Synthetic Multimodal Drone Delivery Dataset

README: Synthetic Logistics Dataset Structure and Components

This dataset provides a structured representation of logistics data designed to evaluate and optimize hybrid truck-and-drone delivery networks. It captures a comprehensive set of parameters essential for modeling real-world logistics scenarios, including spatial coordinates, environmental conditions, and operational constraints. The data is meticulously organized into distinct keys, each representing a critical aspect of the delivery network, enabling researchers and practitioners to conduct flexible and in-depth analyses.  

The dataset is a curated subset derived from the research presented in the paper "Synthetic Dataset Generation for Optimizing Multimodal Drone Delivery Systems" by Altinsel et al. (2024), published in Drones. It serves as a practical resource for studying the interplay between ground-based and aerial delivery systems, with a focus on efficiency, environmental impact, and operational feasibility.  

Altinses, D., Torres, D. O. S., Gobachew, A. M., Lier, S., & Schwung, A. (2024). Synthetic Dataset Generation for Optimizing Multimodal Drone Delivery Systems. Drones (2504-446X), 8(12).

Each data file contains information on ten customer locations, specified by their x and y coordinates, which facilitate the modeling of delivery routes and service areas. Additionally, the dataset includes communication data represented as a two-dimensional grid, which can be used to assess signal strength, connectivity, or other network-related factors that influence drone operations.  

A key feature of this dataset is the inclusion of wind data, structured as a two-dimensional grid with four distinct features per grid point. These features likely represent wind velocity components (such as horizontal and vertical directions) along with auxiliary parameters like turbulence intensity or wind shear, which are crucial for drone path planning and energy consumption estimation. The wind data enables researchers to simulate realistic environmental conditions and evaluate their impact on drone performance, stability, and battery life.  

By integrating geospatial, environmental, and operational data, this dataset supports a wide range of applications, from route optimization and energy efficiency studies to risk assessment and resilience planning in multimodal delivery systems. Its synthetic nature ensures reproducibility while maintaining relevance to real-world logistics challenges, making it a valuable tool for advancing research in drone-assisted delivery networks.  

The 4 wind channels represent:

X and Y (Grid Positions)

These define where the arrows start (usually a meshgrid).

U and V (Arrow Directions)

U = Horizontal component (e.g., gradient in x).

V = Vertical component (e.g., gradient in y).

How to load the files using Python:

data = np.loadtxt('data.txt')

#### Just for Wind data:

data = data.reshape((4,16,16))