Development, calibration, and validation of a large-scale traffic simulation model: Belgium network
summary
A probabilistic travel demand model generates hourly OD matrices from four inputs (city populations, intercity distances, yearly vehicle‑km, yearly truck trips) and yields 3.3 million daily trips across 60 selected cities (3,600 OD pairs); matrices import to SUMO mesoscopic + dynamic user equilibrium assignment, calibrated against 50 traffic counts (average GEH 4.9, >78% <7.5) and validated with Google Distance Matrix travel times (good R²); runs ~1 hour per DUE iteration; excludes cross‑border through trips and treats trucks like passenger cars.
AI-written summary · about
chapters (8)
- 0:00Introduction
- 0:36Motivation and objectives
- 1:30Network supply modeling
- 3:08Demand modeling approach
- 5:27Data processing and workflow
- 12:46Calibration process
- 15:11Validation and results
- 17:01Conclusion and future work
related talks
16mAutomated Calibration of Traffic Demand and Traffic Lights in SUMO Using Real-World Observations
16mOnline calibration with SUMO for network-wide traffic and emission monitoring - Case ITS Huainan
17mA comparison of SUMO’s count based and countless demand generation tools
13mUtilizing Vehicle Trajectory Data to Characterize Performance Measures of a Highway Corridors
16mTopology-Preserving Simplification of OpenStreetMap Network Data for Large-scale Simulation in SUMO
19mCycle-Level Distribution-Based Calibration of Microscopic Intersection Models Using UAV Trajectories
24mTowards Smarter Intersections Through Digital Twins - The Coupling of SUMO and SCENIMINI
16mTidy Transit: Real Life Data Modeling for Public Transportation