73 research outputs found
Unveiling the frontiers of deep learning: innovations shaping diverse domains
Deep learning (DL) enables the development of computer models that are
capable of learning, visualizing, optimizing, refining, and predicting data. In
recent years, DL has been applied in a range of fields, including audio-visual
data processing, agriculture, transportation prediction, natural language,
biomedicine, disaster management, bioinformatics, drug design, genomics, face
recognition, and ecology. To explore the current state of deep learning, it is
necessary to investigate the latest developments and applications of deep
learning in these disciplines. However, the literature is lacking in exploring
the applications of deep learning in all potential sectors. This paper thus
extensively investigates the potential applications of deep learning across all
major fields of study as well as the associated benefits and challenges. As
evidenced in the literature, DL exhibits accuracy in prediction and analysis,
makes it a powerful computational tool, and has the ability to articulate
itself and optimize, making it effective in processing data with no prior
training. Given its independence from training data, deep learning necessitates
massive amounts of data for effective analysis and processing, much like data
volume. To handle the challenge of compiling huge amounts of medical,
scientific, healthcare, and environmental data for use in deep learning, gated
architectures like LSTMs and GRUs can be utilized. For multimodal learning,
shared neurons in the neural network for all activities and specialized neurons
for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques
Origin-destination~(OD) flow modeling is an extensively researched subject
across multiple disciplines, such as the investigation of travel demand in
transportation and spatial interaction modeling in geography. However,
researchers from different fields tend to employ their own unique research
paradigms and lack interdisciplinary communication, preventing the
cross-fertilization of knowledge and the development of novel solutions to
challenges. This article presents a systematic interdisciplinary survey that
comprehensively and holistically scrutinizes OD flows from utilizing
fundamental theory to studying the mechanism of population mobility and solving
practical problems with engineering techniques, such as computational models.
Specifically, regional economics, urban geography, and sociophysics are adept
at employing theoretical research methods to explore the underlying mechanisms
of OD flows. They have developed three influential theoretical models: the
gravity model, the intervening opportunities model, and the radiation model.
These models specifically focus on examining the fundamental influences of
distance, opportunities, and population on OD flows, respectively. In the
meantime, fields such as transportation, urban planning, and computer science
primarily focus on addressing four practical problems: OD prediction, OD
construction, OD estimation, and OD forecasting. Advanced computational models,
such as deep learning models, have gradually been introduced to address these
problems more effectively. Finally, based on the existing research, this survey
summarizes current challenges and outlines future directions for this topic.
Through this survey, we aim to break down the barriers between disciplines in
OD flow-related research, fostering interdisciplinary perspectives and modes of
thinking.Comment: 49 pages, 6 figure
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
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges
Machine learning (ML) is widely used for key tasks in Connected and Automated
Vehicles (CAV), including perception, planning, and control. However, its
reliance on vehicular data for model training presents significant challenges
related to in-vehicle user privacy and communication overhead generated by
massive data volumes. Federated learning (FL) is a decentralized ML approach
that enables multiple vehicles to collaboratively develop models, broadening
learning from various driving environments, enhancing overall performance, and
simultaneously securing local vehicle data privacy and security. This survey
paper presents a review of the advancements made in the application of FL for
CAV (FL4CAV). First, centralized and decentralized frameworks of FL are
analyzed, highlighting their key characteristics and methodologies. Second,
diverse data sources, models, and data security techniques relevant to FL in
CAVs are reviewed, emphasizing their significance in ensuring privacy and
confidentiality. Third, specific and important applications of FL are explored,
providing insight into the base models and datasets employed for each
application. Finally, existing challenges for FL4CAV are listed and potential
directions for future work are discussed to further enhance the effectiveness
and efficiency of FL in the context of CAV
Learning Effective Embeddings for Dynamic Graphs and Quantifying Graph Embedding Interpretability
Graph representation learning has been a very active research area in recent years. The goal of graph representation learning is to generate representation vectors that accurately capture the structure and features of large graphs. This is especially important because the quality of the graph representation vectors will affect the performance of these vectors in downstream tasks such as node classification and link prediction. Many techniques have been proposed for generating effective graph representation vectors. These methods can be applied to both static and dynamic graphs. A static graph is a single fixed graph, while a dynamic graph evolves over time, and its nodes and edges can be added or deleted from the graph. We surveyed the graph embedding methods for both static and dynamic graphs. The majority of the existing graph embedding methods are developed for static graphs. Therefore, since most real-world graphs are dynamic, developing novel graph embedding methods suitable for evolving graphs is essential.
