11 research outputs found
Inductive Graph Neural Networks for Spatiotemporal Kriging
Time series forecasting and spatiotemporal kriging are the two most important
tasks in spatiotemporal data analysis. Recent research on graph neural networks
has made substantial progress in time series forecasting, while little
attention has been paid to the kriging problem -- recovering signals for
unsampled locations/sensors. Most existing scalable kriging methods (e.g.,
matrix/tensor completion) are transductive, and thus full retraining is
required when we have a new sensor to interpolate. In this paper, we develop an
Inductive Graph Neural Network Kriging (IGNNK) model to recover data for
unsampled sensors on a network/graph structure. To generalize the effect of
distance and reachability, we generate random subgraphs as samples and
reconstruct the corresponding adjacency matrix for each sample. By
reconstructing all signals on each sample subgraph, IGNNK can effectively learn
the spatial message passing mechanism. Empirical results on several real-world
spatiotemporal datasets demonstrate the effectiveness of our model. In
addition, we also find that the learned model can be successfully transferred
to the same type of kriging tasks on an unseen dataset. Our results show that:
1) GNN is an efficient and effective tool for spatial kriging; 2) inductive
GNNs can be trained using dynamic adjacency matrices; 3) a trained model can be
transferred to new graph structures and 4) IGNNK can be used to generate
virtual sensors.Comment: AAAI 202
Fairness-enhancing deep learning for ride-hailing demand prediction
Short-term demand forecasting for on-demand ride-hailing services is one of
the fundamental issues in intelligent transportation systems. However, previous
travel demand forecasting research predominantly focused on improving
prediction accuracy, ignoring fairness issues such as systematic
underestimations of travel demand in disadvantaged neighborhoods. This study
investigates how to measure, evaluate, and enhance prediction fairness between
disadvantaged and privileged communities in spatial-temporal demand forecasting
of ride-hailing services. A two-pronged approach is taken to reduce the demand
prediction bias. First, we develop a novel deep learning model architecture,
named socially aware neural network (SA-Net), to integrate the
socio-demographics and ridership information for fair demand prediction through
an innovative socially-aware convolution operation. Second, we propose a
bias-mitigation regularization method to mitigate the mean percentage
prediction error gap between different groups. The experimental results,
validated on the real-world Chicago Transportation Network Company (TNC) data,
show that the de-biasing SA-Net can achieve better predictive performance in
both prediction accuracy and fairness. Specifically, the SA-Net improves
prediction accuracy for both the disadvantaged and privileged groups compared
with the state-of-the-art models. When coupled with the bias mitigation
regularization method, the de-biasing SA-Net effectively bridges the mean
percentage prediction error gap between the disadvantaged and privileged
groups, and also protects the disadvantaged regions against systematic
underestimation of TNC demand. Our proposed de-biasing method can be adopted in
many existing short-term travel demand estimation models, and can be utilized
for various other spatial-temporal prediction tasks such as crime incidents
predictions
Uncertainty Quantification in the Road-Level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN) (Short Paper)
Urban road-based risk prediction is a crucial yet challenging aspect of research in transportation safety. While most existing studies emphasize accurate prediction, they often overlook the importance of model uncertainty. In this paper, we introduce a novel Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) for road-level traffic risk prediction, with a focus on uncertainty quantification. Our case study, conducted in the Lambeth borough of London, UK, demonstrates the superior performance of our approach in comparison to existing methods. Although the negative binomial distribution may not be the most suitable choice for handling real, non-binary risk levels, our work lays a solid foundation for future research exploring alternative distribution models or techniques. Ultimately, the STZINB-GNN contributes to enhanced transportation safety and data-driven decision-making in urban planning by providing a more accurate and reliable framework for road-level traffic risk prediction and uncertainty quantification
Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction
Predicting traffic incident risks at granular spatiotemporal levels is
challenging. The datasets predominantly feature zero values, indicating no
incidents, with sporadic high-risk values for severe incidents. Notably, a
majority of current models, especially deep learning methods, focus solely on
estimating risk values, overlooking the uncertainties arising from the
inherently unpredictable nature of incidents. To tackle this challenge, we
introduce the Spatiotemporal Zero-Inflated Tweedie Graph Neural Networks
(STZITD-GNNs). Our model merges the reliability of traditional statistical
models with the flexibility of graph neural networks, aiming to precisely
