2,053 research outputs found
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
Knowledge Distillation on Spatial-Temporal Graph Convolutional Network for Traffic Prediction
Efficient real-time traffic prediction is crucial for reducing transportation
time. To predict traffic conditions, we employ a spatio-temporal graph neural
network (ST-GNN) to model our real-time traffic data as temporal graphs.
Despite its capabilities, it often encounters challenges in delivering
efficient real-time predictions for real-world traffic data. Recognizing the
significance of timely prediction due to the dynamic nature of real-time data,
we employ knowledge distillation (KD) as a solution to enhance the execution
time of ST-GNNs for traffic prediction. In this paper, We introduce a cost
function designed to train a network with fewer parameters (the student) using
distilled data from a complex network (the teacher) while maintaining its
accuracy close to that of the teacher. We use knowledge distillation,
incorporating spatial-temporal correlations from the teacher network to enable
the student to learn the complex patterns perceived by the teacher. However, a
challenge arises in determining the student network architecture rather than
considering it inadvertently. To address this challenge, we propose an
algorithm that utilizes the cost function to calculate pruning scores,
addressing small network architecture search issues, and jointly fine-tunes the
network resulting from each pruning stage using KD. Ultimately, we evaluate our
proposed ideas on two real-world datasets, PeMSD7 and PeMSD8. The results
indicate that our method can maintain the student's accuracy close to that of
the teacher, even with the retention of only of network parameters
Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks
Accurate prediction of vehicle trajectories is vital for advanced driver
assistance systems and autonomous vehicles. Existing methods mainly rely on
generic trajectory predictions derived from large datasets, overlooking the
personalized driving patterns of individual drivers. To address this gap, we
propose an approach for interaction-aware personalized vehicle trajectory
prediction that incorporates temporal graph neural networks. Our method
utilizes Graph Convolution Networks (GCN) and Long Short-Term Memory (LSTM) to
model the spatio-temporal interactions between target vehicles and their
surrounding traffic. To personalize the predictions, we establish a pipeline
that leverages transfer learning: the model is initially pre-trained on a
large-scale trajectory dataset and then fine-tuned for each driver using their
specific driving data. We employ human-in-the-loop simulation to collect
personalized naturalistic driving trajectories and corresponding surrounding
vehicle trajectories. Experimental results demonstrate the superior performance
of our personalized GCN-LSTM model, particularly for longer prediction
horizons, compared to its generic counterpart. Moreover, the personalized model
outperforms individual models created without pre-training, emphasizing the
significance of pre-training on a large dataset to avoid overfitting. By
incorporating personalization, our approach enhances trajectory prediction
accuracy
Long-Term Traffic Prediction Based on Stacked GCN Model
With the recent surge in road traffic within major cities, the need for both short and long-term traffic flow forecasting has become paramount for city authorities. Previous research efforts have predominantly focused on short-term traffic flow estimations for specific road segments and paths. However, applications of paramount importance, such as traffic management and schedule routing planning, demand a deep understanding of long-term traffic flow predictions. However, due to the intricate interplay of underlying factors, there exists a scarcity of studies dedicated to long-term traffic prediction. Previous research has also highlighted the challenge of lower accuracy in long-term predictions owing to error propagation within the model. This model effectively combines Graph Convolutional Network (GCN) capacity to extract spatial characteristics from the road network with the stacked GCN aptitude for capturing temporal context. Our developed model is subsequently employed for traffic flow forecasting within urban road networks. We rigorously compare our method against baseline techniques using two real-world datasets. Our approach significantly reduces prediction errors by 40% to 60% compared to other methods. The experimental results underscore our model's ability to uncover spatiotemporal dependencies within traffic data and its superior predictive performance over baseline models using real-world traffic datasets
Graph Neural Network for spatiotemporal data: methods and applications
In the era of big data, there has been a surge in the availability of data
containing rich spatial and temporal information, offering valuable insights
into dynamic systems and processes for applications such as weather
forecasting, natural disaster management, intelligent transport systems, and
precision agriculture. Graph neural networks (GNNs) have emerged as a powerful
tool for modeling and understanding data with dependencies to each other such
as spatial and temporal dependencies. There is a large amount of existing work
that focuses on addressing the complex spatial and temporal dependencies in
spatiotemporal data using GNNs. However, the strong interdisciplinary nature of
spatiotemporal data has created numerous GNNs variants specifically designed
for distinct application domains. Although the techniques are generally
applicable across various domains, cross-referencing these methods remains
essential yet challenging due to the absence of a comprehensive literature
review on GNNs for spatiotemporal data. This article aims to provide a
systematic and comprehensive overview of the technologies and applications of
GNNs in the spatiotemporal domain. First, the ways of constructing graphs from
spatiotemporal data are summarized to help domain experts understand how to
generate graphs from various types of spatiotemporal data. Then, a systematic
categorization and summary of existing spatiotemporal GNNs are presented to
enable domain experts to identify suitable techniques and to support model
developers in advancing their research. Moreover, a comprehensive overview of
significant applications in the spatiotemporal domain is offered to introduce a
broader range of applications to model developers and domain experts, assisting
them in exploring potential research topics and enhancing the impact of their
work. Finally, open challenges and future directions are discussed
Attention-based Spatial-Temporal Graph Neural ODE for Traffic Prediction
Traffic forecasting is an important issue in intelligent traffic systems
(ITS). Graph neural networks (GNNs) are effective deep learning models to
capture the complex spatio-temporal dependency of traffic data, achieving ideal
prediction performance. In this paper, we propose attention-based graph neural
ODE (ASTGODE) that explicitly learns the dynamics of the traffic system, which
makes the prediction of our machine learning model more explainable. Our model
aggregates traffic patterns of different periods and has satisfactory
performance on two real-world traffic data sets. The results show that our
model achieves the highest accuracy of the root mean square error metric among
all the existing GNN models in our experiments
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