Special Issue (SI): Applications of Artificial Intelligence in Transportation Systems

Short Abstract

With the rapid development of smart cities and intelligent transportation, artificial intelligence has become a core driving force for upgrading transportation systems. This special issue focuses on cutting-edge AI theories, algorithms and engineering applications in the transportation field, aiming to gather innovative achievements in traffic flow prediction, intelligent traffic control, connected and automated vehicles, multi-modal traffic optimization, traffic big data mining, urban computing and low-carbon intelligent transportation. It welcomes high-quality original research papers, review articles and technical communications addressing real-world challenges in complex traffic scenarios, such as dynamic perception, real-time decision-making, spatio-temporal data modeling and edge-cloud collaborative intelligence. By showcasing interdisciplinary advances between AI and transportation engineering, this issue seeks to promote theoretical innovation, technical breakthroughs and industrial implementation, providing theoretical support and practical solutions for building safer, more efficient, green and intelligent modern transportation systems.

 

Keywords

Artificial Intelligence; Intelligent Transportation Systems; Urban Computing; Traffic Flow Prediction; Connected Vehicles; Spatio-Temporal Data Mining; Smart Mobility

 

Research Background and Motivation

The rapid expansion of smart cities and intelligent transportation systems has elevated artificial intelligence to a central role in reshaping modern mobility. Accelerating urbanization continues to strain transportation infrastructure worldwide, leading to worsening congestion, rising energy demands, and persistent safety challenges. Conventional traffic management approaches, which rely heavily on static models and rule-based controls, are increasingly ill-equipped to handle the dynamic complexity and massive scale of contemporary transportation networks.

Recent advances in AI—including deep learning, reinforcement learning, graph neural networks, and spatio-temporal data mining—coupled with the widespread deployment of traffic sensors and edge-cloud platforms, have opened transformative opportunities. These technologies enable real-time traffic forecasting, adaptive signal optimization, coordinated operation of connected and automated vehicles, and intelligent multi-modal mobility management. The growing availability of large-scale heterogeneous data further empowers the development of self-learning, data-driven transportation systems that promise greater efficiency, safety, and environmental sustainability.

Despite notable progress, critical challenges persist. Robust perception under uncertain conditions, real-time decision-making in mixed traffic environments, scalable spatio-temporal modeling across city-scale networks, and privacy-aware collaborative intelligence between vehicles and infrastructure remain open research problems. Bridging the gap between theoretical AI innovations and practical engineering deployment demands interdisciplinary collaboration across computer science, control engineering, and transportation planning. This special issue aims to address these gaps by curating high-quality research at the intersection of AI and transportation engineering, advancing both fundamental understanding and actionable solutions for building the intelligent, resilient, and low-carbon transportation systems of the future.

  Necessity and Significance of the Special Issue

The integration of artificial intelligence into transportation systems is driving a fundamental shift toward safer, more efficient, and sustainable mobility. However, research remains fragmented across isolated domains such as traffic forecasting, adaptive control, and connected vehicles, with a persistent gap between theoretical advances and real-world deployment. A consolidated, peer-reviewed collection is urgently needed to unify these efforts and bridge the divide between AI innovation and engineering practice.

This special issue addresses this need by gathering original contributions spanning the full spectrum of AI applications in transportation—from foundational spatio-temporal modeling to applied multi-modal and low-carbon mobility solutions. It provides a timely, centralized reference for researchers and practitioners while fostering interdisciplinary collaboration across computer science, control engineering, and transportation planning. Ultimately, the issue will accelerate the development of intelligent, resilient, and environmentally responsible transportation systems.

 

Forward-Thinking Insights and Research Inspiration

The convergence of artificial intelligence and transportation systems is poised to redefine the future of mobility, yet the most transformative breakthroughs lie just beyond the current horizon. As AI models grow increasingly sophisticated, the next frontier will involve moving beyond predictive analytics toward autonomous, self-optimizing transportation ecosystems capable of real-time adaptation to evolving urban dynamics, environmental constraints, and human behavior. This shift demands novel architectures that seamlessly integrate edge-cloud collaborative intelligence, enabling low-latency decision-making across vast, heterogeneous networks of vehicles, infrastructure, and mobile devices.

Equally critical is the evolution of spatio-temporal modeling from static, data-driven correlations toward causally informed, physics-aware frameworks that can reason about complex interactions under uncertainty. Such models will unlock new possibilities for proactive traffic management, resilient infrastructure planning, and equitable access to mobility services. Furthermore, the imperative of sustainability calls for research that couples AI-driven efficiency gains with explicit carbon-awareness, embedding environmental objectives directly into optimization and control loops.

This special issue aspires to serve as both a milestone and a launchpad. By assembling pioneering work at the intersection of AI and transportation, it seeks to illuminate emerging trends, expose underexplored challenges, and inspire the research community to pursue bold, interdisciplinary inquiries. We hope the contributions collected herein will not only advance the state of the art but also catalyze the next generation of innovations essential for building truly intelligent, sustainable, and human-centric transportation systems.

 

Proposed Research Topics (include, but are not limited to)

1. AI-driven traffic flow prediction and anomaly detection

2. Deep learning and reinforcement learning for adaptive traffic signal control

3. Connected and automated vehicle coordination and cooperative driving

4. Spatio-temporal data mining and graph neural networks for urban computing

5. Multi-modal transportation optimization and smart mobility services

6. Edge-cloud collaborative intelligence for real-time transportation systems

7. Dynamic perception and decision-making in complex traffic environments

8. Big data analytics for transportation infrastructure monitoring and maintenance

9. AI-enabled low-carbon and sustainable transportation solutions

10. Digital twins and simulation for intelligent transportation systems

11. Privacy-preserving and secure AI in vehicular networks

12. Human-centric AI for travel behavior modeling and demand management

 

Guest Editors

Prof. Zhiqiang Lv

Affiliation: Qingdao University of Technology, China 

Scopus：https://www.scopus.com/authid/detail.uri?authorId=57219119913

ORCID：https://orcid.org/0000-0002-3071-160X

Email：lvzhiqiang@ubinet.cn

 

Timeline

Submission Opens: 20 April 2026

Submission Deadline: 20 April 2027

 

Editorial Process Timeline

Preliminary Review: 1 week

Peer Review: 4-6 weeks

Final Decision: 2 weeks

Authors are encouraged to submit their manuscripts through the journal's online submission system, adhering to the submission guidelines provided on the journal's website.

 

Submit Options

- OJS: https://ojs.wiserpub.com/index.php/EST/about/submissions

- Email: est@universalwiser.com

 

Contact for Inquiries

Prof. Zhiqiang Lv
Qingdao University of Technology, China
Email: lvzhiqiang@ubinet.cn

 

We look forward to your contributions to this endeavor!

 

Michelle Zu

Journal Coordinator

Email: est@universalwiser.com