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Sustainable Transport: Logistic, Optimization, Traffic Flow and Road Safety

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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: 31 December 2026 | Viewed by 8363

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Special Issue Editors

Prof. Domenico Gattuso

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Guest Editor

Department DICEAM, Mediterranea University of Reggio Calabria, 89124 Reggio Calabria, Italy
Interests: logistics; traffic flow; railway; transport; optimization; SCM; transportation planning; transport management; transport modeling; transport planning; transport engineering; simulation; infrastructure development; railway scheduling; traffic engineering; road safety; ITS; urban planning; accident analysis; container shipping; pedestrian safety; containers; local segression analysis; public transport; transportation systems; sustainable mobility; network traffic simulation; urban transportation; intelligent transportation systems; traffic control

Dr. Giuseppe Guido

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Guest Editor

Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende, Italy
Interests: transportation engineering; road safety; smart cities; intelligent transportation systems; smart mobility; transit systems; mobility management; mobility modelling; users’ behavior modelling
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Special Issue Information

Dear Colleagues,

We believe that transportation systems play a vital role in achieving global sustainability goals, encompassing environmental protection, economic development, and social well-being. However, the exponential growth of global movements of goods and people, while offering numerous benefits, also brings challenges such as increasing greenhouse gas emissions, growing congestion levels, and road safety concerns. I see technological innovations as valuable tools to monitor the impacts of mobility, while a multidisciplinary approach can help develop strategies that integrate logistics flow planning, optimization methods, traffic flow management, and advanced safety measures. With this Special Issue, "Sustainable Transport: Logistics, Optimization, Traffic Flow, and Road Safety", I aim to create a platform to showcase cutting-edge research and practical applications that contribute to the development of sustainable and equitable transport solutions. I invite you to share your research and reviews on topics including, but not limited to, the following:

Optimization models for sustainable logistics and transport planning;
Approaches to mitigate environmental impacts by transport systems;
Traffic flow analysis and innovative management strategies;
Integration of green technologies and digital tools into transportation systems;
Data collection and analysis for improving transport efficiency, effectiveness, and sustainability;
Road safety improvement through innovative technologies or targeted policies;
Social equity in access to transportation infrastructure and services;
Insights into specific sectors facing unique logistical challenges, such as construction logistics, agri-food supply chains, fashion industry, tourism, etc.;
Exploration of innovative concepts, including solutions for the “15-minute city” or efficient last-mile logistics;
Integration of urban and transport planning to achieve sustainability goals;
Case studies providing original insights into sustainable transport practices.

By exploring these topics together, I hope to enhance our collective understanding of how sustainable transport systems can be implemented on various scales, from urban mobility to regional and global logistics networks. I look forward to reading your contributions and working together to shape the future of sustainable transportation.

Prof. Domenico Gattuso
Dr. Giuseppe Guido
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 250 words) can be sent to the Editorial Office for assessment.

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

sustainable transport
logistics optimization
traffic flow management
green technologies
road safety
energy efficiency
social equity in transportation
last-mile logistics

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Published Papers (5 papers)

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Research

29 pages, 5308 KB

Open AccessArticle

Recommender Systems for Multimodal Transportation in Smart Sustainable Cities

by Houda El Bouhissi, Thomas Hanne and Mounia Madadi

Sustainability 2025, 17(23), 10810; https://doi.org/10.3390/su172310810 - 2 Dec 2025

Viewed by 928

Abstract

Transportation recommendation systems (RS)s have garnered significant attention owing to their ongoing potential for enhancement. One of the key innovations in this domain is multimodal transportation RSs, which suggest travel routes using a combination of different transportation modes. In this paper, a multimodal transportation RS is introduced, which recommends optimized trajectories based on user preferences. The system involves two main steps, trajectory generation and ranking. In the first step, Particle Swarm Optimization (PSO) is used to find optimal trajectory combinations between the origin and destination, followed by post-processing. In the second step, the generated trajectory is evaluated using a RankNet model trained on historical user data with a content-based approach. The results demonstrate the system’s ability to generate feasible trajectories and provide precise recommendations. The results enable an efficient usage and convenient user experiences and may foster the broader use of public transportation combined with other transport modes addressing the objectives of smart and sustainable future cities. Full article

