Network-Wide Freeway Speed Nowcasting and Forecasting from Sparse Fixed Sensors with a Probe-Supervised Graph Convolutional Recurrent Network

Key points

• Traffic agencies need current and near-future speeds on every link of their network, but their fixed sensors cover only part of it.1
• We study joint nowcasting and next-hour forecasting of hourly speeds on all 4{,}127 directional freeway links of the Dallas--Fort Worth region from sensors on 643 links (15.6%).1
• It combines a parameter-free, direction-aware kernel interpolation of sensor anomalies, used as input features and as an output baseline, with a diffusion-convolutional GRU on the directed road graph, a low-rank adaptive graph and learnable link embeddings.1
• On four held-out test days, GCRN+ attains an RMSE of 2.21 mph for the current hour and 2.39 mph for the next hour on unsensored links, against 3.23 mph for the historical average.1

Sources (1)

• [1]Network-Wide Freeway Speed Nowcasting and Forecasting from Sparse Fixed Sensors with a Probe-Supervised Graph Convolutional Recurrent Network
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 08:35 PM

  We propose GCRN+, a graph convolutional recurrent network that predicts deviations from a historical speed profile.

  Traffic agencies need current and near-future speeds on every link of their network, but their fixed sensors cover only part of it.

Extractive summary: sentences quoted from the sources.