1,456 research outputs found
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
AST-GIN: Attribute-Augmented Spatial-Temporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting
Electric Vehicle (EV) charging demand and charging station availability
forecasting is one of the challenges in the intelligent transportation system.
With the accurate EV station situation prediction, suitable charging behaviors
could be scheduled in advance to relieve range anxiety. Many existing deep
learning methods are proposed to address this issue, however, due to the
complex road network structure and comprehensive external factors, such as
point of interests (POIs) and weather effects, many commonly used algorithms
could just extract the historical usage information without considering
comprehensive influence of external factors. To enhance the prediction accuracy
and interpretability, the Attribute-Augmented Spatial-Temporal Graph Informer
(AST-GIN) structure is proposed in this study by combining the Graph
Convolutional Network (GCN) layer and the Informer layer to extract both
external and internal spatial-temporal dependence of relevant transportation
data. And the external factors are modeled as dynamic attributes by the
attribute-augmented encoder for training. AST-GIN model is tested on the data
collected in Dundee City and experimental results show the effectiveness of our
model considering external factors influence over various horizon settings
compared with other baselines.Comment: 10 pages; 17 figures; Under review for IEEE Transaction on Vehicular
Technolog
TensorLayer: A Versatile Library for Efficient Deep Learning Development
Deep learning has enabled major advances in the fields of computer vision,
natural language processing, and multimedia among many others. Developing a
deep learning system is arduous and complex, as it involves constructing neural
network architectures, managing training/trained models, tuning optimization
process, preprocessing and organizing data, etc. TensorLayer is a versatile
Python library that aims at helping researchers and engineers efficiently
develop deep learning systems. It offers rich abstractions for neural networks,
model and data management, and parallel workflow mechanism. While boosting
efficiency, TensorLayer maintains both performance and scalability. TensorLayer
was released in September 2016 on GitHub, and has helped people from academia
and industry develop real-world applications of deep learning.Comment: ACM Multimedia 201
A Survey on Graph Neural Networks in Intelligent Transportation Systems
Intelligent Transportation System (ITS) is vital in improving traffic
congestion, reducing traffic accidents, optimizing urban planning, etc.
However, due to the complexity of the traffic network, traditional machine
learning and statistical methods are relegated to the background. With the
advent of the artificial intelligence era, many deep learning frameworks have
made remarkable progress in various fields and are now considered effective
methods in many areas. As a deep learning method, Graph Neural Networks (GNNs)
have emerged as a highly competitive method in the ITS field since 2019 due to
their strong ability to model graph-related problems. As a result, more and
more scholars pay attention to the applications of GNNs in transportation
domains, which have shown excellent performance. However, most of the research
in this area is still concentrated on traffic forecasting, while other ITS
domains, such as autonomous vehicles and urban planning, still require more
attention. This paper aims to review the applications of GNNs in six
representative and emerging ITS domains: traffic forecasting, autonomous
vehicles, traffic signal control, transportation safety, demand prediction, and
parking management. We have reviewed extensive graph-related studies from 2018
to 2023, summarized their methods, features, and contributions, and presented
them in informative tables or lists. Finally, we have identified the challenges
of applying GNNs to ITS and suggested potential future directions
Integrating IoT-Sensing and Crowdsensing with Privacy: Privacy-Preserving Hybrid Sensing for Smart Cities
Data sensing and gathering is an essential task for various
information-driven services in smart cities. On the one hand, Internet of
Things (IoT) sensors can be deployed at certain fixed locations to capture data
reliably but suffer from limited sensing coverage. On the other hand, data can
also be gathered dynamically through crowdsensing contributed by voluntary
users but suffer from its unreliability and the lack of incentives for users'
contributions. In this paper, we explore an integrated paradigm called "hybrid
sensing" that harnesses both IoT-sensing and crowdsensing in a complementary
manner. In hybrid sensing, users are incentivized to provide sensing data not
covered by IoT sensors and provide crowdsourced feedback to assist in
calibrating IoT-sensing. Their contributions will be rewarded with credits that
can be redeemed to retrieve synthesized information from the hybrid system. In
this paper, we develop a hybrid sensing system that supports explicit user
privacy -- IoT sensors are obscured physically to prevent capturing private
user data, and users interact with a crowdsensing server via a
privacy-preserving protocol to preserve their anonymity. A key application of
our system is smart parking, by which users can inquire and find the available
parking spaces in outdoor parking lots. We implemented our hybrid sensing
system for smart parking and conducted extensive empirical evaluations.
