Spatiotemporal correlation modelling for machine learning-based traffic state predictions: state-of-the-art and beyond
H Cui, Q Meng, TH Teng, X Yang - Transport reviews, 2023 - Taylor & Francis
Predicting traffic states has gained more attention because of its practical significance.
However, the existing literature lacks a critical review regarding how to address the …
However, the existing literature lacks a critical review regarding how to address the …
Traffic flow matrix-based graph neural network with attention mechanism for traffic flow prediction
Traffic flow forecasting is of great importance in intelligent transportation systems for
congestion mitigation and intelligent traffic management. Most of the existing methods …
congestion mitigation and intelligent traffic management. Most of the existing methods …
Traffic management approaches using machine learning and deep learning techniques: A survey
Traffic management is improved in cutting-edge smart cities using technologies such as
machine learning and deep learning to streamline daily tasks and boost productivity …
machine learning and deep learning to streamline daily tasks and boost productivity …
[HTML][HTML] Hybrid Graph Models for Traffic Prediction
R Chen, H Yao - Applied Sciences, 2023 - mdpi.com
Obtaining accurate road conditions is crucial for traffic management, dynamic route
planning, and intelligent guidance services. The complex spatial correlation and nonlinear …
planning, and intelligent guidance services. The complex spatial correlation and nonlinear …
[HTML][HTML] Proposal of a machine learning approach for traffic flow prediction
M Berlotti, S Di Grande, S Cavalieri - Sensors, 2024 - mdpi.com
Rapid global urbanization has led to a growing urban population, posing challenges in
transportation management. Persistent issues such as traffic congestion, environmental …
transportation management. Persistent issues such as traffic congestion, environmental …
Urban traffic forecasting using federated and continual learning
Smart cities are instrumented with several types pf sensors, which allow to transmit,
elaborate and exploit the collected data for different services. In this paper we focus on the …
elaborate and exploit the collected data for different services. In this paper we focus on the …
Spatiotemporal Exogenous Variables Enhanced Model for Traffic Flow Prediction
C Dong, X Feng, Y Wang, X Wei - IEEE Access, 2023 - ieeexplore.ieee.org
Traffic flow prediction is a vital component of Intelligent Transportation Systems (ITS).
However, it is extremely challenging to predict traffic flow accurately for a large-scale road …
However, it is extremely challenging to predict traffic flow accurately for a large-scale road …
[HTML][HTML] Fiber optic acoustic sensing to understand and affect the rhythm of the cities: proof-of-concept to create data-driven urban mobility models
In the framework of massive sensing and smart sustainable cities, this work presents an
urban distributed acoustic sensing testbed in the vicinity of the School of Technology and …
urban distributed acoustic sensing testbed in the vicinity of the School of Technology and …
Traffic congestion forecasting using multilayered deep neural network
This study proposes a multilayered deep neural network (MLDNN) and a congestion index
(CI) based on traffic density factor to forecast traffic congestion directly. Data were collected …
(CI) based on traffic density factor to forecast traffic congestion directly. Data were collected …
Urban traffic flow estimation system based on gated recurrent unit deep learning methodology for Internet of Vehicles
AHA Hussain, MA Taher, OA Mahmood… - IEEE …, 2023 - ieeexplore.ieee.org
Congestion in the world's traffic systems is a major issue that has far-reaching
repercussions, including wasted time and money due to longer commutes and more …
repercussions, including wasted time and money due to longer commutes and more …