Accurate forecasting of future traffic flow has a wide range of applications, which is a
fundamental component of intelligent transportation systems. However, timely and accurate …

Short-term traffic flow forecasting at isolated points is a fundamental yet challenging task in
many intelligent transportation systems. We present a novel long short-term memory (LSTM) …

Timely and accurate traffic flow forecasting is open challenging. Canonical long short-term
memory (LSTM) network is considered qualified to capture the long-term temporal …

Deep Learning methods have been proven to be flexible to model complex phenomena.
This has also been the case of Intelligent Transportation Systems, in which several areas …

Convolutional neural networks (CNNs) offer a broad technical framework to deal with spatial
feature extraction and nonlinearity capture, whereas they cannot process sequence data …

This paper presents a spatial-temporal deep learning network, termed ST-TrafficNet, for
traffic flow forecasting. Recent deep learning methods highly relate accurate predetermined …

The complex traffic network spatial correlation and the characteristic of high nonlinear and
dynamic traffic conditions in the time are the challenges to accurate traffic flow forecasting …

A prompt and precise estimation of traffic conditions on the scale of a few minutes by
analyzing past data is crucial for establishing an effective intelligent traffic management …

Traffic flow modeling plays a crucial role in intelligent transportation systems, which is of vital
significance for mitigating traffic congestion and reducing carbon emissions. Owing to the …

Real-time and high-precision traffic flow prediction plays a crucial role in transportation
management, contributing to control dispatch and reducing traffic congestion. Due to the …