A Spatial-Temporal Graph Convolutional Recurrent Network for Transportation Flow Estimation.

ABSTRACT: Accurate estimation of transportation flow is a challenging task in Intelligent Transportation Systems (ITS). Transporting data with dynamic spatial-temporal dependencies elevates transportation flow forecasting to a significant issue for operational planning, managing passenger flow, and arranging for individual travel in a smart city. The task is challenging due to the composite spatial dependency on transportation networks and the non-linear temporal dynamics with mobility conditions changing over time. To address these challenges, we propose a Spatial-Temporal Graph Convolutional Recurrent Network (ST-GCRN) that learns from both the spatial stations network data and time series of historical mobility changes in order to estimate transportation flow at a future time. The model is based

SUBMITTER: Drosouli I 

PROVIDER: S-EPMC10490678 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature