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:

```bibtex
@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}
}