Simulation-Free Estimation of Traffic Flows from Sparse Count Data
2026-06-22 • Machine Learning
Machine Learning
AI summaryⓘ
The authors created a way to guess how traffic moves over time using limited vehicle count data. They split the area into regions, guess possible routes between them, and use math to figure out how many cars take each route. Their method uses sensor data to focus on observable traffic flows and then matches routes to traffic patterns. When tested on Brussels roads, it closely matched real traffic and worked better and faster than older methods.
traffic flow estimationaggregated vehicle countsweighted least-squares optimizationsensor coverageroute allocationtraffic modelingregional partitioningBrussels road networksynthetic traffic dataflow reconstruction
Authors
Davide Guastella, Gianluca Bontempi
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.