quantify uncertainties associated with road-level traffic incident risks. This
model strategically employs a compound model from the Tweedie family, as a
Poisson distribution to model risk frequency and a Gamma distribution to
account for incident severity. Furthermore, a zero-inflated component helps to
identify the non-incident risk scenarios. As a result, the STZITD-GNNs
effectively capture the dataset's skewed distribution, placing emphasis on
infrequent but impactful severe incidents. Empirical tests using real-world
traffic data from London, UK, demonstrate that our model excels beyond current
benchmarks. The forte of STZITD-GNN resides not only in its accuracy but also
in its adeptness at curtailing uncertainties, delivering robust predictions
over short (7 days) and extended (14 days) timeframes
Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction
Understanding Origin-Destination (O-D) travel demand is crucial for transportation management. However, traditional spatialtemporal deep learning models grapple with addressing the sparse
and long-tail characteristics in high-resolution O-D matrices and
quantifying prediction uncertainty. This dilemma arises from the
numerous zeros and over-dispersed demand patterns within these
matrices, which challenge the Gaussian assumption inherent to
deterministic deep learning models. To address these challenges,
we propose a novel approach: the Spatial-Temporal Tweedie Graph
Neural Network (STTD). The STTD introduces the Tweedie distribution as a compelling alternative to the traditional ’zero-inflated’
model and leverages spatial and temporal embeddings to parameterize travel demand distributions. Our evaluations using realworld datasets highlight STTD’s superiority in providing accurate
predictions and precise confidence intervals, particularly in highresolution scenarios. GitHub code is available online
From compound word to metropolitan station: Semantic similarity analysis using smart card data
Rapid urbanization and modern civilization require sound integration with public transportation systems. In the same time, the volume and complexity of public transportation network are increasing, making it harder to understand the public transportation dynamics. As a first step, understanding the similarity among subway stations is imperative. In this paper, we proposed a semantic framework inspired from natural language processing (NLP) to interpret subway stations as compound words. Specifically, we transplanted context and literal meaning of compound words into mobility and location attributes of stations. Using smart card data, we trained stacked autoencoders (SAE) with designed flow matrices as an embedding method to learn the mobility attributes. Subsequently, to discover the location attributes, we have applied affinity propagation clustering to classify 9 point of interest (POI) categories. Combined with urban planning knowledge, we manage to comprehend the land use meanings of 9 POI clusters. The location semantics is chosen from those categories reflecting its urban land use pattern. By choose meaningful combination of mobility and location semantics for stations’ similarity case studies, we summarized potential applications of this semantic framework
ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks
Traffic data serves as a fundamental component in both research and
applications within intelligent transportation systems. However, real-world
transportation data, collected from loop detectors or similar sources, often
contain missing values (MVs), which can adversely impact associated
applications and research. Instead of discarding this incomplete data,
researchers have sought to recover these missing values through numerical
statistics, tensor decomposition, and deep learning techniques. In this paper,
we propose an innovative deep-learning approach for imputing missing data. A
graph attention architecture is employed to capture the spatial correlations
present in traffic data, while a bidirectional neural network is utilized to
learn temporal information. Experimental results indicate that our proposed
method outperforms all other benchmark techniques, thus demonstrating its
effectiveness.Comment: In submission to IEEE-ITSC 202
The Braess Paradox in Dynamic Traffic
The Braess's Paradox (BP) is the observation that adding one or more roads to
the existing road network will counter-intuitively increase traffic congestion
and slow down the overall traffic flow. Previously, the existence of the BP is
modeled using the static traffic assignment model, which solves for the user
equilibrium subject to network flow conservation to find the equilibrium state
and distributes all vehicles instantaneously. Such approach neglects the
dynamic nature of real-world traffic, including vehicle behaviors and the
interaction between vehicles and the infrastructure. As such, this article
proposes a dynamic traffic network model and empirically validates the
existence of the BP under dynamic traffic. In particular, we use
microsimulation environment to study the impacts of an added path on a grid
network. We explore how the network flow, vehicle travel time, and network
capacity respond, as well as when the BP will occur.Comment: Accepted by 2022 IEEE Intelligent Transportation Systems Conference
(ITSC): https://ieeexplore.ieee.org/abstract/document/992199