TY  - EJOU
AU  - Zhang, Qun 
AU  - Mei, Yusheng 
AU  - Wang, Ziyue 
AU  - Liu, Yingzhe 
AU  - Zhao, Zhuofeng 

TI  - A Proximity-Aware Road Network–Integrated Trajectory Graph Neural Network for Road-Level Traffic Flow Prediction
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - Accurate road-level traffic flow prediction remains challenging in heterogeneous urban networks, where traffic interactions extend beyond direct road connectivity. Existing graph-based models can exploit road topology or vehicle trajectories but often overlook geometric continuity and dependencies between non-adjacent yet functionally related road segments. To address this gap, this study proposes a Proximity-Aware Road Network–Integrated Trajectory Graph Neural Network (PA-RNTrGNN). The model integrates trajectory-derived movement transitions, ordered road geometry, proximity-aware structural relations, and bidirectional traffic status information. Specifically, map-matched trajectories are converted into transition matrices to preserve directional movement causality; road geometries are encoded as ordered sequences using a unidirectional gated recurrent unit (GRU); and a proximity-aware structural matrix is constructed from distance decay, directional similarity, and road-type compatibility. Experiments on the Beijing, Chengdu, and Porto datasets yield overall mean absolute error (MAE) values of 4.491, 8.537, and 0.593. Compared with Multi-Spatio-Temporal Fusion Graph Recurrent Network (MSTFGRN), the best-performing baseline in terms of overall MAE, PA-RNTrGNN reduces MAE by 4.30%, 33.92%, and 2.15% on the three datasets, while also achieving the lowest mean peak-hour MAE. The largest improvement on Chengdu highlights the effectiveness of combining trajectory-driven propagation with proximity-aware structural modeling for large and heterogeneous road networks.
KW  - Traffic flow prediction; vehicle trajectories; proximity-aware constraints; graph neural networks; road geometry; spatiotemporal modeling

DO  - 10.32604/cmc.2026.088700