Simulation-Driven Vehicular Traffic Data Augmentation: Extending Sensor Coverage Through Virtual Sensing

2026-08-14

AI Summary

This study addresses the challenges of poor model generalization and high retraining costs caused by sparse urban traffic sensors by proposing a simulation-based virtual sensing data augmentation method. A graph search heuristic optimizes virtual sensor deployment under joint constraints of flow continuity, metric similarity, and spatial displacement diversity to expand monitoring coverage cost-effectively. Empirical evaluations in Brussels and Namur demonstrate that the augmented data effectively preserves bimodal demand patterns and traffic dynamics. Consequently, this approach significantly enhances model generalization under data-sparse conditions, offering a novel paradigm for mitigating reliance on physical infrastructure.

Abstract

Urban traffic management relies on sensor networks whose spatial coverage is limited by deployment costs and privacy regulations. Machine learning models trained on such sparse data cannot generalize to unmonitored locations and must be retrained whenever the sensor infrastructure changes. We propose a simulation-based methodology that addresses this problem by generating augmented traffic count datasets in which each physical sensor is replaced by a virtual sensor placed at a surrogate location in the road network. Virtual sensors are selected by a graph-search heuristic that jointly maximises vehicle-flow continuity and traffic-metric similarity between the original and surrogate locations, while enforcing a minimum spatial displacement to ensure diversity of observed traffic conditions. We validate the method on two Belgian cities: Brussels, using a calibrated model, and Namur, using synthetic models. The augmented datasets preserve the bimodal daily demand profile and the dynamics of traffic at the observed locations.

Problem

• Vehicular Traffic Data Augmentation
• Sensor Coverage
• Sparse Data
• Model Generalization

Innovation

• Simulation-Driven Data Augmentation
• Virtual Sensing
• Graph-Search Heuristic
• Sensor Coverage Extension
• Traffic Flow Continuity