzed to assess autonomous vehicle behavior in VISSIM. The simulation environment was established to match the real road environment. Decision tree (DT) analysis was adopted to derive the indicators influencing the classification of hazardous sections of real roads by MPR. The vehicle–vehicle interaction indicators used to evaluate driving safety were applied as the input variables of the DT, and the classification of real-world hazardous road sections was the output variable. An integrated evaluation index was developed using the promising evaluation indicators and information gains derived for each MPR. The most hazardous section and the factors affecting the driving safety of the section based on the integrated evaluation index for each MPR were then presented. The results of this study can be utilized to proactively identify hazardous road sections in the real world through simulations of mixed traffic conditions. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems for Sustainable Mobility)
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