One fundamental issue in managing bike sharing systems is the bike flow prediction. Due to the hardness of predicting the flow for a single station, recent research works often predict the bike flow at cluster-level. While such studies gain satisfactory prediction accuracy, they cannot directly guide some fine-grained …
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48 results for “bike flow estimation”
Dockless bike sharing systems need effective bike flow prediction models.
problem Imbalanced and dynamic use of bikes leads to mandatory rebalancing operations.
method Divide urban area into regions, model spatio-temporal bike flows, extract traffic patterns, and predict bike flows.
result Interpretable bike flow prediction model provides valuable insights into bike flow analysis.
Paper presents LSTMMDN for hourly bike flow estimation in Copenhagen.
problem Sparse or unavailable hourly bike flow data for safety analysis.
method Hybrid LSTM MDN model for hourly bike flow estimation.
result 66-77% more accurate bike flow estimates compared to calibration factors.
Bike usage in Smart Cities becomes paramount for sustainable urban development. Cycling provides tremendous opportunities for a more healthy lifestyle, lower energy consumption and carbon emissions as well as reduction of traffic jams. While the number of cyclists increase along with the expansion of bike sharing initi…
Study improves cross-modal bike-share and transit demand prediction.
problem Cross-modal ripple effects in urban transportation demand.
method Transfer learning and stacked LSTM models for cross-modal demand prediction.
result Transfer learning models outperform unimodal models in cross-modal demand prediction.
This study analyzes how weather impacts bike sharing usage in Washington D.C.
problem Understanding how weather affects bike sharing usage patterns.
method Gathered bike usage and weather data, used k-means clustering algorithm to identify clusters.
result Weather significantly impacts bike usage, with temperature and precipitation being the most influential factors.
Proposes AtCoR for predicting bike station usage, improving station network reconfiguration.
problem Challenges in predicting new bike stations due to lack of historical data.
method AtCoR algorithm that predicts both existing and new bike stations using station-centered heatmaps and historical correlations.
result AtCoR outperforms existing models in predicting bike station usage.
In recent years, dock-less shared bikes have been widely spread across many cities in China and facilitate people's lives. However, at the same time, it also raises many problems about dock-less shared bike management due to the mismatching between demands and real distribution of bikes. Before deploying dock-less shar…
Paper improves bike-sharing demand prediction by adapting to changing patterns.
problem Improving bike-sharing demand prediction under temporal domain shifts.
method Gen-ROTDA, a robust optimal transport-guided residual domain adaptation framework.
result Gen-ROTDA achieves the lowest MAE and is the best OT-family method on average.
Sub-Riemannian geometry connects bike paths to mathematical curves.
problem Understanding bike paths and their mathematical properties.
method Relating sub-Riemannian geometry to bicycle motion and curve shapes.
result Geodesics in sub-Riemannian geometry correspond to specific bike paths.
Dataset of 4500 bicycle designs aids in design analysis and synthesis.
problem Identify gaps in bicycle market and design space, classify bicycles, synthesize new designs.
method Processed dataset, unsupervised dimensionality reduction, supervised classification, machine learning synthesis.
result Identified design parameters and factors influencing bicycle classification.
This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored; G…
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 dom…
Estimator improves prediction with missing data in multi-environment settings.
problem Handling missing data in multi-environment settings for robust prediction.
method Derive an estimator from invariance objective under missing outcomes.
result The estimator achieves lower prediction error despite using a biased imputation model.
MCD automates counterfactual design searches for multi-modal tasks.
problem Designing for multi-objective goals and complex constraints.
method Model-agnostic counterfactual search method for multi-modal design modifications.
result MCD streamlines and automates counterfactual search, recommending effective design modifications.
Proposes a new model for more accurate demand forecasting considering dynamic contextual information.
problem Traditional methods fail to capture spatio-temporal and dynamic contextual dependencies in demand forecasting.
method Integrates temporal, relational, spatial, and dynamic contextual dependencies using a Context Integrated Graph Neural Network (CIGNN).
result CIGNN outperforms state-of-the-art baselines in multi-step ahead demand forecasting.
