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.
Traffic actors' future motion predicted using a hybrid graph model.
problem Predicting long-term behaviors of traffic actors in complex scenes.
method A hybrid graph model with nodes for actors and traffic elements, and edges for interaction types.
result TrafficGraphNet achieves state-of-the-art trajectory prediction accuracy.
Research explores unsupervised methods for detecting vessel behavior changes in real-time data streams.
problem Detecting shifts in vessel behavior for maritime traffic monitoring.
method Investigates unsupervised and semi-supervised change detection methods.
result Identifies shifts in vessel behavior for unusual events detection.
A new model captures car-following and lane-changing behaviors in traffic.
problem Modeling stochastic microscopic traffic behaviors.
method Physics regularized Gaussian process (PRGP) approach.
result The proposed model outperforms previous methods in estimation precision.
Neural networks improve traffic flow predictions by considering lane interactions.
problem Improving traffic flow predictions at the lane level.
method Applying neural attention to deep neural networks for modeling spatiotemporal traffic dynamics.
result Attentional neural network models can use information from nearby lanes to improve predictions.
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.
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…
Project analyzes traffic videos to improve Jakarta's safety.
problem Improving traffic safety in Jakarta.
method Developed a pipeline to analyze traffic videos, turning them into usable databases.
result Better understanding of traffic challenges and safety risks.
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.
This study uses Twitter to analyze traveler behavior in Manhattan.
problem Analyzing traveler behavior using social media data.
method Systematic method to extract displacement information from geo-tagged tweets.
result Twitter reveals unique demographics and travel behavior patterns.
Study improves vehicle motion prediction by incorporating traffic density.
problem Lack of contextual knowledge in predicting driving behavior.
method Categorize and define external conditions, evaluate motion prediction approach.
result Traffic density significantly improves motion prediction accuracy.
New approach learns traffic behavior from unlabelled video.
problem Learning from demonstration in settings with unlabelled raw video data.
method Calibrates camera, detects objects, tracks them, and uses trajectories for LfD.
result Can learn traffic behavior from raw video without additional expert knowledge.
Learn effective driving policies from traffic cameras without interaction.
problem Learning policies from observational data in dynamic environments.
method Model-predictive policy learning with uncertainty regularization.
result Effective driving policies learned from observational data.
Develops a new model for controllable and realistic traffic simulation.
problem Lack of models that offer both controllability and realism in traffic simulation.
method Guided Conditional Diffusion (CTG) model using diffusion modeling and differentiable logic.
result Improves controllability-realism tradeoff over strong baselines.
We focus on an interpolation method referred to Bayesian reconstruction in this paper. Whereas in standard interpolation methods missing data are interpolated deterministically, in Bayesian reconstruction, missing data are interpolated probabilistically using a Bayesian treatment. In this paper, we address the framewor…
Extracts patterns from mobile network data for better resource management.
problem Improving network efficiency and resource allocation for mobile users.
method Spatiotemporal analysis of internet activity records (IARs) data.
result Developed a mobile traffic partitioning scheme.
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.
New method estimates traffic congestion delays using statistical causality.
problem Accurate estimation of traffic congestion delays during accidents.
method Proposes a novel time delay estimation method using lag-specific transfer entropy (TE) and Markov bootstrap techniques.
result Validated the method's efficacy using simulated and real data.
Researchers forecast VoIP traffic in mobile networks using multivariate time series analysis.
problem Predicting VoIP traffic behavior in real mobile networks for better resource allocation.
method Multivariate time series analysis, Vector Autoregressive models, machine learning techniques.
result Forecasting accuracy and insights into VoIP traffic dynamics.
Study quantifies pedestrian traffic patterns in NYC.
problem Understanding pedestrian traffic dynamics in urban areas.
method Publicly available traffic camera data in NYC, time series analysis.
result Pedestrian traffic exhibits diurnal patterns with weekday peaks and no peak on weekends.
DQN optimizes traffic light control policies in ITSs.
problem Challenges in scalable real-time actuation mechanisms for smart traffic management.
method Exploration of Deep Q-Networks (DQN) for traffic light control policies.
result DQN algorithms produce intelligent behavior, such as greenwave patterns.
HGP models traffic speed uncertainty better than current methods.
problem Highly variable measurement noise in crowdsourced traffic data.
method Heteroscedastic Gaussian processes (HGP) conditioned on sample size and traffic regime (SRC-HGP).
result SRC-HGP produces significantly better predictive distributions.
MuJAM learns traffic signal control policies that generalize to unseen intersections and traffic conditions.
problem Lack of transferability in reinforcement learning methods for traffic signal control.
method Model-based graph reinforcement learning with explicit coordination and generalization to both cyclic and acyclic constraints.
result MuJAM outperforms existing methods in zero-shot and larger transfer settings.
