GC-GRU-N for Traffic Prediction using Loop Detector Data

11/13/2022

∙

Because traffic characteristics display stochastic nonlinear spatiotemporal dependencies, traffic prediction is a challenging task. In this paper develop a graph convolution gated recurrent unit (GC GRU N) network to extract the essential Spatio temporal features. we use Seattle loop detector data aggregated over 15 minutes and reframe the problem through space and time. The model performance is compared o benchmark models; Historical Average, Long Short Term Memory (LSTM), and Transformers. The proposed model ranked second with the fastest inference time and a very close performance to first place (Transformers). Our model also achieves a running time that is six times faster than transformers. Finally, we present a comparative study of our model and the available benchmarks using metrics such as training time, inference time, MAPE, MAE and RMSE. Spatial and temporal aspects are also analyzed for each of the trained models.

READ FULL TEXT

GC-GRU-N for Traffic Prediction using Loop Detector Data

Understanding Dynamic Spatio-Temporal Contexts in Long Short-Term Memory for Road Traffic Speed Prediction

Deep Representation of Imbalanced Spatio-temporal Traffic Flow Data for Traffic Accident Detection

A deep learning approach to real-time parking occupancy prediction in spatio-termporal networks incorporating multiple spatio-temporal data sources

Temporal Fusion Transformers for Streamflow Prediction: Value of Combining Attention with Recurrence

High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

Newell's theory based feature transformations for spatio-temporal traffic prediction

Deep Spatiotemporal Models for Robust Proprioceptive Terrain Classification

GC-GRU-N for Traffic Prediction using Loop Detector Data

Related Research

Understanding Dynamic Spatio-Temporal Contexts in Long Short-Term Memory for Road Traffic Speed Prediction

Deep Representation of Imbalanced Spatio-temporal Traffic Flow Data for Traffic Accident Detection

A deep learning approach to real-time parking occupancy prediction in spatio-termporal networks incorporating multiple spatio-temporal data sources

Temporal Fusion Transformers for Streamflow Prediction: Value of Combining Attention with Recurrence

High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

Newell's theory based feature transformations for spatio-temporal traffic prediction

Deep Spatiotemporal Models for Robust Proprioceptive Terrain Classification