(This article belongs to the Special Issue Sustainable Transport: Logistic, Optimization, Traffic Flow and Road Safety)

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28 pages, 1074 KB

Open AccessArticle

Sustainable Mobility-as-a-Service: Integrating Spatial–Temporal Proximity and Environmental Performance in Transport Disruption Management

by Cecília Vale and Leonor Vale

Sustainability 2025, 17(23), 10686; https://doi.org/10.3390/su172310686 - 28 Nov 2025

Viewed by 841

Abstract

This paper investigates the integration of proximity theory (PT) into the management of public transport service disruptions within sustainable Mobility-as-a-Service (MaaS) systems, an area that is largely underexplored. PT provides a multidimensional framework for analyzing relationships and interactions within complex systems, encompassing five dimensions: geographical, cognitive, institutional, organizational, and social, each influencing coordination, learning, and adaptability. Building on this framework, the study introduces temporal proximity as an original sub-dimension of geographical proximity, forming a spatial–temporal proximity theory (PTST), which highlights the critical role of timing, synchronization, and coordinated responses in transport disruption management. To operationalize these principles, a mixed-integer programming (MIP) model was developed to optimize traveler assignments across 50 routes for 10 travelers, minimizing delays, transfers, walking distance, crowding, and CO₂ emissions. Two scenarios were analyzed: one without environmental considerations and another with CO₂ penalties. Results show that emissions were reduced by up to 50% for certain routes, while maintaining feasible travel times and route choices. The case study demonstrates that PTST can be operationalized as a practical tool, bridging mobility resilience and environmental responsibility, and providing actionable insights for sustainable and intelligent MaaS platforms. Full article

(This article belongs to the Special Issue Sustainable Transport: Logistic, Optimization, Traffic Flow and Road Safety)

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28 pages, 2038 KB

Open AccessArticle

Cognitive-Inspired Multimodal Learning Framework for Hazard Identification in Highway Construction with BIM–GIS Integration

by Jibiao Zhou, Zewei Li, Zhan Shi, Xinhua Mao and Chao Gao

Sustainability 2025, 17(21), 9395; https://doi.org/10.3390/su17219395 - 22 Oct 2025

Cited by 1 | Viewed by 1345

Abstract

Highway construction remains one of the most hazardous sectors in the infrastructure domain, where persistent accident rates challenge the vision of sustainable and safe development. Traditional hazard identification methods rely on manual inspections that are often slow, error-prone, and unable to cope with complex and dynamic site conditions. To address these limitations, this study develops a cognitive-inspired multimodal learning framework integrated with BIM–GIS-enabled digital twins to advance intelligent hazard identification and digital management for highway construction safety. The framework introduces three key innovations: a biologically grounded attention mechanism that simulates inspector search behavior, an adaptive multimodal fusion strategy that integrates visual, textual, and sensor information, and a closed-loop digital twin platform that synchronizes physical and virtual environments in real time. The system was validated across five highway construction projects over an 18-month period. Results show that the framework achieved a hazard detection accuracy of 91.7% with an average response time of 147 ms. Compared with conventional computer vision methods, accuracy improved by 18.2%, while gains over commercial safety systems reached 24.8%. Field deployment demonstrated a 34% reduction in accidents and a 42% increase in inspection efficiency, delivering a positive return on investment within 8.7 months. By linking predictive safety analytics with BIM–GIS semantics and site telemetry, the framework enhances construction safety, reduces delays and rework, and supports more resource-efficient, low-disruption project delivery, highlighting its potential as a sustainable pathway toward zero-accident highway construction. Full article