This dissertation proposes three dynamic graph embedding models. In previous dynamic methods, the inputs were mainly adjacency matrices of graphs which are not memory efficient and may not capture the neighbourhood structure in graphs effectively. Therefore, we developed Dynnode2vec based on random walks using the static model Node2vec. Dynnode2vec generates node embeddings in each snapshot by initializing the current model with previous embedding vectors and training the model using a set of random walks obtained for nodes in the snapshot. Our second model, LSTM-Node2vec, is also based on random walks. This method leverages the LSTM model to capture the long-range dependencies between nodes in combination with Node2vec to generate node embeddings. Finally, inspired by the importance of substructures in the graphs, our third model TGR-Clique generates node embeddings by considering the effects of neighbours of a node in the maximal cliques containing the node. Experiments on real-world datasets demonstrate the effectiveness of our proposed methods in comparison to the state-of-the-art models. In addition, motivated by the lack of proper measures for quantifying and comparing graph embeddings interpretability, we proposed two interpretability measures for graph embeddings using the centrality properties of graphs
Real-Time Optimization for Dynamic Ride-Sharing
Throughout the last decade, the advent of novel mobility services such as ride-hailing,
car-sharing, and ride-sharing has shaped urban mobility. While these types of services
offer flexible on-demand transportation for customers, they may also increase the load
on the, already strained, road infrastructure and exacerbate traffic congestion problems.
One potential way to remedy this problem is the increased usage of dynamic ride-sharing
services. In this type of service, multiple customer trips are combined into share a vehicle simultaneously.
This leads to more efficient vehicle utilization, reduced prices for customers,
and less traffic congestion at the cost of slight delays compared to direct transportation in
ride-hailing services.
In this thesis, we consider the planning and operation of such dynamic ride-sharing
services. We present a wider look at the planning context of dynamic ride-sharing and
discuss planning problems on the strategical, tactical, and operational level. Subsequently,
our focus is on two operational planning problems: dynamic vehicle routing, and idle
vehicle repositioning.
Regarding vehicle routing, we introduce the vehicle routing problem for dynamic ridesharing
and present a solution procedure. Our algorithmic approach consists of two
phases: a fast insertion heuristic, and a local search improvement phase. The former
handles incoming trip requests and quickly assigns them to suitable vehicles while the
latter is responsible for continuously improving the current routing plan. This way, we
enable fast response times for customers while simultaneously effectively utilizing available
computational resources.
Concerning the idle vehicle repositioning problem, we propose a mathematical model that
takes repositioning decisions and adequately reflects available vehicle resources as well as
a forecast of the upcoming trip request demand. This model is embedded into a real-time
planning algorithm that regularly re-optimizes the movement of idle vehicles. Through an
adaptive parameter calculation process, our algorithm dynamically adapts to changes in
the current system state.
To evaluate our algorithms, we present a modular simulation-based evaluation framework.
We envision that this framework may also be used by other researchers and developers.
In this thesis, we perform computational evaluations on a variety of scenarios based on
real-world data from Chengdu, New York City, and Hamburg. The computational results
show that we are able to produce high-quality solutions in real-time, enabling the usage in
high-demand settings. In addition, our algorithms perform robustly in a variety of settings
and are quickly adapted to new application settings, such as the deployment in a new city
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
Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark]
The field of urban spatial-temporal prediction is advancing rapidly with the
development of deep learning techniques and the availability of large-scale
datasets. However, challenges persist in accessing and utilizing diverse urban
spatial-temporal datasets from different sources and stored in different
formats, as well as determining effective model structures and components with
the proliferation of deep learning models. This work addresses these challenges
and provides three significant contributions. Firstly, we introduce "atomic
files", a unified storage format designed for urban spatial-temporal big data,
and validate its effectiveness on 40 diverse datasets, simplifying data
management. Secondly, we present a comprehensive overview of technological
advances in urban spatial-temporal prediction models, guiding the development
of robust models. Thirdly, we conduct extensive experiments using diverse
models and datasets, establishing a performance leaderboard and identifying
promising research directions. Overall, this work effectively manages urban
spatial-temporal data, guides future efforts, and facilitates the development
of accurate and efficient urban spatial-temporal prediction models. It can
potentially make long-term contributions to urban spatial-temporal data
management and prediction, ultimately leading to improved urban living
standards.Comment: 14 pages, 3 figures. arXiv admin note: text overlap with
arXiv:2304.1434
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