This master thesis focuses on practical application of Convolutional Neural Network models on the task of road labeling with bike attractivity score. We start with an abstraction of real world locations into nodes and scored edges in partially annotated dataset. We enhance information available about each edge with pho…
We utilize Wi-Fi communications from smartphones to predict their mobility mode, i.e. walking, biking and driving. Wi-Fi sensors were deployed at four strategic locations in a closed loop on streets in downtown Toronto. Deep neural network (Multilayer Perceptron) along with three decision tree based classifiers (Decisi…
The paper optimizes sensor selection for network time series data.
problem Optimizing sensor selection for network time series data with minimal error.
method Data-driven strategies to turn off sensors or select a sampling set of nodes.
result Proposes and compares various data-driven strategies for sensor selection.
The design of personalized incentives or recommendations to improve user engagement is gaining prominence as digital platform providers continually emerge. We propose a multi-armed bandit framework for matching incentives to users, whose preferences are unknown a priori and evolving dynamically in time, in a resource c…
The paper proposes a new auto-regressive model for multivariate distributional time series.
problem Statistical analysis of multivariate time series of probability measures.
method Wasserstein space, auto-regressive model, iterated random function systems.
result Consistent estimator for auto-regressive coefficients with sparse structure.
The paper proposes a new model to better estimate demand from censored data.
problem Challenges in inferring true demand from aggregate, censored data.
method Combines Tobit likelihood with graph diffusion process in Gaussian Processes.
result The new model produces more accurate out-of-sample predictions.
Assessment of mental workload in real-world conditions is key to ensure the performance of workers executing tasks that demand sustained attention. Previous literature has employed electroencephalography (EEG) to this end despite having observed that EEG correlates of mental workload vary across subjects and physical s…
This paper designs sensor arrays for estimating unsteady flows efficiently.
problem Estimating high-dimensional unsteady flow fields with limited sensor placement.
method Combines data-driven modeling, Kalman Filter design, and sparsification for sensor selection.
result Proposed sensor arrays are highly effective for flow-field estimation across various conditions.
Paper proves curvature estimates for a specific flow on Kähler manifolds.
problem Proving local curvature estimates for a specific flow on Kähler manifolds.
method Proves local curvature estimates for the -LYZ flow over Kähler manifolds.
result Generalizes the long time existence of the flow.
Extends heat kernel estimates for super Ricci flow.
problem Heat kernel estimates for super Ricci flow.
method Generalizes Bamler-Zhang's geometric analysis to super Ricci flow.
result Obtains Gaussian heat kernel estimates for super Ricci flow.
Sharp estimate for flow in any dimension.
problem Interior gradient estimate for graphical mean curvature flow.
method Proving sharp interior gradient estimate for area decreasing graphical mean curvature flow in arbitrary codimension.
result Generalized result in arbitrary codimension.
Quantitative estimate for curvature in mean curvature flow.
problem Estimating curvature in mean curvature flow.
method Proving a curvature estimate for smooth convex ancient flows.
result Curvature grows at most quadratically in terms of rescaled extrinsic distance.
Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks. Training of the so-called FlowNet was enabled by a large synthetically generated dataset. The present paper extends the concept of optical flow estimation via co…
Estimates mean curvature flow with geometric bounds.
problem Controlling mean curvature flow dynamics.
method Pointwise estimate using initial geometry and jHAj bound.
result Extension theorem and blowup rate estimate of HA.
We simplify and improve the curvature estimates in the paper: On the conditions to extend Ricci flow(II). Furthermore, we develop some volume estimates for the Ricci flow with bounded scalar curvature. These estimates can be applied to study the singularities of the Ricci flow and convergence properties of the Kähler R…
Hamilton and Souplet-Zhang type estimations on semilinear parabolic system along geometric flowmath.DG
The article derives gradient estimations for semilinear equations on geometric flows.
problem Gradient estimation for semilinear equations on geometric flows.
method Derives both Hamilton and Souplet-Zhang type gradient estimations.
result Gradient estimations for semilinear equations on geometric flows.
Proves local noncollapsing estimate for mean curvature flow.
problem Ensuring noncollapsing in mean curvature flow.
method Combining local estimate with earlier work on ancient solutions.
result Ancient convex solutions that sweep out entire space are noncollapsed.
