127 research outputs found
Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development
Recently, practical applications for passenger flow prediction have brought
many benefits to urban transportation development. With the development of
urbanization, a real-world demand from transportation managers is to construct
a new metro station in one city area that never planned before. Authorities are
interested in the picture of the future volume of commuters before constructing
a new station, and estimate how would it affect other areas. In this paper,
this specific problem is termed as potential passenger flow (PPF) prediction,
which is a novel and important study connected with urban computing and
intelligent transportation systems. For example, an accurate PPF predictor can
provide invaluable knowledge to designers, such as the advice of station scales
and influences on other areas, etc. To address this problem, we propose a
multi-view localized correlation learning method. The core idea of our strategy
is to learn the passenger flow correlations between the target areas and their
localized areas with adaptive-weight. To improve the prediction accuracy, other
domain knowledge is involved via a multi-view learning process. We conduct
intensive experiments to evaluate the effectiveness of our method with
real-world official transportation datasets. The results demonstrate that our
method can achieve excellent performance compared with other available
baselines. Besides, our method can provide an effective solution to the
cold-start problem in the recommender system as well, which proved by its
outperformed experimental results
STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph
The key to traffic prediction is to accurately depict the temporal dynamics
of traffic flow traveling in a road network, so it is important to model the
spatial dependence of the road network. The essence of spatial dependence is to
accurately describe how traffic information transmission is affected by other
nodes in the road network, and the GNN-based traffic prediction model, as a
benchmark for traffic prediction, has become the most common method for the
ability to model spatial dependence by transmitting traffic information with
the message passing mechanism. However, existing methods model a local and
static spatial dependence, which cannot transmit the global-dynamic traffic
information (GDTi) required for long-term prediction. The challenge is the
difficulty of detecting the precise transmission of GDTi due to the uncertainty
of individual transport, especially for long-term transmission. In this paper,
we propose a new hypothesis\: GDTi behaves macroscopically as a transmitting
causal relationship (TCR) underlying traffic flow, which remains stable under
dynamic changing traffic flow. We further propose spatial-temporal Granger
causality (STGC) to express TCR, which models global and dynamic spatial
dependence. To model global transmission, we model the causal order and causal
lag of TCRs global transmission by a spatial-temporal alignment algorithm. To
capture dynamic spatial dependence, we approximate the stable TCR underlying
dynamic traffic flow by a Granger causality test. The experimental results on
three backbone models show that using STGC to model the spatial dependence has
better results than the original model for 45 min and 1 h long-term prediction.Comment: 14 pages, 16 figures, 4 table
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
Sparsity-aware neural user behavior modeling in online interaction platforms
Modern online platforms offer users an opportunity to participate in a variety of content-creation, social networking, and shopping activities. With the rapid proliferation of such online services, learning data-driven user behavior models is indispensable to enable personalized user experiences. Recently, representation learning has emerged as an effective strategy for user modeling, powered by neural networks trained over large volumes of interaction data. Despite their enormous potential, we encounter the unique challenge of data sparsity for a vast majority of entities, e.g., sparsity in ground-truth labels for entities and in entity-level interactions (cold-start users, items in the long-tail, and ephemeral groups).
In this dissertation, we develop generalizable neural representation learning frameworks for user behavior modeling designed to address different sparsity challenges across applications. Our problem settings span transductive and inductive learning scenarios, where transductive learning models entities seen during training and inductive learning targets entities that are only observed during inference. We leverage different facets of information reflecting user behavior (e.g., interconnectivity in social networks, temporal and attributed interaction information) to enable personalized inference at scale. Our proposed models are complementary to concurrent advances in neural architectural choices and are adaptive to the rapid addition of new applications in online platforms.
First, we examine two transductive learning settings: inference and recommendation in graph-structured and bipartite user-item interactions. In chapter 3, we formulate user profiling in social platforms as semi-supervised learning over graphs given sparse ground-truth labels for node attributes. We present a graph neural network framework that exploits higher-order connectivity structures (network motifs) to learn attributed structural roles of nodes that identify structurally similar nodes with co-varying local attributes. In chapter 4, we design neural collaborative filtering models for few-shot recommendations over user-item interactions. To address item interaction sparsity due to heavy-tailed distributions, our proposed meta-learning framework learns-to-recommend few-shot items by knowledge transfer from arbitrary base recommenders. We show that our framework consistently outperforms state-of-art approaches on overall recommendation (by 5% Recall) while achieving significant gains (of 60-80% Recall) for tail items with fewer than 20 interactions.
Next, we explored three inductive learning settings: modeling spread of user-generated content in social networks; item recommendations for ephemeral groups; and friend ranking in large-scale social platforms. In chapter 5, we focus on diffusion prediction in social networks where a vast population of users rarely post content. We introduce a deep generative modeling framework that models users as probability distributions in the latent space with variational priors parameterized by graph neural networks. Our approach enables massive performance gains (over 150% recall) for users with sparse activities while being faster than state-of-the-art neural models by an order of magnitude. In chapter 6, we examine item recommendations for ephemeral groups with limited or no historical interactions together. To overcome group interaction sparsity, we present self-supervised learning strategies that exploit the preference co-variance in observed group memberships for group recommender training. Our framework achieves significant performance gains (over 30% NDCG) over prior state-of-the-art group recommendation models. In chapter 7, we introduce multi-modal inference with graph neural networks that captures knowledge from multiple feature modalities and user interactions for multi-faceted friend ranking. Our approach achieves notable higher performance gains for critical populations of less-active and low degree users
Fundamentals
Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters
Fundamentals
Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters
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