(This article belongs to the Special Issue Sustainable Transport: Logistic, Optimization, Traffic Flow and Road Safety)

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14 pages, 884 KB

Open AccessArticle

Evaluating the Safety and Cost-Effectiveness of Shoulder Rumble Strips and Road Lighting on Freeways in Saudi Arabia

by Saif Alarifi and Khalid Alkahtani

Sustainability 2025, 17(15), 6868; https://doi.org/10.3390/su17156868 - 29 Jul 2025

Viewed by 1574

Abstract

This study examines the safety and cost-effectiveness of implementing shoulder rumble strips (SRS) and road lighting on Saudi Arabian freeways, providing insights into their roles in fostering sustainable transport systems. By leveraging the Highway Safety Manual (HSM) framework, this research develops localized Crash Modification Factors (CMFs) for these interventions, ensuring evidence-based and context-specific evaluations. Data were collected for two periods—pre-pandemic (2017–2019) and post-pandemic (2021–2022). For each period, we obtained traffic crash records from the Saudi Highway Patrol database, traffic volume data from the Ministry of Transport and Logistic Services’ automated count stations, and roadway characteristics and pavement-condition metrics from the National Road Safety Center. The findings reveal that SRS reduces fatal and injury run-off-road crashes by 52.7% (CMF = 0.473) with a benefit–cost ratio of 14.12, highlighting their high cost-effectiveness. Road lighting, focused on nighttime crash reduction, decreases such crashes by 24% (CMF = 0.760), with a benefit–cost ratio of 1.25, although the adoption of solar-powered lighting systems offers potential for greater sustainability gains and a higher benefit–cost ratio. These interventions align with global sustainability goals by enhancing road safety, reducing the socio-economic burden of crashes, and promoting the integration of green technologies. This study not only provides actionable insights for achieving KSA Vision 2030’s target of improved road safety but also demonstrates how engineering solutions can be harmonized with sustainability objectives to advance equitable, efficient, and environmentally responsible transportation systems. Full article

(This article belongs to the Special Issue Sustainable Transport: Logistic, Optimization, Traffic Flow and Road Safety)

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18 pages, 3451 KB

Open AccessArticle

Integrating Neural Networks for Automated Video Analysis of Traffic Flow Routing and Composition at Intersections

by Maros Jakubec, Michal Cingel, Eva Lieskovská and Marek Drliciak

Sustainability 2025, 17(5), 2150; https://doi.org/10.3390/su17052150 - 2 Mar 2025

Cited by 6 | Viewed by 2715

Abstract

Traffic flow at intersections is influenced by spatial design, control methods, technical equipment, and traffic volume. This article focuses on detecting traffic flows at intersections using video recordings, employing a YOLO-based framework for automated analysis. We compare manual evaluation with machine processing to demonstrate the efficiency improvements in traffic engineering tasks through automated traffic data analysis. The output data include traditionally immeasurable parameters, such as speed and vehicle gaps within the observed intersection area. The traffic analysis incorporates findings from monitoring groups of vehicles, focusing on their formation and speed as they traverse the intersection. Our proposed system for monitoring and classifying traffic flow was implemented at a selected intersection in the city of Zilina, Slovak Republic, as part of a pilot study for this research initiative. Based on evaluations using local data, the YOLOv9c detection model achieved an mAP50 of 98.2% for vehicle localization and classification across three basic classes: passenger cars, trucks, and buses. Despite the high detection accuracy of the model, the automated annotations for vehicle entry and exit at the intersection showed varying levels of accuracy compared to manual evaluation. On average, the mean absolute error between annotations by traffic specialists and the automated framework for the most frequent class, passenger cars, was 2.73 across all directions at 15 min intervals. This indicates that approximately three passenger cars per 15 min interval were either undetected or misclassified. Full article

(This article belongs to the Special Issue Sustainable Transport: Logistic, Optimization, Traffic Flow and Road Safety)

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