222 research outputs found
Joint predictions of multi-modal ride-hailing demands: a deep multi-task multigraph learning-based approach
Ride-hailing platforms generally provide various service options to
customers, such as solo ride services, shared ride services, etc. It is
generally expected that demands for different service modes are correlated, and
the prediction of demand for one service mode can benefit from historical
observations of demands for other service modes. Moreover, an accurate joint
prediction of demands for multiple service modes can help the platforms better
allocate and dispatch vehicle resources. Although there is a large stream of
literature on ride-hailing demand predictions for one specific service mode,
little efforts have been paid towards joint predictions of ride-hailing demands
for multiple service modes. To address this issue, we propose a deep multi-task
multi-graph learning approach, which combines two components: (1) multiple
multi-graph convolutional (MGC) networks for predicting demands for different
service modes, and (2) multi-task learning modules that enable knowledge
sharing across multiple MGC networks. More specifically, two multi-task
learning structures are established. The first one is the regularized
cross-task learning, which builds cross-task connections among the inputs and
outputs of multiple MGC networks. The second one is the multi-linear
relationship learning, which imposes a prior tensor normal distribution on the
weights of various MGC networks. Although there are no concrete bridges between
different MGC networks, the weights of these networks are constrained by each
other and subject to a common prior distribution. Evaluated with the
for-hire-vehicle datasets in Manhattan, we show that our propose approach
outperforms the benchmark algorithms in prediction accuracy for different
ride-hailing modes
Deep-learning Architecture for Short-term Passenger Flow Forecasting in Urban Rail Transit
Short-term passenger flow forecasting is an essential component in urban rail
transit operation. Emerging deep learning models provide good insight into
improving prediction precision. Therefore, we propose a deep learning
architecture combining the residual network (ResNet), graph convolutional
network (GCN), and long short-term memory (LSTM) (called "ResLSTM") to forecast
short-term passenger flow in urban rail transit on a network scale. First,
improved methodologies of the ResNet, GCN, and attention LSTM models are
presented. Then, the model architecture is proposed, wherein ResNet is used to
capture deep abstract spatial correlations between subway stations, GCN is
applied to extract network topology information, and attention LSTM is used to
extract temporal correlations. The model architecture includes four branches
for inflow, outflow, graph-network topology, as well as weather conditions and
air quality. To the best of our knowledge, this is the first time that
air-quality indicators have been taken into account, and their influences on
prediction precision quantified. Finally, ResLSTM is applied to the Beijing
subway using three time granularities (10, 15, and 30 min) to conduct
short-term passenger flow forecasting. A comparison of the prediction
performance of ResLSTM with those of many state-of-the-art models illustrates
the advantages and robustness of ResLSTM. Moreover, a comparison of the
prediction precisions obtained for time granularities of 10, 15, and 30 min
indicates that prediction precision increases with increasing time granularity.
This study can provide subway operators with insight into short-term passenger
flow forecasting by leveraging deep learning models.Comment: in IEEE Transactions on Intelligent Transportation System
Position-Aware Convolutional Networks for Traffic Prediction
Forecasting the future traffic flow distribution in an area is an important
issue for traffic management in an intelligent transportation system. The key
challenge of traffic prediction is to capture spatial and temporal relations
between future traffic flows and historical traffic due to highly dynamical
patterns of human activities. Most existing methods explore such relations by
fusing spatial and temporal features extracted from multi-source data. However,
they neglect position information which helps distinguish patterns on different
positions. In this paper, we propose a position-aware neural network that
integrates data features and position information. Our approach employs the
inception backbone network to capture rich features of traffic distribution on
the whole area. The novelty lies in that under the backbone network, we apply
position embedding technique used in neural language processing to represent
position information as embedding vectors which are learned during the
training. With these embedding vectors, we design position-aware convolution
which allows different kernels to process features of different positions.
Extensive experiments on two real-world datasets show that our approach
outperforms previous methods even with fewer data sources
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
Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network
With the rapid development of mobile-internet technologies, on-demand
ride-sourcing services have become increasingly popular and largely reshaped
the way people travel. Demand prediction is one of the most fundamental
components in supply-demand management systems of ride-sourcing platforms. With
accurate short-term prediction for origin-destination (OD) demand, the
platforms make precise and timely decisions on real-time matching, idle vehicle
reallocations and ride-sharing vehicle routing, etc. Compared to zone-based
demand prediction that has been examined by many previous studies, OD-based
demand prediction is more challenging. This is mainly due to the complicated
spatial and temporal dependencies among demand of different OD pairs. To
overcome this challenge, we propose the Spatio-Temporal Encoder-Decoder
Residual Multi-Graph Convolutional network (ST-ED-RMGC), a novel deep learning
model for predicting ride-sourcing demand of various OD pairs. Firstly, the
model constructs OD graphs, which utilize adjacent matrices to characterize the
non-Euclidean pair-wise geographical and semantic correlations among different
OD pairs. Secondly, based on the constructed graphs, a residual multi-graph
convolutional (RMGC) network is designed to encode the contextual-aware spatial
dependencies, and a long-short term memory (LSTM) network is used to encode the
temporal dependencies, into a dense vector space. Finally, we reuse the RMGC
networks to decode the compressed vector back to OD graphs and predict the
future OD demand. Through extensive experiments on the for-hire-vehicles
datasets in Manhattan, New York City, we show that our proposed deep learning
framework outperforms the state-of-arts by a significant margin
Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
Short-term demand forecasting models commonly combine convolutional and
recurrent layers to extract complex spatiotemporal patterns in data. Long-term
histories are also used to consider periodicity and seasonality patterns as
time series data. In this study, we propose an efficient architecture,
Temporal-Guided Network (TGNet), which utilizes graph networks and
temporal-guided embedding. Graph networks extract invariant features to
permutations of adjacent regions instead of convolutional layers.
