Conditional Deep Learning for Urban Origin–Destination (OD) Matrix Estimation Under Varying Connected-Vehicle Penetration

Connected vehicles and vehicle-to-everything (V2X) communication create new opportunities for estimating urban origin–destination (OD) demand from continuously collected mobility data. However, in realistic deployment conditions, only a fraction of vehicles may be connected, making OD reconstruction a highly underdetermined problem under low penetration rates. This paper proposes a supervised deep-learning framework that reconstructs full OD matrices from synthetic connected-vehicle data in a simulated Manhattan network from New York City. Vehicle movement information is aggregated into intra-zonal and adjacent-zone traffic counts using K-means traffic analysis zones. These partial connected-vehicle observations, represented by the zonal movement matrix, outgoing and incoming zonal-movement summaries, diagonal movement counts, and penetration-rate features, form a compact input to a conditional Multi-Layer Perceptron (MLP) that predicts the complete OD matrix of all vehicles. The training objective separates OD spatial shape from total traffic volume and adds losses on marginals, diagonal elements, and log-space reconstruction to embed basic flow-conservation properties. A single conditional MLP is trained across multiple connected-vehicle penetration-rate scenarios by appending the penetration rate ρ and log(ρ) to the input representation. The model is evaluated over ten random connected-vehicle sampling seeds. Results show that the proposed estimator remains stable down to 20% penetration, with test sMAPE increasing only from 20.70±0.00% at full penetration to 21.71±0.38% at 20% penetration. Marginal and total-flow errors increase more gradually as penetration decreases, while clear degradation appears below approximately 2–1% penetration. Baseline and ablation comparisons further show that penetration-rate conditioning and the conservation-aware loss are essential for improving OD reconstruction and total-flow consistency. Within the evaluated simulated Manhattan scenarios, these findings suggest the potential of conditional neural estimators for OD reconstruction under limited connected-vehicle penetration. Validation across longer periods, additional demand regimes, and real-world data is required before the results can be generalized to broader urban traffic conditions.