Dataset for Data-Driven Location–Allocation with Asymmetric Travel Distances in Urban Networks

This dataset supports the study of data-driven location–allocation problems under asymmetric travel distances in urban road networks.

The dataset combines real-world travel distance data with simulated demand locations for two urban regions, Bangkok and Guangzhou. Demand locations are generated within the corresponding planning regions to approximate realistic spatial distributions of users. The provided map images (Bangkok.png and Guangzhou.png) illustrate these planning regions and serve as the spatial reference for the experiments.

Travel distances are obtained via the Google Maps Distance Matrix API and exhibit inherent asymmetry due to traffic conditions and road network structure.

The dataset includes:
- Demand point data (data_*.csv): simulated geographic coordinates of 100 demand locations within the planning regions.
- Regional information (data_Bangkok_regions.csv): spatial partitioning used for the land cost analysis.
- Asymmetric dissimilarity matrices (dissimilarity_matrix_*.csv): pairwise travel distances between demand points, where d(i,j) ≠ d(j,i). Each matrix is aligned with the ordering of demand points in the corresponding data file.
- K-means evaluation data (KMeans-by-LatLng_Distances-To-Centers_*.json): precomputed travel distances between demand points and cluster centers. These are provided because K-means centers are not restricted to observed demand locations, and additional distance queries are required.
- Map images (Bangkok.png, Guangzhou.png): background maps representing the planning regions and serving as the spatial reference for visualization.
- Implementation script (main.ipynb): code for reproducing the experiments, including clustering procedures, synthetic asymmetric distance matrix generation, and objective value evaluation.

Travel distance data are obtained via the Google Maps Distance Matrix API. Due to usage restrictions associated with third-party map services, raw API responses are not included. However, all processed data, coordinates, and necessary procedures are provided to enable reproducibility.

The dataset supports research on asymmetric distance modeling, data-driven location–allocation, and clustering-based optimization in real-world urban environments.