101 research outputs found
Towards better traffic volume estimation: Tackling both underdetermined and non-equilibrium problems via a correlation-adaptive graph convolution network
Traffic volume is an indispensable ingredient to provide fine-grained
information for traffic management and control. However, due to limited
deployment of traffic sensors, obtaining full-scale volume information is far
from easy. Existing works on this topic primarily focus on improving the
overall estimation accuracy of a particular method and ignore the underlying
challenges of volume estimation, thereby having inferior performances on some
critical tasks. This paper studies two key problems with regard to traffic
volume estimation: (1) underdetermined traffic flows caused by undetected
movements, and (2) non-equilibrium traffic flows arise from congestion
propagation. Here we demonstrate a graph-based deep learning method that can
offer a data-driven, model-free and correlation adaptive approach to tackle the
above issues and perform accurate network-wide traffic volume estimation.
Particularly, in order to quantify the dynamic and nonlinear relationships
between traffic speed and volume for the estimation of underdetermined flows, a
speed patternadaptive adjacent matrix based on graph attention is developed and
integrated into the graph convolution process, to capture non-local
correlations between sensors. To measure the impacts of non-equilibrium flows,
a temporal masked and clipped attention combined with a gated temporal
convolution layer is customized to capture time-asynchronous correlations
between upstream and downstream sensors. We then evaluate our model on a
real-world highway traffic volume dataset and compare it with several benchmark
models. It is demonstrated that the proposed model achieves high estimation
accuracy even under 20% sensor coverage rate and outperforms other baselines
significantly, especially on underdetermined and non-equilibrium flow
locations. Furthermore, comprehensive quantitative model analysis are also
carried out to justify the model designs
Spatial Data Quality in the IoT Era:Management and Exploitation
Within the rapidly expanding Internet of Things (IoT), growing amounts of spatially referenced data are being generated. Due to the dynamic, decentralized, and heterogeneous nature of the IoT, spatial IoT data (SID) quality has attracted considerable attention in academia and industry. How to invent and use technologies for managing spatial data quality and exploiting low-quality spatial data are key challenges in the IoT. In this tutorial, we highlight the SID consumption requirements in applications and offer an overview of spatial data quality in the IoT setting. In addition, we review pertinent technologies for quality management and low-quality data exploitation, and we identify trends and future directions for quality-aware SID management and utilization. The tutorial aims to not only help researchers and practitioners to better comprehend SID quality challenges and solutions, but also offer insights that may enable innovative research and applications
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values
Multivariate time series (MTS) forecasting is widely used in various domains,
such as meteorology and traffic. Due to limitations on data collection,
transmission, and storage, real-world MTS data usually contains missing values,
making it infeasible to apply existing MTS forecasting models such as linear
regression and recurrent neural networks. Though many efforts have been devoted
to this problem, most of them solely rely on local dependencies for imputing
missing values, which ignores global temporal dynamics. Local
dependencies/patterns would become less useful when the missing ratio is high,
or the data have consecutive missing values; while exploring global patterns
can alleviate such problems. Thus, jointly modeling local and global temporal
dynamics is very promising for MTS forecasting with missing values. However,
work in this direction is rather limited. Therefore, we study a novel problem
of MTS forecasting with missing values by jointly exploring local and global
temporal dynamics. We propose a new framework LGnet, which leverages memory
network to explore global patterns given estimations from local perspectives.
We further introduce adversarial training to enhance the modeling of global
temporal distribution. Experimental results on real-world datasets show the
effectiveness of LGnet for MTS forecasting with missing values and its
robustness under various missing ratios.Comment: Accepted by AAAI 202
Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective
Data-driven decision making is becoming an integral part of manufacturing
companies. Data is collected and commonly used to improve efficiency and
produce high quality items for the customers. IoT-based and other forms of
object tracking are an emerging tool for collecting movement data of
objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over
space and time. Movement data can provide valuable insights like process
bottlenecks, resource utilization, effective working time etc. that can be used
for decision making and improving efficiency.
Turning movement data into valuable information for industrial management and
decision making requires analysis methods. We refer to this process as movement
analytics. The purpose of this document is to review the current state of work
for movement analytics both in manufacturing and more broadly.
We survey relevant work from both a theoretical perspective and an
application perspective. From the theoretical perspective, we put an emphasis
on useful methods from two research areas: machine learning, and logic-based
knowledge representation. We also review their combinations in view of movement
analytics, and we discuss promising areas for future development and
application. Furthermore, we touch on constraint optimization.
From an application perspective, we review applications of these methods to
movement analytics in a general sense and across various industries. We also
describe currently available commercial off-the-shelf products for tracking in
manufacturing, and we overview main concepts of digital twins and their
applications
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
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
Urban Mobility Analytics: Understanding, Inference and Forecasting
Transport systems are the backbones of social and economic activities, which promote industry development and accelerate the process of urbanization. However, the contradiction between the pursuit of travel quality and unbalanced/inadequate development needs the rational construction and operation of transport systems. Owing to the evolution of a massive amount of multi-source data from transport systems, urban mobility analytics, including understanding, inference, and forecasting, support the management and control of transport, which attracts great attention in the long term and becomes more essential in smart transport research. In this thesis, we focus on inferring passenger demographics and predicting passenger demand by understanding travel patterns based on deep spatial-temporal learning algorithms.
We first review the latest state-of-the-art deep learning methods for traffic understanding and attributes inference, traffic forecasting, and demand forecasting to form an overview of the current research progress. Second, we introduce the study public transport dataset collected from the Greater Sydney area and analyze the distributions and similarities of multiple transport modes. Third, we study the investigation of spatial and temporal features in order to infer traveler attributes by proposing a deep-based network with two modules (i.e., a Product-based Spatial-Temporal Module and an Auto-Encoder-based Compression Module). In addition, we study providing confidence interval-based passenger demand forecasting by proposing Probabilistic Graph Convolution Model to help relevant authorities and institutions to better accommodate demand uncertainty/variability. Then, to explore the relations in multimodal transport to boost the demand prediction performance, we propose two deep-based networks for knowledge adaptation between different transport modes by data sharing and model sharing, respectively. Finally, we provide promising directions for future works and conclude the thesis
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