8,125 research outputs found
DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting
Predicting traffic conditions has been recently explored as a way to relieve
traffic congestion. Several pioneering approaches have been proposed based on
traffic observations of the target location as well as its adjacent regions,
but they obtain somewhat limited accuracy due to lack of mining road topology.
To address the effect attenuation problem, we propose to take account of the
traffic of surrounding locations(wider than adjacent range). We propose an
end-to-end framework called DeepTransport, in which Convolutional Neural
Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain
spatial-temporal traffic information within a transport network topology. In
addition, attention mechanism is introduced to align spatial and temporal
information. Moreover, we constructed and released a real-world large traffic
condition dataset with 5-minute resolution. Our experiments on this dataset
demonstrate our method captures the complex relationship in temporal and
spatial domain. It significantly outperforms traditional statistical methods
and a state-of-the-art deep learning method
DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting
Traffic speed forecasting is one of the core problems in Intelligent
Transportation Systems. For a more accurate prediction, recent studies started
using not only the temporal speed patterns but also the spatial information on
the road network through the graph convolutional networks. Even though the road
network is highly complex due to its non-Euclidean and directional
characteristics, previous approaches mainly focus on modeling the spatial
dependencies only with the distance. In this paper, we identify two essential
spatial dependencies in traffic forecasting in addition to distance, direction
and positional relationship, for designing basic graph elements as the smallest
building blocks. Using the building blocks, we suggest DDP-GCN (Distance,
Direction, and Positional relationship Graph Convolutional Network) to
incorporate the three spatial relationships into prediction network for traffic
forecasting. We evaluate the proposed model with two large-scale real-world
datasets, and find 7.40% average improvement for 1-hour forecasting in highly
complex urban networks
Epidemiological Prediction using Deep Learning
Department of Mathematical SciencesAccurate and real-time epidemic disease prediction plays a significant role in the health system and is of great importance for policy making, vaccine distribution and disease control. From the SIR model by Mckendrick and Kermack in the early 1900s, researchers have developed a various mathematical model to forecast the spread of disease. With all attempt, however, the epidemic prediction has always been an ongoing scientific issue due to the limitation that the current model lacks flexibility or shows poor performance. Owing to the temporal and spatial aspect of epidemiological data, the problem fits into the category of time-series forecasting. To capture both aspects of the data, this paper proposes a combination of recent Deep Leaning
models and applies the model to ILI (influenza like illness) data in the United States. Specifically, the graph convolutional network (GCN) model is used to capture the geographical feature of the U.S. regions and the gated recurrent unit (GRU) model is used to capture the temporal dynamics of ILI. The result was compared with the Deep Learning model proposed by other researchers, demonstrating the proposed model outperforms the previous methods.clos
Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion
which represents a critical problem globally, resulting in negative
consequences such as lost hours of additional travel time and increased fuel
consumption. Integrating emerging technologies into transportation systems
provides opportunities for improving traffic prediction significantly and
brings about new research problems. In order to lay the foundation for
understanding the open research challenges in traffic prediction, this survey
aims to provide a comprehensive overview of traffic prediction methodologies.
Specifically, we focus on the recent advances and emerging research
opportunities in Artificial Intelligence (AI)-based traffic prediction methods,
due to their recent success and potential in traffic prediction, with an
emphasis on multivariate traffic time series modeling. We first provide a list
and explanation of the various data types and resources used in the literature.
Next, the essential data preprocessing methods within the traffic prediction
context are categorized, and the prediction methods and applications are
subsequently summarized. Lastly, we present primary research challenges in
traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies
(TR_C), Volume 145, 202
Enhancing Spatiotemporal Traffic Prediction through Urban Human Activity Analysis
Traffic prediction is one of the key elements to ensure the safety and
convenience of citizens. Existing traffic prediction models primarily focus on
deep learning architectures to capture spatial and temporal correlation. They
often overlook the underlying nature of traffic. Specifically, the sensor
networks in most traffic datasets do not accurately represent the actual road
network exploited by vehicles, failing to provide insights into the traffic
patterns in urban activities. To overcome these limitations, we propose an
improved traffic prediction method based on graph convolution deep learning
algorithms. We leverage human activity frequency data from National Household
Travel Survey to enhance the inference capability of a causal relationship
between activity and traffic patterns. Despite making minimal modifications to
the conventional graph convolutional recurrent networks and graph convolutional
transformer architectures, our approach achieves state-of-the-art performance
without introducing excessive computational overhead.Comment: CIKM 202
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