Calibrating Urban Traffic Simulation from Sparse Road Observations via Genetic Optimization

2026-06-02

AI Summary

Urban traffic simulation is often hindered by sparse road observation data and the lack of high-resolution employment distribution, making high-fidelity cross-city modeling challenging. This work proposes a genetic algorithm–based calibration framework that jointly optimizes employment distribution and origin–destination traffic parameters in SUMO using only limited road flow observations, without requiring detailed employment data. The approach achieves, for the first time, city-scale traffic simulation calibration relying solely on sparse observations, demonstrating strong generalization to unobserved road segments. Simulated traffic flows exhibit high correlation with real-world measurements, and the inferred employment distributions align qualitatively with census data, significantly enhancing the model’s practicality and transferability across urban contexts.

Abstract

Urban traffic simulation is a critical tool for infrastructure planning, including the placement of electric vehicle charging stations. However, realistic traffic simulation across many cities is hindered by two fundamental data limitations: detailed real-world traffic measurements are available for only a small fraction of road segments in most cities, and employment distribution data critical for modeling commuter traffic is rarely available at the resolution needed for simulation. This paper presents a genetic algorithm-based framework that directly addresses both limitations, calibrating urban traffic simulations from sparse road observations without requiring detailed job location data. Using the SUMO traffic simulation platform for Greensboro, North Carolina, our approach optimizes job distributions and gate-traffic parameters to align simulated traffic with a small sample of roads with known traffic-flow rates. We demonstrate that this approach produces simulated traffic that correlates well with real-world measurements, generalizes to road segments withheld from training, and produces job distributions that show promising qualitative agreement with census employment data despite never directly training on that employment data. This work demonstrates that realistic urban traffic simulation can be achieved from minimal real-world observations, offering a scalable and data-light approach to simulation calibration that reduces the barrier to deploying traffic models across diverse cities.

Problem

• traffic simulation
• sparse observations
• employment distribution
• calibration
• urban traffic

Innovation

• genetic algorithm
• traffic simulation calibration
• sparse observations
• employment distribution inference
• SUMO