Harnessing floating car data, traffic camera observations, and network flow analysis for traffic volume estimation

Abstract

Cities increasingly rely on vehicle trajectory data to monitor traffic conditions; however, such data offer only a partial and spatially heterogeneous view of network dynamics and exhibit systematic biases across corridors and time periods. In contrast, surveillance cameras can provide high-fidelity traffic information, but only at a limited set of locations, typically sparsely distributed across the road network. We present a hybrid modeling and calibration framework that fuses these complementary data sources to produce physically consistent, network-wide estimates and short-horizon forecasts of traffic volumes. The framework leverages kinematic features derived from the Cell Transmission Model (CTM) formulation within a graph neural network (GNN). By enforcing traffic-flow conservation, capacity limits, and spillback dynamics, the CTM provides a physically grounded representation of traffic flow, while the GNN learns the spatiotemporal evolution of traffic states over the entire road network. To calibrate the model predictions on traffic camera observations, we use a progressive data-assimilation scheme based on an Ensemble Square-Root Kalman filter (EnSRF). A topology-informed flow-weighted transition matrix is further employed to propagate camera-driven corrections to unobserved road segments, enabling real-time, network-wide traffic state and volume estimation. The approach is demonstrated using probe-vehicle trajectory data and municipal traffic cameras in Manhattan, New York City, where it achieves improved accuracy relative to trajectory-based estimates while maintaining physically plausible and network-consistent traffic flows. The proposed framework accommodates varying sensor availability and produces calibrated traffic volumes with uncertainty estimates, supporting operational monitoring and evaluation of transportation policies in data-constrained urban environments.