TY  - Data
T1  - Grid-based Path Planning Simulation Dataset for Intelligent Transportation Networks
A1  - mo yuan qiu
PY  - 2026
DA  - 2026-07-09
PB  - National Cryosphere Desert Data Center
AB  - This dataset is designed for the performance evaluation of single-agent shortest path planning algorithms in intelligent transportation systems. Its generation background originates from path search and navigation problems of robots or vehicles in urban road networks under static obstacle environments. The dataset is constructed based on a grid‑based scanned map of a Parisian district (see R. Stern et al., "Multi-agent pathfinding: Definitions, variants, and benchmarks," in Proc. Int. Symp. Combinatorial Search, vol. 10, no. 1, 2019, pp. 151–158), using a 256 × 256 grid representation, where black regions denote building obstacles (impassable) and white regions denote traversable areas. Each grid point is treated as a network node. Nodes in traversable areas are connected to their eight adjacent neighbors in the directions of up, down, left, right, upper‑left, lower‑left, upper‑right, and lower‑right, forming movement paths, while obstacle nodes are isolated from the network. The resulting large‑scale path network contains 65,536 nodes and 354,938 edges, yielding a 65,536 × 8 sparse adjacency matrix data file named Paris_1_256_weight_nodirect.mat and its corresponding local directed graph matrix file Paris_1_256_weight_direct.mat, providing a high‑complexity scenario for algorithm testing. This dataset supports path planning experiments in both undirected and directed graph modes (by restricting neighbor directions of some nodes), and has been used to verify the convergence performance of various discrete‑time cuckoo search (DBMC) control strategies. The data content includes a complete grid map network topology (in GML or other formats) and clear start and end node identifiers (start node 65,374 and end node 1), which can effectively support research on shortest path solving capability, convergence speed, and robustness of path planning algorithms.
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/3fcf28f7-547c-4cfe-826e-3c6589802fff
ER  -