17,218 research outputs found
Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services
This study develops an online predictive optimization framework for
dynamically operating a transit service in an area of crowd movements. The
proposed framework integrates demand prediction and supply optimization to
periodically redesign the service routes based on recently observed demand. To
predict demand for the service, we use Quantile Regression to estimate the
marginal distribution of movement counts between each pair of serviced
locations. The framework then combines these marginals into a joint demand
distribution by constructing a Gaussian copula, which captures the structure of
correlation between the marginals. For supply optimization, we devise a linear
programming model, which simultaneously determines the route structure and the
service frequency according to the predicted demand. Importantly, our framework
both preserves the uncertainty structure of future demand and leverages this
for robust route optimization, while keeping both components decoupled. We
evaluate our framework using a real-world case study of autonomous mobility in
a university campus in Denmark. The results show that our framework often
obtains the ground truth optimal solution, and can outperform conventional
methods for route optimization, which do not leverage full predictive
distributions.Comment: 34 pages, 12 figures, 5 table
Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction
Effective long-term predictions have been increasingly demanded in urban-wise
data mining systems. Many practical applications, such as accident prevention
and resource pre-allocation, require an extended period for preparation.
However, challenges come as long-term prediction is highly error-sensitive,
which becomes more critical when predicting urban-wise phenomena with
complicated and dynamic spatial-temporal correlation. Specifically, since the
amount of valuable correlation is limited, enormous irrelevant features
introduce noises that trigger increased prediction errors. Besides, after each
time step, the errors can traverse through the correlations and reach the
spatial-temporal positions in every future prediction, leading to significant
error propagation. To address these issues, we propose a Dynamic
Switch-Attention Network (DSAN) with a novel Multi-Space Attention (MSA)
mechanism that measures the correlations between inputs and outputs explicitly.
To filter out irrelevant noises and alleviate the error propagation, DSAN
dynamically extracts valuable information by applying self-attention over the
noisy input and bridges each output directly to the purified inputs via
implementing a switch-attention mechanism. Through extensive experiments on two
spatial-temporal prediction tasks, we demonstrate the superior advantage of
DSAN in both short-term and long-term predictions.Comment: 11 pages, an ACM SIGKDD 2020 pape
Node Embedding over Temporal Graphs
In this work, we present a method for node embedding in temporal graphs. We
propose an algorithm that learns the evolution of a temporal graph's nodes and
edges over time and incorporates this dynamics in a temporal node embedding
framework for different graph prediction tasks. We present a joint loss
function that creates a temporal embedding of a node by learning to combine its
historical temporal embeddings, such that it optimizes per given task (e.g.,
link prediction). The algorithm is initialized using static node embeddings,
which are then aligned over the representations of a node at different time
points, and eventually adapted for the given task in a joint optimization. We
evaluate the effectiveness of our approach over a variety of temporal graphs
for the two fundamental tasks of temporal link prediction and multi-label node
classification, comparing to competitive baselines and algorithmic
alternatives. Our algorithm shows performance improvements across many of the
datasets and baselines and is found particularly effective for graphs that are
less cohesive, with a lower clustering coefficient
- …