Year: 2025
Type: article
Abstract: Predicting traffic flow with high accuracy is crucial for enhancing the performance of urban road networks, alleviating congestion, and boosting transportation efficiency. This study investigates advanced deep learning approaches, such as Long Short-Term Memory (LSTM), Graph Neural Networks (GNN), and Transformer-based architectures, to predict traffic flow in urban environments. By utilizing historical traffic records, weather information, and live sensor data, the models effectively learn and represent intricate spatial-temporal relationships in traffic patterns. Experiments on benchmark datasets show that deep learning models surpass traditional statistical approaches in prediction accuracy and scalability. These results underscore the capability of advanced neural network architectures to deliver valuable insights for smart city traffic management.
Author: Yilin Han
Institution: Northwest University
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,500
OpenAlex ID: W4409003763