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 …
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,657 papers · 148 categories
Trend · papers per month
48 results for “Data Traffic”
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.
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.
Traffic speed prediction is a critically important component of intelligent transportation systems (ITS). Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models have been designed, which achieved high accuracy and large-scale predicti…
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.
NCPF model improves traffic data imputation with neural and tensor methods.
problem Pervasive missing data in traffic analysis due to sensor failures and gaps.
method Neural Canonical Polyadic Factorization (NCPF) integrating CP decomposition and deep learning.
result NCPF outperforms state-of-the-art baselines in urban traffic datasets.
Urban traffic systems worldwide are suffering from severe traffic safety problems. Traffic safety is affected by many complex factors, and heavily related to all drivers' behaviors involved in traffic system. Drivers with aggressive driving behaviors increase the risk of traffic accidents. In order to manage the safety…
Proposes a feature transformation for spatio-temporal traffic models to improve performance and transferability.
problem Limited transferability of deep learning models for traffic flow prediction across different locations.
method Integrates Newell's traffic flow estimators to capture broader dynamics and incorporates spatial dependencies.
result Improves model performance in predicting traffic flows over different horizons.
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…
Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road …
Paper proposes a new model for imputing missing spatiotemporal traffic data.
problem Missing data and sparsity in spatiotemporal traffic data.
method Low-rank tensor completion (LRTC) framework with truncated nuclear norm (TNN).
result The proposed model outperforms state-of-the-art imputation models in various scenarios.
Traffic flow forecasting is hot spot research of intelligent traffic system construction. The existing traffic flow prediction methods have problems such as poor stability, high data requirements, or poor adaptability. In this paper, we define the traffic data time singularity ratio in the dropout module and propose a …
Improved traffic forecasting model handles missing data.
problem Short-term traffic forecasting with missing values.
method Proposed SBU-LSTM architecture with bidirectional and unidirectional LSTM.
result Superior performance in accuracy and robustness for network-wide traffic prediction.
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.
A new method for traffic data imputation considering spatiotemporal correlations.
problem Traffic data imputation, especially for high-level missing scenarios.
method Spatiotemporal regularized Tucker decomposition approach.
result The proposed method outperforms existing methods on real-world traffic datasets.
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…
IVMs help identify dangerous traffic conditions in real-time.
problem Real-time crash risk analysis in urban traffic systems.
method Import Vector Machines (IVMs) applied to historical crash and traffic data.
result IVMs successfully identify dangerous traffic conditions with computational advantage.
Two autoencoding models learn latent traffic scene representations.
problem Learning latent representations of traffic scenarios.
method CNN and RNN models for spatio-temporal and temporal data, incorporating permutation invariance.
result Latent scenario embeddings can be used for clustering and similarity retrieval.
Proposes a graph neural network for traffic forecasting in WANs.
problem Traffic forecasting challenges in WANs due to dynamic and large data volumes.
method Dynamic diffusion convolutional recurrent neural networks for multistep traffic forecasting.
result Significant improvements in forecasting accuracy compared to classical methods.
Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The …
We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, we show that a well trained neural network can learn spatio-temporal traffic speed dynamics from time-space diagrams. We demonstrate this for…
Bayesian model improves traffic prediction with uncertainty estimates.
problem Lack of uncertainty estimates in deep-learning traffic models.
method Proposes a Bayesian recurrent neural network with spectral normalization.
result Spectral normalization improves uncertainty estimates and generalizability.
Traffic sign recognition is an important component of many advanced driving assistance systems, and it is required for full autonomous driving. Computational performance is usually the bottleneck in using large scale neural networks for this purpose. SqueezeNet is a good candidate for efficient image classification of …
Transfer learning improves highway traffic forecasting using graph neural networks.
problem Lack of historical data for traffic forecasting on large highway networks.
method Developed a transfer learning approach for DCRNN, a graph neural network for highway forecasting.
result TL-DCRNN can forecast traffic on unseen regions of the highway network with high accuracy.
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.
IGANI: Iterative Generative Adversarial Networks for Imputation with Application to Traffic Datastat.ML
IGANI uses iterative GANs to improve traffic data imputation.
problem Imputation of traffic data in the absence of sensor data.
method Iterative Generative Adversarial Networks (IGANI) for unsupervised learning.
result IGANI produces more accurate imputation results compared to previous methods.
Study uses vehicle trajectory data to predict traffic incidents on highways.
problem Early detection of traffic incidents to reduce secondary crashes.
method Machine learning algorithms (Logistic Regression, Random Forest, Extreme Gradient Boost, Artificial Neural Network) applied to vehicle trajectory data.
result Random Forest model performs best for incident prediction.
H-STGCN predicts traffic using navigation data and improves accuracy.
problem Limited accuracy in traffic forecasting due to lack of contextual information.
method Proposes H-STGCN, a hybrid spatio-temporal graph convolutional network.
result H-STGCN outperforms state-of-the-art methods in various metrics, especially for non-recurring congestion.
