arXiv AI July 28, 2026 By Abdullah Anjum, Abdolazim Rezaei, Mehdi Sookhak

MINT-V2X: A Mobility-Integrated Network Trajectory Dataset for Predictive Resource Management

arXiv:2607. 22654v1 Announce Type: new Abstract: Vehicle-to-Everything (V2X) communication systems are based on datasets that not only contain vehicle trajectory data but also wireless network parameters with a realistic level of fidelity, enabling the creation of prediction and optimization models.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 7

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking

arXiv:2603. 06607v2 Announce Type: replace-cross Abstract: Radio resource allocation (RRA) is a critical function in cellular vehicle-to-everything (C-V2X) networks, where vehicles must share limited wireless resources to support safety-critical communications.

By Siyuan Wang, Lei Lei, Pranav Maheshwari, Sam Bellefeuille, Kan Zheng

agents reinforcement-learning benchmarks safety

arXiv AI

Jun 2

CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation

arXiv:2606. 02287v1 Announce Type: cross Abstract: Urban trajectory generation is a fundamental task for transportation simulation, urban planning, and mobility analytics.

By Shibo Zhu, Xiaodan Shi, Dayin Chen, Yuntian Chen, Haoran Zhang, Tianhao Wu, Jinyue Yan

diffusion benchmarks

arXiv AI

Jul 21

A Multi-Agent System for 5G Throughput Prediction in Multi-Operator Urban Environments

arXiv:2607. 16930v1 Announce Type: cross Abstract: Throughput prediction is foundational for artificial intelligence-driven 6G resource orchestration.

By Muhammad Kabeer, Rosdiadee Nordin, Nadiva Nuriftitah, Sian Lun Lau

agents efficiency

arXiv AI

Jul 24

AI-Driven Multi-Hop Relay Selection for Smart Urban NR-V2X Networks via Learning-to-Optimize Graph Neural Networks

arXiv:2607. 20554v1 Announce Type: new Abstract: Reliable and low-latency NR-V2X communications are essential for smart mobility in dense urban environments.

By Giambattista Amati, Federica Mangiatordi, Simone Angelini, Emiliano Pallotti, Pierpaolo Salvo

efficiency

arXiv Machine Learning

Jun 9

Efficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depth

arXiv:2606. 09539v1 Announce Type: new Abstract: Spatio-temporal graph neural networks (STGNNs) have become the dominant approach for traffic prediction, yet their computational requirements pose challenges for practical deployment in intelligent transportation systems (ITS).

By Soban Nasir Lone, Mohamed Abouelela, Taeyoung Yu, Jiwon Kim, Constantinos Antoniou

efficiency benchmarks

arXiv Machine Learning

1d ago

Evaluating the impact of adversarial traffic patterns on vanet communication using veins simulation

arXiv:2608. 14583v1 Announce Type: cross Abstract: Vehicular Ad Hoc Networks (VANETs) are a key component of intelligent transportation systems, enabling real-time communication between vehicles.

By Henry Agyapong

efficiency safety

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Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking

CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation

A Multi-Agent System for 5G Throughput Prediction in Multi-Operator Urban Environments

AI-Driven Multi-Hop Relay Selection for Smart Urban NR-V2X Networks via Learning-to-Optimize Graph Neural Networks

Efficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depth

Evaluating the impact of adversarial traffic patterns on vanet communication using veins simulation