Spatiotemporal traffic forecasting: review and proposed directions
A Ermagun, D Levinson - Transport Reviews, 2018 - Taylor & Francis
This paper systematically reviews studies that forecast short-term traffic conditions using
spatial dependence between links. We extract and synthesise 130 research papers …
spatial dependence between links. We extract and synthesise 130 research papers …
Optimized graph convolution recurrent neural network for traffic prediction
Traffic prediction is a core problem in the intelligent transportation system and has broad
applications in the transportation management and planning, and the main challenge of this …
applications in the transportation management and planning, and the main challenge of this …
Deep learning for road traffic forecasting: Does it make a difference?
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 …
This has also been the case of Intelligent Transportation Systems, in which several areas …
Dual dynamic spatial-temporal graph convolution network for traffic prediction
Recently, Graph Convolution Network (GCN) and Temporal Convolution Network (TCN) are
introduced into traffic prediction and achieve state-of-the-art performance due to their good …
introduced into traffic prediction and achieve state-of-the-art performance due to their good …
Short-term speed predictions exploiting big data on large urban road networks
Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the
road network and provide great opportunities for enhanced short-term traffic predictions …
road network and provide great opportunities for enhanced short-term traffic predictions …
[HTML][HTML] A fundamental diagram based hybrid framework for traffic flow estimation and prediction by combining a Markovian model with deep learning
Accurate traffic congestion estimation and prediction are critical building blocks for smart trip
planning and rerouting decisions in transportation systems. Over the decades, there have …
planning and rerouting decisions in transportation systems. Over the decades, there have …
Impact of data loss for prediction of traffic flow on an urban road using neural networks
T Pamuła - IEEE Transactions on Intelligent Transportation …, 2018 - ieeexplore.ieee.org
The deployment of intelligent transport systems requires efficient means of assessing the
traffic situation. This involves gathering real traffic data from the road network and predicting …
traffic situation. This involves gathering real traffic data from the road network and predicting …
Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models
G Fusco, C Colombaroni, L Comelli… - … Conference on Models …, 2015 - ieeexplore.ieee.org
The paper discusses the issues to face in applications of short-term traffic predictions on
urban road networks and the opportunities provided by explicit and implicit models. Different …
urban road networks and the opportunities provided by explicit and implicit models. Different …
The impact of electric mobility scenarios in large urban areas: The Rome case study
In this paper, we evaluate the changes in energy demand and resulting climate change and
air pollutant emissions from the electrification of both the private vehicle fleet and the public …
air pollutant emissions from the electrification of both the private vehicle fleet and the public …
Urban traffic flow online prediction based on multi‐component attention mechanism
B Sun, T Sun, Y Zhang, P Jiao - IET intelligent transport …, 2020 - Wiley Online Library
Traffic flow prediction is regarded as an important concept used in traffic planning, traffic
design, and traffic management. In this study, the authors propose a multi‐component …
design, and traffic management. In this study, the authors propose a multi‐component …