Abstract: Deep-learning-based data-driven forecasting methods have achieved impressive results for traffic forecasting. Specifically, spatiotemporal graph neural networks have emerged as a promising ...

An interactive route planning project that models a city road network as a graph and finds optimized routes using core Data Structures and Algorithms concepts. The project supports shortest, fastest, ...

Abstract: Traffic flow prediction faces challenges in spatial relationship modeling and risk-aware external factor integration. Current graph-based methods typically rely on single adjacency matrices ...