2D Dubai SUMO Simulation Dataset
Real traffic simulation of the Al Wasl / Dubai World Trade Centre corridor (approx. 25.202β25.228Β° N, 55.248β55.282Β° E).
Files
| File | Description | Size |
|---|---|---|
dubai_al_wasl.osm |
Source OSM XML exported via Overpass API (65k+ elements) | 6.3 MB |
dubai_al_wasl.net.xml |
SUMO road network (junctions, lanes, signals) | 27 KB |
polygons.xml |
Building + water + commercial footprints | 2.6 MB |
trips.trips.xml |
Randomized traffic demand (600 veh/h, seed=42) | 55 KB |
routes.rou.xml |
Shortest-path routes assigned by duarouter | 73 KB |
dubai_al_wasl.sumocfg |
SUMO configuration (entry point) | 765 B |
Usage
# Install SUMO (Ubuntu)
sudo add-apt-repository -y ppa:sumo/stable
sudo apt-get update && sudo apt-get install -y sumo sumo-tools sumo-doc
# Headless
sumo -c dubai_al_wasl.sumocfg --no-warnings
# GUI
sumo-gui -c dubai_al_wasl.sumocfg
# Monitor Trackio dashboard
python3 scripts/monitor.py --project 2dsumo --run default
trackio show --project 2dsumo
Build pipeline
OSM (65k+ elements)
β curl + Overpass API + User-Agent header
β netconvert --osm.layer-elevation 5 --junctions.join --tls.default-type static
Net (_sumo/dubai_al_wasl.net.xml)
β polyconvert + osmPolyconvert.typ.xml
Polygons (_polyconvert/polygons.xml)
β python3 /usr/share/sumo/tools/randomTrips.py --seed 42 --period 6.0
Trips (_sumo/trips.trips.xml)
β duarouter --routing-algorithm astar --remove-loops
Routes (_sumo/routes.rou.xml)
β
.sumocfg (entry point for sumo-gui)
Build details
- Epoch range: 0 β 1800 s (30 minutes simulated, verified headless)
- Seed: 42
- Inter-departure period: 6.0 s (~600 veh/h)
- Min trip distance: 80 m
- Fringe factor: 5
- Class mix (default): passenger, bus, motorcycle, truck
- TLS strategy: Static, default-type set during
--tls.default-type staticin netconvert; individual phases inheriting SUMO defaults - Routing algorithm: A* (
--routing-algorithm astar)
Source
Companion code, CI, docs: thtahamid/2dsumo
License
ODbL (from OpenStreetMap).
Generated by ML Intern
This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.
- Try ML Intern: https://smolagents-ml-intern.hf.space
- Source code: https://github.com/huggingface/ml-intern
Inference Providers NEW
This model isn't deployed by any Inference Provider. π Ask for provider support