README.md

🚗 MINT-V2X

Mobility-Integrated Network Trajectory Dataset for V2X Systems

MINT-V2X (Mobility-Integrated Network Trajectory Dataset for V2X Systems) is a large-scale dataset designed for research on vehicle-to-everything (V2X) communication, mobility-aware networking, and intelligent transportation systems (ITS).

The dataset integrates vehicle trajectory dynamics with wireless network measurements, enabling predictive modeling of vehicle mobility, network quality, and RSU resource demand.

Unlike existing datasets that provide either vehicle trajectories or network statistics independently, MINT-V2X provides synchronized mobility and communication data, allowing researchers to investigate the interactions between vehicle movement, wireless channel quality, and network load.

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📊 Dataset Overview

MINT-V2X contains nearly 10 million synchronized records generated from a realistic urban V2X simulation environment.

📌 Property	📈 Value
🚙 Vehicles	1,386
🗃️ Total Records	9,873,977
⏱️ Simulation Duration	3 hours
⚡ Sampling Rate	10 Hz (100 ms)
🔢 Features per Record	29
🗺️ Spatial Coverage	61.19 km²
📡 RSU Deployment	15 roadside units (5 × 3 grid)

Each record corresponds to a vehicle–timestep observation, capturing both the vehicle mobility state and its corresponding wireless network conditions.

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🧩 Dataset Features

Each data sample contains 29 features organized into four main categories.

🚘 1. Vehicle Trajectory

• Position (x, y, z)
• Velocity
• Acceleration
• Heading angle
• Lane identifier

📡 2. Network State

• RSU association (Cell ID)
• Distance to RSU
• Neighbor vehicle count
• Signal-to-Interference-plus-Noise Ratio (SINR)
• Received signal power

📶 3. Physical Layer Metrics

• Channel Quality Indicator (CQI)
• Modulation and Coding Scheme (MCS)
• Packet Delivery Ratio (PDR)
• Channel Busy Ratio (CBR)

🔄 4. Communication Performance

• Throughput
• End-to-end latency
• Handover events

💡 These features enable joint modeling of vehicle mobility and wireless network performance, rather than analyzing mobility and communication as independent processes.

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⚙️ Dataset Generation Pipeline

MINT-V2X is generated through a three-layer co-simulation architecture:

🚦 Traffic Simulation — SUMO

Generates realistic vehicle trajectories using urban traffic models.

SUMO → Vehicle mobility and traffic dynamics

🔗 Middleware — Veins

Synchronizes the traffic and communication simulators through the TraCI interface.

Veins → Mobility–network synchronization

📡 Network Simulation — OMNeT++ / Simu5G

Simulates V2X wireless communication and computes network metrics including:

• SINR
• CQI
• PDR
• Throughput
• Latency

OMNeT++ / Simu5G → Wireless communication and network performance

🔄 Overall Pipeline

SUMO ➜ Veins / TraCI ➜ OMNeT++ / Simu5G ➜ MINT-V2X Dataset

All measurements are recorded at a 10 Hz temporal resolution, providing precise synchronization between vehicle mobility and network states.

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✅ Validation Framework

MINT-V2X was evaluated using a 14-point validation framework based on established communication standards and theoretical principles:

• 📘 3GPP C-V2X standards
• 📡 ETSI congestion control specifications
• 📐 Shannon information theory

Key Validation Results

Validation Metric	Result
📶 SINR Range	−5 dB to +25 dB
🔗 CQI–SINR Correlation	0.993
🔗 SINR–PDR Correlation	0.946
🌐 Connectivity Ratio	99.78%

✅ These results confirm that the simulated network measurements exhibit physically consistent wireless communication behavior.

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🔬 Research Applications

MINT-V2X supports research across vehicular networking, V2X communication, machine learning, and intelligent transportation systems.

Potential applications include:

• 🚗 Vehicle trajectory prediction
• 📈 Mobility-aware network traffic prediction
• 📡 RSU load forecasting
• 🔄 Handover optimization
• ⚡ Proactive resource allocation
• 🤖 Multi-agent V2X communication modeling
• 🧠 Mobility–communication co-learning models

🎯 Mobility-Aware Resource Prediction

A key application of MINT-V2X is mobility-aware RSU resource prediction, where future network demand can be estimated from predicted vehicle trajectories.

This enables models to move from reactive resource management toward predictive and proactive V2X resource allocation.

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📚 Citation

If you use MINT-V2X in your research, please cite:

@misc{anjum2026mintv2xmobilityintegratednetworktrajectory,
      title={MINT-V2X: A Mobility-Integrated Network Trajectory Dataset for Predictive Resource Management},
      author={Abdullah Anjum and Abdolazim Rezaei and Mehdi Sookhak},
      year={2026},
      eprint={2607.22654},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2607.22654}
}

If you are also interested in the adjunct research associated with this study, particularly its application to network traffic prediction and dynamic urban congestion modeling, please consider citing:

@misc{rezaei2026parameterefficienthybridtransformer,
      title={Parameter Efficient Hybrid Transformer (PEHT) for Network Traffic Prediction via Dynamic Urban Congestion Integration},
      author={Abdolazim Rezaei and Mehdi Sookhak and Mahboobeh Haghparast},
      year={2026},
      eprint={2606.28274},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2606.28274}
}