Simulation-Free Estimation of Traffic Flows from Sparse Count Data

2026-06-22

AI Summary

This study addresses the challenge of estimating time-varying traffic flows under sparse vehicle count data by proposing an efficient reconstruction method that eliminates the need for traffic simulation. The approach partitions the study area into subregions, constructs a feasible path set, and introduces a sensor coverage contribution matrix to guide a weighted least squares optimization for rational path flow allocation. By further integrating temporal and volumetric characteristics of regional counts, the method generates link-level trajectories. It substantially reduces computational overhead while improving estimation accuracy, successfully reproducing daily traffic patterns in real-world experiments on the Brussels road network and outperforming existing baseline methods.

Abstract

We propose a method for estimating time-varying traffic flow patterns from sparse aggregated vehicle counts. The method partitions the study area into spatial regions, constructs a set of feasible region-to-region routes, and solves a weighted least-squares optimization problem to determine the number of vehicles to allocate on each route. A weighted contribution matrix encodes sensor coverage, steering the optimizer toward flow configurations that are directly observable by sensors. Edge-level trajectories are then derived by scoring candidate routes against the temporal and volumetric profiles of aggregated regional sensor counts. The method is evaluated on the Brussels road network using real and synthetic traffic data. Results show that the proposed approach reproduces the daily traffic profile in the input data and outperforms the baseline methods at a fraction of the computational cost.

Problem

• traffic flow estimation
• sparse count data
• time-varying traffic patterns
• vehicle counts
• traffic monitoring

Innovation

• simulation-free estimation
• sparse count data
• weighted least-squares optimization
• traffic flow reconstruction
• sensor coverage encoding