Nature

Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data

Long short-term memory (LSTM) models provide high predictive performance through their ability to recognize longer sequences of time series data. More recently, bidirectional deep learning models ...
Nature

An efficient intelligent transportation system for traffic flow prediction using meta-temporal hyperbolic quantum graph neural networks

Considering, growing quantity of automobiles on the road, modern transportation schemes face significant challenges such as gridlock, frequent accidents, thenconservational deterioration. When taking ...
National Academies of Sciences%2c Engineering%2c and Medicine

Practices for Operational Traffic Simulation Models

This chapter presents the results of the literature review for operational traffic simulation models. Sources compiled for the literature review include guidance documents (general and DOT-specific), ...
EurekAlert!

Are state-of-the-art deep learning traffic prediction models truly effective?

All of the baseline models achieve excellent performance in predicting high speed while performing extremely poorly in predicting lower ones. Specifically, even if the prediction horizon is 60 mins, a ...