IduEdu is an open-source Python library for building and analyzing multimodal city networks from OpenStreetMap and GTFS Schedule feeds.

Code style: black PyPI version Tests and Coverage Coverage License Docs GitHub

Features

  • UrbanGraph model: graph topology, geometry, CRS and weights are stored in explicit GeoDataFrame node and edge tables, with lazy CSR adjacency for numerical routines.

  • Street graph builders: get_drive_graph and get_walk_graph build OSM-based networks with local metric projection, travel-time weights and optional simplification.

  • Public transport from OSM: get_public_transport_graph builds static bus, trolleybus, tram, subway, monorail and share-taxi graphs directly from OSM route relations; trains are available through DEFAULT_REGISTRY_W_TRAIN.

  • Public transport from GTFS: get_gtfs_public_transport_graph reads a local GTFS Schedule directory or ZIP archive, or several feeds merged with merge_gtfs_feeds, and builds a static graph with timetable-derived boarding waits.

  • Intermodal graphs: get_intermodal_graph combines public transport and walk networks by projecting stops, platforms and subway access points onto pedestrian edges.

  • Matrices and shortest paths: od_matrix and Dijkstra helpers use Numba-backed CSR kernels, cutoff thresholds and adaptive graph reversal for large accessibility workloads. Ordered node routes can be converted back to their real edge geometries for map visualization.

  • Interoperability: optional NetworkX adapters are available for projects that need graph exchange or compatibility with older workflows.

See Benchmarks and design notes for the construction benchmark summary, raw-result locations and limitations of the static public-transport model.

Installation

pip install iduedu

Supports Python 3.11 through 3.14 and the geospatial stack (GeoPandas, Shapely, PyProj, NumPy, Pandas, SciPy, Numba). Install iduedu[io] to read and write .urbangraph archives. NetworkX is not installed with IduEdu; install it separately to use the optional NetworkX helpers.

Quickstart

1) Build an intermodal graph

from iduedu import get_intermodal_graph

# Define a territory (use OSM relation id or a shapely polygon/geodataframe)
G = get_intermodal_graph(osm_id=1114252)  # e.g., Saint Petersburg, Vasileostrovsky District

2) Compute an OD matrix (time or length)

import geopandas as gpd
from iduedu import od_matrix

# A "graph_node_id" column is used as is; without it, each geometry is matched to its nearest graph node
origins = gpd.GeoDataFrame({"graph_node_id": [...]}, geometry=[...], crs=G.crs)
destinations = gpd.GeoDataFrame({"graph_node_id": [...]}, geometry=[...], crs=G.crs)

M = od_matrix(
    G,
    gdf_origins=origins,
    gdf_destinations=destinations,
    weight="time_min",
    threshold=30,  # pairs without a path or beyond 30 minutes are inf
)
print(M.head())

3) Reconstruct a route

from iduedu import path_to_edges, single_source_dijkstra_path

nodes = G.nodes_gdf.index
path = single_source_dijkstra_path(G, nodes[0], nodes[-1], weight="time_min")  # [] if unreachable
route = path_to_edges(G, path)  # traversed edges in order, with geometry, type and time_min

print(route[["type", "length_meter", "time_min"]])

Configuration

IduEdu reads its settings from the global config object, and most of them also from environment variables. Use it to point requests at another Overpass instance, pace them, cache responses, query a historical OSM snapshot, choose the OSM tags kept on edges, or set up logging:

from iduedu import config

config.set_overpass_url("https://overpass-api.de/api/interpreter")
config.set_rate_limit(min_interval=2.0, max_retries=5)
config.set_overpass_cache(cache_dir=".iduedu_cache", enabled=True)
config.set_overpass_date(date="2020-01-01")  # query OSM as it stood on this date
config.configure_logging(level="INFO")

See the configuration guide for every setting and its environment variable.

Roadmap / Ideas

  • GTFS transfers and time-dependent timetable routing

  • Richer edge attributes (e.g., elevation, turn costs)

Contributions and ideas are welcome! Please open an issue or PR.

Contacts

Acknowledgments

Реализовано при финансовой поддержке Фонда поддержки проектов Национальной технологической инициативы в рамках реализации “дорожной карты” развития высокотехнологичного направления “Искусственный интеллект” на период до 2030 года (Договор № 70-2021-00187)

This research is financially supported by the Foundation for National Technology Initiative’s Projects Support as a part of the roadmap implementation for the development of the high-tech field of Artificial Intelligence for the period up to 2030 (agreement 70-2021-00187)

Citing IduEdu

If you use IduEdu in research, please cite it. Citation metadata is kept in CITATION.cff; on GitHub, Cite this repository in the repository sidebar copies it as APA or BibTeX. Publications describing IduEdu will be listed below.

Publications

Coming soon…