The paper analyzes vehicle encounters using driving primitives.
problem Understanding complex vehicle encounters for autonomous driving.
method Decompose driving data into primitives using nonparametric Bayesian learning.
result More than 4000 driving primitives identified from 976 encounters.
We propose a primitive called PJOIN, for "predictive join," which combines and extends the operations JOIN and LINK, which Valiant proposed as the basis of a computational theory of cortex. We show that PJOIN can be implemented in Valiant's model. We also show that, using PJOIN, certain reasonably complex learning and …
This paper presents a novel context-based approach for pedestrian motion prediction in crowded, urban intersections, with the additional flexibility of prediction in similar, but new, environments. Previously, Chen et. al. combined Markovian-based and clustering-based approaches to learn motion primitives in a grid-bas…
Berge introduced knots that are primitive/primitive with respect to the genus 2 Heegaard surface, F, in S3; surgery on such knots at the surface slope yields a lens space. Later Dean described a similar class of knots that are primitive/Seifert with respect to F; surgery on these knots at the surface slope yield…
The twisted torus knots lie on the standard genus 2 Heegaard surface for S3, as do the primitive/primitive and primitive/Seifert knots. It is known that primitive/primitive knots are fibered, and that not all primitive/Seifert knots are fibered. Since there is a wealth of primitive/Seifert knots that are twisted tor…
Primitive curves in handlebodies form a connected complex.
problem Understanding the structure of curves in handlebodies.
method Defining and analyzing primitive curves and constructing sequences between them.
result The primitive curve complex for a handlebody is connected.
Note on connectedness of primitive disk complex.
problem Whether primitive disk complex is connected for genus > 3 Heegaard splittings.
method Defined and quotiented primitive disk complex to prove connectedness.
result Homotopy primitive disk complex is connected.
Project aims to reduce traffic congestion in Singapore using CNNs.
problem Traffic congestion in Singapore.
method Convolutional Neural Networks (CNNs) for traffic density estimation; traffic signal control algorithms.
result CNNs effectively estimate traffic density from images, leading to improved traffic control.
Study primitive cohomology in symplectic manifolds.
problem Understanding primitive cohomology in symplectic geometry.
method Reviewing superbundle-valued forms and proving a transgression formula.
result Introduced primitive characteristic classes and proved a transgression formula.
Disk surgery on primitive disks of genus-3 Heegaard splittings of 3-sphere yields no primitive disks.
problem Characterizing primitive disks in genus-3 Heegaard splittings of 3-sphere.
method Analyzing the effect of disk surgery on primitive disks in genus-3 Heegaard splittings of 3-sphere.
result Primitive disks in genus-3 Heegaard splittings of 3-sphere are not weakly closed under disk surgery.
Critical to evaluating the capacity, scalability, and availability of web systems are realistic web traffic generators. Web traffic generation is a classic research problem, no generator accounts for the characteristics of web robots or crawlers that are now the dominant source of traffic to a web server. Administrator…
TM-CNN predicts lane-level traffic speeds considering volume impact.
problem Aggregated lane-level traffic speed prediction and volume impact.
method Two-stream multi-channel CNN, data conversion, two-stream deep neural network, loss function.
result TM-CNN outperforms existing models in multi-lane traffic speed prediction.
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph C…
No primitive Teichmüller curves found in Prym(2,2).
problem Existence of primitive Teichmüller curves in Prym(2,2).
method Completed work by Lanneau and Möller.
result No primitive Teichmüller curves in Prym(2,2).
T-GCN predicts traffic using neural networks for spatial and temporal data.
problem Accurate real-time traffic forecasting in urban networks.
method Combines GCN for spatial and GRU for temporal data analysis.
result T-GCN outperforms state-of-the-art baselines on real-world traffic datasets.
Model predicts traffic incident duration and identifies key features.
problem Predict traffic incident duration and identify critical features.
method Multi-task learning framework with sparsity optimization and ADMM algorithm.
result Model predicts incident duration and identifies key features effectively.
Model predicts traffic speed using urban incidents.
problem Accurately predicting traffic speed in urban areas.
method Deep Incident-Aware Graph Convolutional Network (DIGC-Net).
result Model outperforms competing benchmarks in traffic speed prediction.