Proves estimates for Kähler-Ricci flow solutions.
problem Positive solutions to Kähler-Ricci flow.
method Matrix Li-Yau-Hamilton estimates coupled with flow.
result Monotonicity formula derived.
The study provides interior estimates for -flows and translators in .
problem Estimating -flows and translators in .
method Proved interior gradient and second order estimates.
result Non-existence of -translators asymptotic to .
Study high codimension mean curvature flow in Riemannian manifolds, proving limiting flow in Euclidean space.
problem Analyzing mean curvature flow in high codimension Riemannian manifolds.
method Establishing codimension estimate, using quadratic pinching condition, gradient estimates.
result Existence of limiting flow in Euclidean space under cylindrical pinching condition.
Eigenvalue estimate for shrinkers in mean curvature flow.
problem Eigenvalue estimates on shrinkers for mean curvature flow.
method Generalized earlier work of Ding and Xin to noncompact cases.
result Eigenvalue estimate holds on every properly embedded shrinker.
New rigidity estimate derived via harmonic map flow.
problem Rigidity of maps from to .
method Harmonic map flow approach.
result Rigidity estimate derived.
The paper improves curvature estimates for Ricci flow solutions with bounded scalar curvature.
problem Proving curvature estimates for Ricci flow solutions with bounded scalar curvature.
method Localised weighted curvature integral estimates for solutions to Ricci flow.
result Integral curvature estimates imply a uniform bound on the spatial norm of the Riemannian curvature tensor.
Paper studies Laplace operator estimates in harmonic map heat flows.
problem Estimating Laplace operator in harmonic map heat flows outside singularities.
method Investigates estimates using spherical coordinates for and boundary conditions.
result Provides higher-order estimates for the Ericksen--Leslie system.
We prove several sharp one-sided pinching estimates for immersed and embedded hypersurfaces evolving by various fully nonlinear, one-homogeneous curvature flows by the method of Stampacchia iteration. These include sharp estimates for the largest principal curvature and the inscribed curvature ('cylindrical estimates')…
Develops local curvature estimates for mean curvature flow.
problem Sharp curvature pinching estimates for mean curvature flow.
method Local version of Huisken-Stampacchia iteration.
result Local curvature estimates do not depend on noncollapsing quality.
In this paper, by maximum principle and cutoff function, we investigate gradient estimates for positive solutions to two nonlinear parabolic equations under Ricci flow. The related Harnack inequalities are deduced. An result about positive solutions on closed manifolds under Ricci flow is abtained. As applications, gra…
Localizes curvature estimates for evolving hypersurfaces under various flows.
problem Establishing curvature estimates for evolving hypersurfaces under different flow conditions.
method Adapted localization of Huisken--Stampacchia iteration method to fully nonlinear flows.
result Asymptotically sharp curvature pinching estimates for general flows.
Kernelised Normalising Flowsstat.ML
Kernelised flows improve density estimation and generation with fewer parameters.
problem Limited expressiveness of flow-based models due to invertibility constraints.
method Integrates kernels into normalising flows to enhance expressiveness and efficiency.
result Kernelised flows outperform neural network-based flows in parameter efficiency and low-data scenarios.
Gradient estimates for a parabolic partial differential equation under the Ricci-Bourguignon flowmath.DG
Gradient estimates for a parabolic PDE under Ricci-Bourguignon flow on warped product manifolds.
problem Analyzing the Ricci-Bourguignon flow on warped product manifolds.
method Establishing gradient estimates for a parabolic partial differential equation.
result Gradient estimates for the parabolic PDE provide analytic input for geometric applications.
Proposes a new method for high-dimensional density estimation.
problem Estimating high-dimensional probability density functions efficiently.
method Tensorizing flow method combining tensor-train and flow-based generative modeling.
result Efficiently constructs an approximate density in tensor-train form and trains a flow model to match empirical distribution.
Generative model for condensed matter using Riemannian flow matching.
problem Sampling equilibrium distributions in condensed-phase systems.
method Riemannian flow matching to incorporate periodicity, using Hutchinson's trace estimator and cumulant expansion for bias correction.
result Highly accurate free energy estimates on monatomic ice without multistage estimators.