30,689 research outputs found
Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions
Traffic prediction plays an essential role in intelligent transportation
system. Accurate traffic prediction can assist route planing, guide vehicle
dispatching, and mitigate traffic congestion. This problem is challenging due
to the complicated and dynamic spatio-temporal dependencies between different
regions in the road network. Recently, a significant amount of research efforts
have been devoted to this area, especially deep learning method, greatly
advancing traffic prediction abilities. The purpose of this paper is to provide
a comprehensive survey on deep learning-based approaches in traffic prediction
from multiple perspectives. Specifically, we first summarize the existing
traffic prediction methods, and give a taxonomy. Second, we list the
state-of-the-art approaches in different traffic prediction applications.
Third, we comprehensively collect and organize widely used public datasets in
the existing literature to facilitate other researchers. Furthermore, we give
an evaluation and analysis by conducting extensive experiments to compare the
performance of different methods on a real-world public dataset. Finally, we
discuss open challenges in this field.Comment: to be published in IEEE Transactions on Intelligent Transportation
System
Cross-City Transfer Learning for Deep Spatio-Temporal Prediction
Spatio-temporal prediction is a key type of tasks in urban computing, e.g.,
traffic flow and air quality. Adequate data is usually a prerequisite,
especially when deep learning is adopted. However, the development levels of
different cities are unbalanced, and still many cities suffer from data
scarcity. To address the problem, we propose a novel cross-city transfer
learning method for deep spatio-temporal prediction tasks, called RegionTrans.
RegionTrans aims to effectively transfer knowledge from a data-rich source city
to a data-scarce target city. More specifically, we first learn an inter-city
region matching function to match each target city region to a similar source
city region. A neural network is designed to effectively extract region-level
representation for spatio-temporal prediction. Finally, an optimization
algorithm is proposed to transfer learned features from the source city to the
target city with the region matching function. Using citywide crowd flow
prediction as a demonstration experiment, we verify the effectiveness of
RegionTrans. Results show that RegionTrans can outperform the state-of-the-art
fine-tuning deep spatio-temporal prediction models by reducing up to 10.7%
prediction error
Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
This paper presents a novel method for detecting pedestrians under adverse
illumination conditions. Our approach relies on a novel cross-modality learning
framework and it is based on two main phases. First, given a multimodal
dataset, a deep convolutional network is employed to learn a non-linear
mapping, modeling the relations between RGB and thermal data. Then, the learned
feature representations are transferred to a second deep network, which
receives as input an RGB image and outputs the detection results. In this way,
features which are both discriminative and robust to bad illumination
conditions are learned. Importantly, at test time, only the second pipeline is
considered and no thermal data are required. Our extensive evaluation
demonstrates that the proposed approach outperforms the state-of- the-art on
the challenging KAIST multispectral pedestrian dataset and it is competitive
with previous methods on the popular Caltech dataset.Comment: Accepted at CVPR 201
Attentive Crowd Flow Machines
Traffic flow prediction is crucial for urban traffic management and public
safety. Its key challenges lie in how to adaptively integrate the various
factors that affect the flow changes. In this paper, we propose a unified
neural network module to address this problem, called Attentive Crowd Flow
Machine~(ACFM), which is able to infer the evolution of the crowd flow by
learning dynamic representations of temporally-varying data with an attention
mechanism. Specifically, the ACFM is composed of two progressive ConvLSTM units
connected with a convolutional layer for spatial weight prediction. The first
LSTM takes the sequential flow density representation as input and generates a
hidden state at each time-step for attention map inference, while the second
LSTM aims at learning the effective spatial-temporal feature expression from
attentionally weighted crowd flow features. Based on the ACFM, we further build
a deep architecture with the application to citywide crowd flow prediction,
which naturally incorporates the sequential and periodic data as well as other
external influences. Extensive experiments on two standard benchmarks (i.e.,
crowd flow in Beijing and New York City) show that the proposed method achieves
significant improvements over the state-of-the-art methods.Comment: ACM MM, full pape
Short-term Road Traffic Prediction based on Deep Cluster at Large-scale Networks
Short-term road traffic prediction (STTP) is one of the most important
modules in Intelligent Transportation Systems (ITS). However, network-level
STTP still remains challenging due to the difficulties both in modeling the
diverse traffic patterns and tacking high-dimensional time series with low
latency. Therefore, a framework combining with a deep clustering (DeepCluster)
module is developed for STTP at largescale networks in this paper. The
DeepCluster module is proposed to supervise the representation learning in a
visualized way from the large unlabeled dataset. More specifically, to fully
exploit the traffic periodicity, the raw series is first split into a number of
sub-series for triplets generation. The convolutional neural networks (CNNs)
with triplet loss are utilized to extract the features of shape by transferring
the series into visual images. The shape-based representations are then used
for road segments clustering. Thereafter, motivated by the fact that the road
segments in a group have similar patterns, a model sharing strategy is further
proposed to build recurrent NNs (RNNs)-based predictions through a group-based
model (GM), instead of individual-based model (IM) in which one model are built
for one road exclusively. Our framework can not only significantly reduce the
number of models and cost, but also increase the number of training data and
the diversity of samples. In the end, we evaluate the proposed framework over
the network of Liuli Bridge in Beijing. Experimental results show that the
DeepCluster can effectively cluster the road segments and GM can achieve
comparable performance against the IM with less number of models.Comment: 12 pages, 15 figures, journa
URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision
Within the burgeoning expansion of deep learning and computer vision across
the different fields of science, when it comes to urban development, deep
learning and computer vision applications are still limited towards the notions
of smart cities and autonomous vehicles. Indeed, a wide gap of knowledge
appears when it comes to cities and urban regions in less developed countries
where the chaos of informality is the dominant scheme. How can deep learning
and Artificial Intelligence (AI) untangle the complexities of informality to
advance urban modelling and our understanding of cities? Various questions and
debates can be raised concerning the future of cities of the North and the
South in the paradigm of AI and computer vision. In this paper, we introduce a
new method for multipurpose realistic-dynamic urban modelling relying on deep
learning and computer vision, using deep Convolutional Neural Networks (CNN),
to sense and detect informality and slums in urban scenes from aerial and
street view images in addition to detection of pedestrian and transport modes.