Paper tackles ML for non-scheduled URLLC traffic in 5G networks.
problem Coexistence of scheduled and non-scheduled URLLC traffic with stringent reliability and latency requirements.
method Distributed risk-aware ML solution for resource management (RRM).
result 75% increase in data rate for scheduled traffic with 99.99% reliability for both types.
Making the right decision in traffic is a challenging task that is highly dependent on individual preferences as well as the surrounding environment. Therefore it is hard to model solely based on expert knowledge. In this work we use Deep Reinforcement Learning to learn maneuver decisions based on a compact semantic st…
Deep RL agent improves lane changing in unpredictable traffic.
problem Uncertainty in other drivers' behaviors and safety vs agility trade-off.
method Developed a deep reinforcement learning agent in a simulated highway environment.
result Significantly better performance in noisy environments compared to heuristic methods.
TG-PSM morphs encrypted traffic to reduce traffic analysis accuracy.
problem Traffic analysis on encrypted traffic reveals sensitive information.
method Clusters websites, finds target sites, morphs traffic using greedy algorithm.
result TG-PSM reduces attacker's accuracy significantly with low overhead.
Novel RL method handles urban driving tasks including traffic lights.
problem Difficulties in reinforcement learning for complex urban driving tasks.
method Implicit affordances for model-free RL.
result Successfully handles urban driving tasks including traffic lights.
This paper proposes a generic classification system designed to detect security threats based on the behavior of malware samples. The system relies on statistical features computed from proxy log fields to train detectors using a database of malware samples. The behavior detectors serve as basic reusable building block…
Advanced travel information and warning, if provided accurately, can help road users avoid traffic congestion through dynamic route planning and behavior change. It also enables traffic control centres mitigate the impact of congestion by activating Intelligent Transport System (ITS) proactively. Deep learning has beco…
Co-DQL improves traffic signal control using multi-agent reinforcement learning.
problem Optimizing signal timing for large-scale traffic control.
method Cooperative double Q-learning (Co-DQL) with mean field approximation and reward allocation.
result Co-DQL reduces average waiting time for vehicles in the road system.
Deep learning identifies unknown IoT devices in network traffic.
problem Unauthorized IoT devices pose security risks in BYOD environments.
method Deep learning applied to network traffic images for IoT device identification.
result Over 99% accuracy in identifying 10 IoT devices and non-white-listed devices.
This paper detects anomalies in cellular network traffic using hybrid methods.
problem Detecting anomalies in network traffic for security and analysis.
method Hybrid method combining GARCH, K-means, and Neural Network.
result Anomaly detection in cellular network traffic successfully achieved.
Improved NODEs for long-term time series forecasting.
problem Dealing with complex, multi-frequency data.
method Progressive learning paradigm with curriculum learning.
result Performance improved by over 64%.
This paper improves route choice models by incorporating contextual factors.
problem Existing route choice models lack consideration of dynamic contextual conditions.
method Knowledge distillation from Stated Choice Experiments in Immersive Virtual Environment.
result High-fidelity route choice models with increased predictive power.
This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called GraphPrints. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets -- small induced subgraphs that describe local topology. By performing…
Adverts optimize organic traffic by strategically bidding in e-commerce feeds.
problem Maximizing organic traffic through strategic advertising in e-commerce feeds.
method Proposes a novel Leverage optimization problem and a Hybrid Training Leverage Bidding (HTLB) algorithm to optimize traffic.
result Demonstrates superior performance of the HTLB algorithm in optimizing organic traffic.
Simulation speeds AV testing by 2-20 times over real-world methods.
problem Lack of scalable and rigorous testing for autonomous vehicles.
method Adaptive importance-sampling methods for rare-event probability evaluation.
result Accelerates accident probability estimation by 2-20 times over naive Monte Carlo methods.
Extends driving model to control agent behavior in simulations.
problem Simulate realistic driving behavior for autonomous systems.
method Introduces Control-ITRA method to influence agent behavior through waypoint assignment and target speed modulation.
result Demonstrates controllable, infraction-free trajectories while preserving realism.
Active learning improves network intrusion detection with minimal labeled data.
problem Finding new attack vectors in low-volume traffic.
method Proposes an active learning framework to minimize labeled data while improving detection quality.
result Active learning with minimal labeled data significantly improves anomaly detection in network traffic.
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…
The goal of this project is to introduce and present a machine learning application that aims to improve the quality of life of people in Singapore. In particular, we investigate the use of machine learning solutions to tackle the problem of traffic congestion in Singapore. In layman's terms, we seek to make Singapore …
The majority of real-world networks are dynamic and extremely large (e.g., Internet Traffic, Twitter, Facebook, ...). To understand the structural behavior of nodes in these large dynamic networks, it may be necessary to model the dynamics of behavioral roles representing the main connectivity patterns over time. In th…
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.
Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles. The majority of current researches fix the number of driving intentions by considering only a specific scenario. However, distinct driving environments usually contain various possible driving…