Work

BiLSTM‐ and GNN‐Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data

Year: 2023

Type: article

Abstract: The timely and accurate forecasting of urban road traffic is crucial for smart city traffic management and control. It can assist both drivers and traffic controllers in selecting efficient routes and diverting traffic to less congested roads. However, estimating traffic volume while taking into account external factors such as weather and accidents is still a challenge. In this research, we propose a hybrid deep learning framework, double attention graph neural network BiLSTM (DAGNBL), that utilizes a graph neural network to represent spatial characteristics and bidirectional LSTM units to capture temporal dependencies between features. Attention modules are added to the GNN and BLSTM to find high‐impact attention weight values for the chosen road section. Our model offers the best prediction accuracy with a mean absolute percentage error of 5.21% and a root mean squared error of 4. It can be utilized as a useful tool for predicting traffic flow on certain stretches of road.

Source: Journal of Advanced Transportation

DOI: 10.1155/2023/8962283

Authors: Abdullah Alourani, Farzeen Ashfaq, N. Z. Jhanjhi, Navid Ali Khan

Institutions: Majmaah University, Taylor's University

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,550