Study develops a machine learning-based ramp metering model to improve freeway efficiency.
problem Improving ramp metering to maintain freeway efficiency under various traffic conditions.
method Machine learning approach using historical data to predict and manage traffic flow.
result The novel model outperforms a baseline traffic-responsive ramp metering algorithm.
Traffic speed data imputation is a fundamental challenge for data-driven transport analysis. In recent years, with the ubiquity of GPS-enabled devices and the widespread use of crowdsourcing alternatives for the collection of traffic data, transportation professionals increasingly look to such user-generated data for m…
A new framework reduces traffic congestion by 36%.
problem Efficient traffic signal control in a metropolitan area.
method Two-stage framework combining Evolution Strategies and multi-agent reinforcement learning.
result Reduces traffic congestion by 36%.
As a crucial component in intelligent transportation systems, traffic flow prediction has recently attracted widespread research interest in the field of artificial intelligence (AI) with the increasing availability of massive traffic mobility data. Its key challenge lies in how to integrate diverse factors (such as te…
Study uses open data to improve traffic emissions estimation.
problem Estimating accurate link level traffic emissions.
method Data-driven framework integrating MOVES, GPS, OSM, and satellite imagery.
result Neural network reduces RMSE by over 50% for key pollutants.
AGCRN forecasts traffic using adaptive graph and recurrent learning.
problem Forecasting traffic dynamics with complex spatial and temporal correlations.
method Adaptive Graph Convolutional Recurrent Network (AGCRN) with Node Adaptive Parameter Learning (NAPL) and Data Adaptive Graph Generation (DAGG).
result AGCRN outperforms state-of-the-art models without pre-defined graphs.
This paper proposes a real-time signal plan recommendation system for traffic incidents.
problem Limited effectiveness of traffic incident management due to late response and workload.
method Decomposes recommendation task into real-time traffic prediction and plan association, learning from historical data and metric learning.
result Precision score of 96.75% and recall of 87.5% on testing plan, with 22.5 minutes lead time ahead of Waze alerts.
Neural networks predict traffic flow in smart cities.
problem Forecasting stochastic and nonlinear traffic flow.
method Various recurrent neural networks trained on intersection data.
result Vector output model with gated recurrent units performed best.
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 model fills in missing traffic data efficiently.
problem Missing data in large-scale spatiotemporal traffic data.
method Developed scalable tensor learning model LSTC-Tubal for imputation.
result LSTC-Tubal achieves high accuracy with lower computational cost.
Predicts traffic flow using reinforcement learning and sensor data.
problem Accurately predict expanding and evolving long-term streaming traffic networks.
method Formulates the problem as a continuous reinforcement learning task, where the agent predicts future traffic based on sensor data.
result The approach improves accuracy in predicting traffic flow by updating the agent's state representation over time.
New neural network predicts traffic flow across different cities.
problem Forecasting traffic flow across different cities is challenging due to spatio-temporal correlations.
method Proposes a local-spacetime neural network (STNN) that captures universal spatio-temporal correlations.
result Improves prediction accuracy by 4% over state-of-the-art methods.
This project presents the results of a partnership between the Data Science for Social Good fellowship, Jakarta Smart City and Pulse Lab Jakarta to create a video analysis pipeline for the purpose of improving traffic safety in Jakarta. The pipeline transforms raw traffic video footage into databases that are ready to …
Forecasting the future traffic flow distribution in an area is an important issue for traffic management in an intelligent transportation system. The key challenge of traffic prediction is to capture spatial and temporal relations between future traffic flows and historical traffic due to highly dynamical patterns of h…
Unified approach detects traffic conflicts across various interactions.
problem Inconsistent detection of traffic conflicts across different interactions.
method Unified probabilistic approach decomposes conflicts into statistical learning tasks.
result Effective collision warnings across diverse datasets and environments.
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.
Critical incident stages identification and reasonable prediction of traffic incident duration are essential in traffic incident management. In this paper, we propose a traffic incident duration prediction model that simultaneously predicts the impact of the traffic incidents and identifies the critical groups of tempo…
Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling. The increasing amounts of generated traffic data have been used to train machine learning predictors for traffic, however this is a challenging task due to inter-dependencies of…
Study predicts traffic congestion based on population mobility data.
problem Predicting traffic congestion in multimodal transport networks.
method Machine learning methods applied to population mobility data.
result Likely prediction of congestion based on population movements.
This study evaluates methods to measure traffic forecasting model confidence.
problem Lack of consensus on uncertainty types and techniques for traffic forecasting models.
method Reviews and compares different uncertainty estimation techniques using real traffic data.
result Empirical evidence shows benefits and caveats of various techniques.