Modern artificial intelligence (AI) and machine learning (ML) traffic prediction models provide accuracy, yet lack interpret-ability. Our approach designs a local neighborhood of segments and time-periods to construct a prediction data set that contain explicit dynamics across properties measured in the spatial temporal frame. The approach performs similarly to a popular TGCN ML model while providing opportunity to understand the relationship between predictor and predictand across the graph structure to which it is applied.
Traffic Speed Prediction Using Explicit Spatial Temporal Dynamics
2024-09-24
1133602 byte
Conference paper
Electronic Resource
English