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