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Advances in Data-Driven Transportation Systems: Emerging Trends, Challenges, and Applications

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 692

Special Issue Editors

School of Transportation Engineering, Chang’an University, Xi'an, China
Interests: sustainable transportation systems; resilient transportation infrastructure network modeling and optimization; low-carbon transportation economy
School of Management, University of Bath, Bath, UK
Interests: sustainable transportation; network modelling; travel behaviour analysis
School of Traffic and Transportation Engineering, Central South University, Changsha, China
Interests: transportation; emergency management

Special Issue Information

Dear Colleagues,

We are excited to invite you to contribute to our upcoming Special Issue, titled ‘Advances in Data-Driven Transportation Systems: Emerging Trends, Challenges, and Applications’. This Special Issue aims to explore how emerging data-driven methodologies can promote the sustainable development of transportation systems.

In an era where digital technologies continuously reshape operational processes, understanding the implications, challenges, and opportunities presented by data-driven solutions in transportation is essential. This Special Issue seeks to gather original articles and research that investigate how big data analytics, intelligent transportation systems, and advanced modeling techniques transform transportation planning, operations, and policy-making.

The increasing availability of transportation data has opened new avenues for enhancing user experience, optimizing traffic management, and promoting sustainable mobility. We encourage the submission of articles that not only present theoretical and empirical research, but that also demonstrate the practical implementations of data-driven methods in real-world transportation contexts.

We are particularly interested in contributions that address, but are not limited to, the following topics:

  • Data analytics and predictive modeling for real-time traffic management;
  • Intelligent transportation systems (ITS) and smart city applications;
  • AI-driven approaches to optimize mobility and reduce congestion;
  • Sustainable and eco-friendly routing supported by data insights;
  • Safety, risk assessment, and incident detection using data-driven methods;
  • Integrating autonomous and connected vehicles into existing transportation networks;
  • Big data fusion techniques for multi-modal transportation planning;
  • Emerging technologies and innovations in public transport management;
  • Future trends and evolving challenges in data-centric mobility solutions.

This Special Issue aims to provide a platform for interdisciplinary dialog and foster new ideas and approaches that challenge traditional practices in transportation management. We welcome original research articles, comprehensive reviews, and case studies that contribute to the theoretical and methodological advancement of the field.

We look forward to receiving your insightful contributions and advancing the discourse in this exciting and rapidly developing area.

Dr. Yi Li
Dr. Meng Meng
Dr. Ziyue Yuan
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

  • low-carbon/resilient transportation systems
  • data-driven optimization
  • sustainable transport
  • emergency management
  • infrastructure management
  • network analysis

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Published Papers (1 paper)

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Research

23 pages, 8057 KiB  
Article
Strategies for Coordinated Merging of Vehicles at Ramps in New Hybrid Traffic Environments
by Zhizhen Liu, Xinyue Liu, Qile Li, Zhaolei Zhang, Chao Gao and Feng Tang
Sustainability 2025, 17(10), 4522; https://doi.org/10.3390/su17104522 - 15 May 2025
Viewed by 476
Abstract
With the advancement of autonomous driving technology, transportation systems are inevitably confronted with mixed traffic flows consisting of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Current research has predominantly focused on implementing homogeneous control strategies for ramp merging vehicles in such [...] Read more.
With the advancement of autonomous driving technology, transportation systems are inevitably confronted with mixed traffic flows consisting of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Current research has predominantly focused on implementing homogeneous control strategies for ramp merging vehicles in such scenarios, which, however, may result in the oversight of specific requirements in fine-grained traffic scenarios. Therefore, a classified cooperative merging strategy is proposed to address the challenges of microscopic decision-making in hybrid traffic environments where HDVs and CAVs coexist. The optimal cooperating vehicle on the mainline is first selected for the target ramp vehicle based on the principle of minimizing time differences. Three merging strategies—joint coordinated control, partial cooperation, and speed limit optimization—are then established according to the pairing type between the cooperating and ramp vehicles. Optimal deceleration and lane-changing decisions are implemented using the average speed change rate within the control area to achieve cooperative merging. Validation via a SUMO-based simulation platform demonstrates that the proposed strategy reduces fuel consumption by 6.32%, NOx emissions by 9.42%, CO2 emissions by 9.37%, and total delay by 32.15% compared to uncontrolled merging. These results confirm the effectiveness of the proposed strategy in mitigating energy consumption, emissions, and vehicle delays. Full article
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