The model has been trained on images of urban scenes in cities across the
globe. The model shows a good validation of understanding a wide spectrum of
nuances among the planned and the unplanned regions, including informal and
slum areas. We attempt to advance urban modelling for better understanding the
dynamics of city developments. We also aim to exemplify the significant impacts
of AI in cities beyond how smart cities are discussed and perceived in the
mainstream. The algorithms of the URBAN-i model are fully-coded in Python
programming with the pre-trained deep learning models to be used as a tool for
mapping and city modelling in the various corner of the globe, including
informal settlements and slum regions.Comment: 12 pages, 9 figure
Visual Affordance and Function Understanding: A Survey
Nowadays, robots are dominating the manufacturing, entertainment and
healthcare industries. Robot vision aims to equip robots with the ability to
discover information, understand it and interact with the environment. These
capabilities require an agent to effectively understand object affordances and
functionalities in complex visual domains. In this literature survey, we first
focus on Visual affordances and summarize the state of the art as well as open
problems and research gaps. Specifically, we discuss sub-problems such as
affordance detection, categorization, segmentation and high-level reasoning.
Furthermore, we cover functional scene understanding and the prevalent
functional descriptors used in the literature. The survey also provides
necessary background to the problem, sheds light on its significance and
highlights the existing challenges for affordance and functionality learning.Comment: 26 pages, 22 image
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
As a key technique for enabling artificial intelligence, machine learning
(ML) is capable of solving complex problems without explicit programming.
Motivated by its successful applications to many practical tasks like image
recognition, both industry and the research community have advocated the
applications of ML in wireless communication. This paper comprehensively
surveys the recent advances of the applications of ML in wireless
communication, which are classified as: resource management in the MAC layer,
networking and mobility management in the network layer, and localization in
the application layer. The applications in resource management further include
power control, spectrum management, backhaul management, cache management,
beamformer design and computation resource management, while ML based
networking focuses on the applications in clustering, base station switching
control, user association and routing. Moreover, literatures in each aspect is
organized according to the adopted ML techniques. In addition, several
conditions for applying ML to wireless communication are identified to help
readers decide whether to use ML and which kind of ML techniques to use, and
traditional approaches are also summarized together with their performance
comparison with ML based approaches, based on which the motivations of surveyed
literatures to adopt ML are clarified. Given the extensiveness of the research
area, challenges and unresolved issues are presented to facilitate future
studies, where ML based network slicing, infrastructure update to support ML
based paradigms, open data sets and platforms for researchers, theoretical
guidance for ML implementation and so on are discussed.Comment: 34 pages,8 figure
CityNet: A Multi-city Multi-modal Dataset for Smart City Applications
Data-driven approaches have been applied to many problems in urban computing.
However, in the research community, such approaches are commonly studied under
data from limited sources, and are thus unable to characterize the complexity
of urban data coming from multiple entities and the correlations among them.
Consequently, an inclusive and multifaceted dataset is necessary to facilitate
more extensive studies on urban computing. In this paper, we present CityNet, a
multi-modal urban dataset containing data from 7 cities, each of which coming
from 3 data sources. We first present the generation process of CityNet as well
as its basic properties. In addition, to facilitate the use of CityNet, we
carry out extensive machine learning experiments, including spatio-temporal
predictions, transfer learning, and reinforcement learning. The experimental
results not only provide benchmarks for a wide range of tasks and methods, but
also uncover internal correlations among cities and tasks within CityNet that,
with adequate leverage, can improve performances on various tasks. With the
benchmarking results and the correlations uncovered, we believe that CityNet
can contribute to the field of urban computing by supporting research on many
advanced topics
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