Simulation-Free Estimation of Traffic Flows from Sparse Count Data
For traffic engineers and urban planners, this method provides a computationally efficient way to estimate traffic flows from sparse sensor data, though it is an incremental improvement over existing optimization-based approaches.
The paper proposes a simulation-free method for estimating time-varying traffic flows from sparse count data, using a weighted least-squares optimization to allocate vehicles to routes. It outperforms baselines on the Brussels road network at a fraction of the computational cost.
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.