{"id":2142,"date":"2025-11-30T07:49:27","date_gmt":"2025-11-30T07:49:27","guid":{"rendered":"https:\/\/scipapermill.com\/index.php\/2025\/11\/30\/intelligent-transportation-navigating-the-future-with-ai-and-ml\/"},"modified":"2025-12-28T21:07:39","modified_gmt":"2025-12-28T21:07:39","slug":"intelligent-transportation-navigating-the-future-with-ai-and-ml","status":"publish","type":"post","link":"https:\/\/scipapermill.com\/index.php\/2025\/11\/30\/intelligent-transportation-navigating-the-future-with-ai-and-ml\/","title":{"rendered":"Intelligent Transportation: Navigating the Future with AI and ML"},"content":{"rendered":"

Latest 50 papers on transportation: Nov. 30, 2025<\/h3>\n

The world of transportation is undergoing a profound transformation, driven by rapid advancements in AI and Machine Learning. From predicting traffic jams and optimizing logistics to securing autonomous vehicles and even planning urban air mobility, AI is at the forefront of creating safer, more efficient, and sustainable transit systems. This digest delves into recent breakthroughs that are shaping the future of how we move, exploring innovations that span computer vision, intelligent decision-making, and robust system design.<\/p>\n

The Big Idea(s) & Core Innovations<\/h2>\n

At the heart of these advancements is the drive to make transportation systems smarter and more responsive. A significant theme is the leveraging of Large Language Models (LLMs) and Graph Neural Networks (GNNs) to handle complex, dynamic data. For instance, the paper TrafficLens: Multi-Camera Traffic Video Analysis Using LLMs<\/a> introduces TrafficLens<\/strong>, an innovative framework that uses LLMs to analyze multi-camera traffic video, showcasing their potential in understanding real-world traffic scenarios. Complementing this, Multi-Agent Multimodal Large Language Model Framework for Automated Interpretation of Fuel Efficiency Analytics in Public Transportation<\/a>, from researchers including Zhipeng Ma and Zheng Grace Ma from SDU Center for Energy Informatics, proposes a multi-agent LLM framework that automates the interpretation of fuel efficiency data in public transportation, significantly improving factual precision and scalability.<\/p>\n

Security and robustness are also paramount. The paper Robustness of LLM-enabled vehicle trajectory prediction under data security threats<\/a> by Feilong Wang (Southwest Jiaotong University) and Fuqiang Liu (McGill University) exposes vulnerabilities of LLM-based trajectory prediction systems to adversarial attacks, advocating for robustness-oriented design. Relatedly, Jailbreaking Large Vision Language Models in Intelligent Transportation Systems<\/a> investigates how LVLMs in ITS can be manipulated, underscoring the urgent need for robust security measures. Countering such threats, Trapped by Their Own Light: Deployable and Stealth Retroreflective Patch Attacks on Traffic Sign Recognition Systems<\/a> by researchers from Waseda University and University of California, Irvine, reveals a novel attack vector using retroreflective patches and proposes the DPR Shield<\/strong> as a defense mechanism, highlighting the ongoing arms race in automotive security.<\/p>\n

Beyond perception and security, optimized decision-making is evolving. For complex logistics, the paper Vehicle Routing Problems via Quantum Graph Attention Network Deep Reinforcement Learning<\/a> by T.G. Le et al.\u00a0introduces a quantum-enhanced deep reinforcement learning framework that reduces parameters by over 50% and improves routing performance by 5% for Vehicle Routing Problems (VRP), a groundbreaking step for logistics optimization. In urban air mobility, Exploring Urban Air Mobility Adoption Potential in San Francisco Bay Area Region: A Systems of Systems Level Case Study on Passenger Waiting Times and Travel Efficiency<\/a> by C. Y. Justin, A. P. Payan, and D. Mavris (NASA Langley Research Center, UC Berkeley, MIT) analyzes UAM adoption, emphasizing the potential to reduce ground traffic and the need for careful integration. For multi-robot coordination, Distributed Switching Model Predictive Control Meets Koopman Operator for Dynamic Obstacle Avoidance<\/a> by Bueno et al.\u00a0(Italian Ministry of Enterprises and Made in Italy) presents a framework for real-time, collision-free navigation of multiple UAVs in dynamic environments.<\/p>\n

Finally, enhancing user experience and data quality is critical for adoption. Three papers (Optimized User Experience for Labeling Systems for Predictive Maintenance Applications (Extended)<\/a> by M. Stern et al., Optimizing Predictive Maintenance: Enhanced AI and Backend Integration<\/a> by Stern, Hofmann et al., and Optimized User Experience for Labeling Systems for Predictive Maintenance Applications<\/a> by Hallmann et al.\u00a0from RailTech Innovation Lab and T\u00dcV Rheinland Consulting) collectively highlight the importance of intuitive labeling UIs and secure backend infrastructure, leveraging distributed ledger networks, for predictive maintenance in rail transport, significantly improving data annotation quality and reducing cognitive load for train drivers and workshop foremen.<\/p>\n

Under the Hood: Models, Datasets, & Benchmarks<\/h2>\n

This wave of research introduces and leverages several key tools and resources to push the boundaries of intelligent transportation:<\/p>\n