A survey of traffic prediction: from spatio-temporal data to intelligent transportation
Intelligent transportation (eg, intelligent traffic light) makes our travel more convenient and
efficient. With the development of mobile Internet and position technologies, it is reasonable …
efficient. With the development of mobile Internet and position technologies, it is reasonable …
Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning
Exploiting deep learning techniques for traffic flow prediction has become increasingly
widespread. Most existing studies combine CNN or GCN with recurrent neural network to …
widespread. Most existing studies combine CNN or GCN with recurrent neural network to …
Hybrid deep learning models for traffic prediction in large-scale road networks
Traffic prediction is an important component in Intelligent Transportation Systems (ITSs) for
enabling advanced transportation management and services to address worsening traffic …
enabling advanced transportation management and services to address worsening traffic …
Multiple dynamic graph based traffic speed prediction method
Z Zhang, Y Li, H Song, H Dong - Neurocomputing, 2021 - Elsevier
Traffic speed prediction is a crucial and challenging task for intelligent transportation
systems. The prediction task can be accomplished via graph neural networks with structured …
systems. The prediction task can be accomplished via graph neural networks with structured …
An effective joint prediction model for travel demands and traffic flows
In this paper, we study how to jointly predict travel demands and traffic flows for all regions of
a city at a future time interval. From an empirical analysis of traffic data, we outline three …
a city at a future time interval. From an empirical analysis of traffic data, we outline three …
A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction
R de Medrano, JL Aznarte - Applied Soft Computing, 2020 - Elsevier
Spatio-temporal forecasting is an open research field whose interest is growing
exponentially. In this work we focus on creating a complex deep neural framework for spatio …
exponentially. In this work we focus on creating a complex deep neural framework for spatio …
Short-term demand forecasting for online car-hailing using ConvLSTM networks
X Lu, C Ma, Y Qiao - Physica A: Statistical Mechanics and its Applications, 2021 - Elsevier
This paper used the previous data of online car-hailing orders in Haikou provided by Didi
Chuxing GAIA Initiative to predict the short-term demand for online car-hailing service. This …
Chuxing GAIA Initiative to predict the short-term demand for online car-hailing service. This …
Adaptive feature fusion networks for origin-destination passenger flow prediction in metro systems
Accurately predicting Origin-Destination (OD) passenger flow can help metro service quality
and efficiency. Existing works have focused on predicting incoming and outgoing flows for …
and efficiency. Existing works have focused on predicting incoming and outgoing flows for …
RGDAN: A random graph diffusion attention network for traffic prediction
Traffic Prediction based on graph structures is a challenging task given that road networks
are typically complex structures and the data to be analyzed contains variable temporal …
are typically complex structures and the data to be analyzed contains variable temporal …