Temporal-guided embedding explicitly learns temporal contexts from training
data and is substituted for the input of long-term histories from days/weeks
ago. TGNet learns an autoregressive model, conditioned on temporal contexts of
forecasting targets from temporal-guided embedding. Finally, our model achieves
competitive performances with other baselines on three spatiotemporal demand
dataset from real-world, but the number of trainable parameters is about 20
times smaller than a state-of-the-art baseline. We also show that
temporal-guided embedding learns temporal contexts as intended and TGNet has
robust forecasting performances even to atypical event situations.Comment: NeurIPS 2018 Workshop on Modeling and Decision-Making in the
Spatiotemporal Domai
Multi-graph convolutional network for short-term passenger flow forecasting in urban rail transit
Short-term passenger flow forecasting is a crucial task for urban rail
transit operations. Emerging deep-learning technologies have become effective
methods used to overcome this problem. In this study, the authors propose a
deep-learning architecture called Conv-GCN that combines a graph convolutional
network (GCN) and a three-dimensional (3D) convolutional neural network (3D
CNN). First, they introduce a multi-graph GCN to deal with three inflow and
outflow patterns (recent, daily, and weekly) separately. Multi-graph GCN
networks can capture spatiotemporal correlations and topological information
within the entire network. A 3D CNN is then applied to deeply integrate the
inflow and outflow information. High-level spatiotemporal features between
different inflow and outflow patterns and between stations that are nearby and
far away can be extracted by 3D CNN. Finally, a fully connected layer is used
to output results. The Conv-GCN model is evaluated on smart card data of the
Beijing subway under the time interval of 10, 15, and 30 min. Results show that
this model yields the best performance compared with seven other models. In
terms of the root-mean-square errors, the performances under three time
intervals have been improved by 9.402, 7.756, and 9.256%, respectively. This
study can provide critical insights for subway operators to optimise urban rail
transit operations.Comment: This paper has been published in IET Intelligent Transport Systems,
202
Short-term Traffic Prediction with Deep Neural Networks: A Survey
In modern transportation systems, an enormous amount of traffic data is
generated every day. This has led to rapid progress in short-term traffic
prediction (STTP), in which deep learning methods have recently been applied.
In traffic networks with complex spatiotemporal relationships, deep neural
networks (DNNs) often perform well because they are capable of automatically
extracting the most important features and patterns. In this study, we survey
recent STTP studies applying deep networks from four perspectives. 1) We
summarize input data representation methods according to the number and type of
spatial and temporal dependencies involved. 2) We briefly explain a wide range
of DNN techniques from the earliest networks, including Restricted Boltzmann
Machines, to the most recent, including graph-based and meta-learning networks.
3) We summarize previous STTP studies in terms of the type of DNN techniques,
application area, dataset and code availability, and the type of the
represented spatiotemporal dependencies. 4) We compile public traffic datasets
that are popular and can be used as the standard benchmarks. Finally, we
suggest challenging issues and possible future research directions in STTP
Urban flows prediction from spatial-temporal data using machine learning: A survey
Urban spatial-temporal flows prediction is of great importance to traffic
management, land use, public safety, etc. Urban flows are affected by several
complex and dynamic factors, such as patterns of human activities, weather,
events and holidays. Datasets evaluated the flows come from various sources in
different domains, e.g. mobile phone data, taxi trajectories data, metro/bus
swiping data, bike-sharing data and so on. To summarize these methodologies of
urban flows prediction, in this paper, we first introduce four main factors
affecting urban flows. Second, in order to further analysis urban flows, a
preparation process of multi-sources spatial-temporal data related with urban
flows is partitioned into three groups. Third, we choose the spatial-temporal
dynamic data as a case study for the urban flows prediction task. Fourth, we
analyze and compare some well-known and state-of-the-art flows prediction
methods in detail, classifying them into five categories: statistics-based,
traditional machine learning-based, deep learning-based, reinforcement
learning-based and transfer learning-based methods. Finally, we give open
challenges of urban flows prediction and an outlook in the future of this
field. This paper will facilitate researchers find suitable methods and open
datasets for addressing urban spatial-temporal flows forecast problems
A Survey on Deep Learning for Human Mobility
The study of human mobility is crucial due to its impact on several aspects
of our society, such as disease spreading, urban planning, well-being,
pollution, and more. The proliferation of digital mobility data, such as phone
records, GPS traces, and social media posts, combined with the predictive power
of artificial intelligence, triggered the application of deep learning to human
mobility. Existing surveys focus on single tasks, data sources, mechanistic or
traditional machine learning approaches, while a comprehensive description of
deep learning solutions is missing. This survey provides a taxonomy of mobility
tasks, a discussion on the challenges related to each task and how deep
learning may overcome the limitations of traditional models, a description of
the most relevant solutions to the mobility tasks described above and the
relevant challenges for the future. Our survey is a guide to the leading deep
learning solutions to next-location prediction, crowd flow prediction,
trajectory generation, and flow generation. At the same time, it helps deep
learning scientists and practitioners understand the fundamental concepts and
the open challenges of the study of human mobility
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