Finally, our hybrid sensing system can be potentially applied to other
information-driven services in smart cities.Comment: To appear in ACM Transactions on Internet of Thing
Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems
The Intelligent Transportation System (ITS) is an important part of modern
transportation infrastructure, employing a combination of communication
technology, information processing and control systems to manage transportation
networks. This integration of various components such as roads, vehicles, and
communication systems, is expected to improve efficiency and safety by
providing better information, services, and coordination of transportation
modes. In recent years, graph-based machine learning has become an increasingly
important research focus in the field of ITS aiming at the development of
complex, data-driven solutions to address various ITS-related challenges. This
chapter presents background information on the key technical challenges for ITS
design, along with a review of research methods ranging from classic
statistical approaches to modern machine learning and deep learning-based
approaches. Specifically, we provide an in-depth review of graph-based machine
learning methods, including basic concepts of graphs, graph data
representation, graph neural network architectures and their relation to ITS
applications. Additionally, two case studies of graph-based ITS applications
proposed in our recent work are presented in detail to demonstrate the
potential of graph-based machine learning in the ITS domain
Identifying Real Estate Opportunities using Machine Learning
The real estate market is exposed to many fluctuations in prices because of
existing correlations with many variables, some of which cannot be controlled
or might even be unknown. Housing prices can increase rapidly (or in some
cases, also drop very fast), yet the numerous listings available online where
houses are sold or rented are not likely to be updated that often. In some
cases, individuals interested in selling a house (or apartment) might include
it in some online listing, and forget about updating the price. In other cases,
some individuals might be interested in deliberately setting a price below the
market price in order to sell the home faster, for various reasons. In this
paper, we aim at developing a machine learning application that identifies
opportunities in the real estate market in real time, i.e., houses that are
listed with a price substantially below the market price. This program can be
useful for investors interested in the housing market. We have focused in a use
case considering real estate assets located in the Salamanca district in Madrid
(Spain) and listed in the most relevant Spanish online site for home sales and
rentals. The application is formally implemented as a regression problem that
tries to estimate the market price of a house given features retrieved from
public online listings. For building this application, we have performed a
feature engineering stage in order to discover relevant features that allows
for attaining a high predictive performance. Several machine learning
algorithms have been tested, including regression trees, k-nearest neighbors,
support vector machines and neural networks, identifying advantages and
handicaps of each of them.Comment: 24 pages, 13 figures, 5 table
Survey of smart parking systems
The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, looking for the most optimal path, are needed. Most contributions in the literature are routing strategies that take into account different criteria to select the optimal route required to find a parking space. This paper aims to identify the types of smart parking systems (SPS) that are available today, as well as investigate the kinds of vehicle detection techniques (VDT) they have and the algorithms or other methods they employ, in order to analyze where the development of these systems is at today. To do this, a survey of 274 publications from January 2012 to December 2019 was conducted. The survey considered four principal features: SPS types reported in the literature, the kinds of VDT used in these SPS, the algorithms or methods they implement, and the stage of development at which they are. Based on a search and extraction of results methodology, this work was able to effectively obtain the current state of the research area. In addition, the exhaustive study of the studies analyzed allowed for a discussion to be established concerning the main difficulties, as well as the gaps and open problems detected for the SPS. The results shown in this study may provide a base for future research on the subject.Fil: Diaz Ogás, Mathias Gabriel. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, FĂsicas y Naturales; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - San Juan; ArgentinaFil: Fabregat Gesa, Ramon. Universidad de Girona; EspañaFil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, FĂsicas y Naturales; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - San Juan; Argentin
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