A symplectic form has a primitive with nowhere vanishing β.
problem Existence of nowhere vanishing primitives for symplectic forms.
method Constructing a primitive β for an exact symplectic form ω. result There exists a nowhere vanishing primitive β for the symplectic form ω. We show that lens space surgeries on knots in S3 which arise from the primitive/Seifert type construction also arise from the primitive/primitive construction. This is the first step of a three step program to prove the Berge conjecture for tunnel number one knots.
Deep neural network reconstructs traffic speeds from sparse vehicle data.
problem Reconstructing traffic speeds from limited probe vehicle data.
method Convolutional neural network architecture for spatio-temporal learning.
result The method can reconstruct traffic speeds with low probe vehicle penetration.
A scoring method for driving safety using trajectory data.
problem Managing traffic safety through driver behaviors and violations.
method Extract driving habits and violations from trajectories, train a model, score drivers.
result Proves the effectiveness of the scoring method using traffic simulation.
For a genus two Heegaard splitting of a lens space, the primitive disk complex is defined to be the full subcomplex of the disk complex for one of the handlebodies of the splitting spanned by all vertices of primitive disks. In this work, we describe the complete combinatorial structure of the primitive disk complex fo…
We show that the exterior derivative operator on a symplectic manifold has a natural decomposition into two linear differential operators, analogous to the Dolbeault operators in complex geometry. These operators map primitive forms into primitive forms and therefore lead directly to the construction of primitive cohom…
GeneraLight improves traffic signal control models' generalization ability.
problem Overfitting and lack of generalization ability in RL TSC models.
method GeneraLight uses a meta-RL framework with a traffic flow generator based on GANs.
result GeneraLight significantly boosts generalization performance across different traffic flows.
This study compares transfer learning and multi-agent learning for AI-driven traffic agents.
problem Improving traffic flow in mixed-intelligence highway scenarios.
method Online MIT DeepTraffic simulation, deep reinforcement learning, elitist evolutionary algorithm, hyperparameter search, transfer learning, multi-agent learning.
result Transfer learning and multi-agent learning yield different average speeds for AI-driven traffic agents.
Model traffic congestion events using multi-modal data and attention-based neural networks.
problem Capture non-homogeneous temporal and directional spatial dependencies in traffic congestion events.
method Attention-based neural networks for point processes, adapted tail-up model for spatial statistics.
result Superior performance compared to state-of-the-art methods on synthetic and real data.
Study primitive decompositions for harmonic forms on almost Kähler manifolds.
problem Decomposing harmonic forms on almost Kähler manifolds.
method Proved primitive decompositions for Bott-Chern and Aeppli harmonic forms in specific bidegrees.
result Optimal bidegrees for primitive decompositions of harmonic forms.
In this article, we prove that every arithmetic locally symmetric orbifold of classical type without Euclidean or compact factors has arbitrarily long arithmetic progressions in its primitive length spectrum. Moreover, we show the stronger property that every primitive length occurs in arbitrarily long arithmetic progr…
We consider the traffic data reconstruction problem. Suppose we have the traffic data of an entire city that are incomplete because some road data are unobserved. The problem is to reconstruct the unobserved parts of the data. In this paper, we propose a new method to reconstruct incomplete traffic data collected from …
Paper uses DeePC to improve urban traffic lights, reducing congestion and emissions.
problem Urban traffic congestion in expanding cities.
method Behavioral system theory, data-driven control, DeePC algorithm.
result DeePC outperforms existing traffic control methods in travel time and CO2 emissions.
A twisted torus knot is a knot obtained from a torus knot by twisting adjacent strands by full twists. The twisted torus knots lie in F, the genus 2 Heegaard surface for S3. Primitive/primitive and primitive/Seifert knots lie in F in a particular way. Dean gives sufficient conditions for the parameters of the tw…
MSCT predicts post-crash traffic speed using causal inference.
problem Time-varying confounding bias in post-crash traffic prediction.
method Marginal Structural Causal Transformer (MSCT) incorporating Marginal Structural Models and balanced loss function.
result MSCT outperforms state-of-the-art models in multi-step-ahead prediction.
Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. In terms of how to dynamically adjust traffic signals' duration…
Adaptive traffic control uses deep RL to improve decision-making.
problem Improving traffic control using deep RL.
method Integrates recent deep RL techniques into a novel DQN-based algorithm (TC-DQN+) for traffic control.
result Proposes a new reward function for traffic control.
New method improves traffic data recovery for streaming data.
problem Improve data quality in traffic data for ITS.
method Online robust tensor recovery algorithm leveraging spatio-temporal correlations and local consistency.
result Significantly improved computational efficiency and high recovery accuracy.
Deep learning and prior maps improve traffic light recognition for autonomous cars.
problem Recognizing traffic lights for autonomous cars in urban environments.
method Combining deep learning-based detection with prior maps for traffic light identification and state recognition.
result The proposed system correctly identified relevant traffic lights along predefined routes.
New CapsNet and NLSTM framework improves traffic forecasting accuracy.
problem Accurate traffic forecasting for complex transportation networks.
method CapsNet for spatial features, nested LSTM for temporal dependencies.
result Framework outperforms multiple baseline models in Beijing network.
QCOMBO optimizes traffic signals across large networks using a combination of independent and centralized RL.
problem Optimizing global traffic conditions over large road networks using single-agent reinforcement learning is challenging.
method QCOMBO integrates independent and centralized learning to optimize traffic signals, using a consistency regularization loss to ensure scalability.
result QCOMBO outperforms state-of-the-art MARL algorithms in diverse road topologies and traffic flow conditions.
The paper compares LSTM and ARIMA for predicting and classifying network traffic.
problem Predicting and classifying network traffic in cellular networks.
method Employed LSTM and ARIMA for time series prediction and classification.
result LSTM outperforms ARIMA in general, especially with longer training series and optimal feature selection.
Study compares neural nets and gradient boosting for traffic optimization, revealing accuracy issues near local optima.
problem Accuracy of neural nets and gradient boosting models in traffic optimization near local optima.
method 16 neural nets and 20 genetic algorithm settings analyzed for traffic optimization.
result Accuracy drops near local optima, affecting traffic optimization efficiency.
Improved LSTM and ARIMA model for traffic flow forecasting.
problem Poor stability, high data requirements, and adaptability issues in existing traffic flow prediction methods.
method Combination prediction method based on improved LSTM and ARIMA models.
result The SDLSTM-ARIMA model achieves higher accuracy in traffic flow prediction.
Study uncovers uncertainty in traffic prediction models across cities.
problem Lack of interpretability in deep learning models for traffic prediction.
method Investigated uncertainty quantification methods for image-based traffic prediction.
result Meaningful uncertainty estimates can be recovered for traffic prediction.
Stochastic models analyze traffic network performance.
problem Evaluate traffic system performance.
method Stochastic cell transmission models, preference functionals, Gaussian process regression.
result Illustrated in two case studies.
Two conditions on primitive elements are shown to be equivalent.
problem Equivalence of primitive stability and Bowditch's BQ-condition. method Proof of equivalence between two conditions on primitive elements.
result Primitive stability and Bowditch's BQ-condition are equivalent. A new reinforcement learning approach using competitive primitives that specialize and specialize based on information needs.
problem Complex environments require efficient and specialized decision-making.
method Decomposes policy into competitive primitives that decide based on information needs, regularized to use minimal information.
result Improves generalization over flat and hierarchical policies.
PSTN improves traffic condition forecasting with deep neural networks.
problem Challenges in accurately forecasting traffic conditions due to complex spatiotemporal correlations.
method Proposes PSTN with three modules: graph convolutional network, temporal convolutional network, and gated recurrent unit framework.
result Significantly outperforms state-of-the-art benchmarks in short-term traffic conditions forecasting.
Predicts user traffic for better resource management in cellular networks.
problem Lack of research on traffic prediction in cellular networks at the user level.
method Statistical, rule-based, and deep machine learning methods.
result Deep machine learning outperforms linear statistical learning after a threshold number of previous observations.
A new traffic signal control method using phase competition.
problem Improving urban transportation efficiency through advanced learning techniques.
method Intuitive phase competition principle applied to reinforcement learning for traffic signal control.
result Our model achieves better solutions, faster convergence, and superior generalizability compared to existing RL methods.