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.
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.
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.
Survey of deep learning applications in traffic congestion detection, prediction, and alleviation.
problem Improving transportation network service by detecting, predicting, and alleviating traffic congestion.
method Comprehensive review of deep learning applications in transportation, focusing on challenges specific to congestion.
result Identification of challenges and gaps in current research on deep learning for traffic congestion.
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 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.
Predicts morning traffic congestion using social media data from the previous evening.
problem Challenges in predicting early morning traffic dynamics.
method Mining Twitter messages to understand evening/midnight work and rest patterns.
result People's tweeting patterns before the morning commute are associated with traffic congestion.
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 …
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%.
Backdoor attacks on DRL-based traffic controllers cause stop-and-go waves or crashes.
problem Vulnerability of DRL-based traffic controllers to machine learning attacks.
method Developed a trigger design methodology based on traffic physics principles.
result Backdoored models can cause stop-and-go traffic waves or AV crashes when triggered.
New algorithm for traffic routing in congested conditions.
problem Optimal routing in congested traffic networks.
method Congested Bandits model, UCB algorithm, iterative least squares planner.
result No-regret algorithms for congestion-aware routing.
This paper proposes MM-DAGs for analyzing traffic congestion, learning multiple DAGs jointly.
problem Analyzing multi-modal traffic data with overlapping and distinct variables.
method Developed MM-DAGs for multi-task, multi-modal DAG learning, using multi-modal regression and CD measure.
result Proved the effectiveness of MM-DAGs in traffic congestion analysis.
Dynamic linear models improve travel time prediction for congested freeways.
problem Accurate travel time prediction for congested freeways.
method Dynamic linear models (DLMs) with time-varying parameters.
result Significant improvements in travel time prediction accuracy, especially for short-term predictions.
Online learning improves traffic congestion prediction over time.
problem Model degradation due to concept drift in traffic data.
method Incremental learning from non-stationary time series data.
result Performance of models degrades with increased prediction horizon.
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.
Framework classifies urban congestion into recurrent and non-recurrent types.
problem Classifying urban congestion into recurrent and non-recurrent types.
method Real-time distributed classification using VANET on a heterogeneous urban road network.
result Predictive accuracy of 89.17% for the boosting technique.
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 of congestion in negative curvature manifolds using fair-division algorithms.
problem Estimating and predicting the size and location of congestion core in negative curvature manifolds.
method Introducing a novel fair-division algorithm to estimate congestion core.
result Demonstrated the effectiveness of fair-division algorithms in estimating congestion core.
Deep learning detects traffic accidents in real time using spatiotemporal data.
problem Detecting traffic accidents to improve safety and reduce delays.
method Used LSTM and GRUs on spatiotemporal sequential data with SMOTE oversampling.
result GRU model performs slightly better than LSTM in detecting traffic accidents.
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.
This study develops methods to coordinate travel routes to reduce congestion.
problem Coordination of travel routes to reduce urban traffic congestion.
method Developed mathematical approaches to quantify coordination potential and adaptive centroid-based clustering algorithm (ACCA).
result ACCA efficiently forms proper coordination groups for CB-CRM, improving efficiency with minimal performance loss.
Optimizes taxi carpool policies using RL and spatio-temporal data.
problem Reduce car usage and minimize traffic congestion through efficient carpooling.
method Developed a deep neural network (ST-NN) for trip time prediction and a carpooling RL simulation environment.
result RL learned policy outperformed a fixed policy in maximizing efficiency and minimizing congestion.
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.
Proposes a deep learning model for predicting traffic speeds in real-time.
problem Difficulty in capturing traffic data's random, seasonal, non-linear, and spatio-temporal correlated nature.
method Hierarchical D-CLSTM-t model combining CNN and LSTM for short-term traffic speed prediction.
result D-CLSTM-t model outperforms other models in predicting traffic speeds.
Due to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and lost time for the ot…
Some optimization or equilibrium problems involving somehow the concept of optimal transport are presented in these notes, mainly devoted to applications to economic and game theory settings. A variant model of transport, taking into account traffic congestion effects is the first topic, and it shows various links with…
Paper develops a framework to test deep RL traffic controllers under various uncertainties.
problem Developing robust deep RL traffic controllers for dynamic urban areas.
method Open-source callback-based framework for evaluating deep RL configurations in a traffic simulation.
result Deep RL controllers perform well under demand surges, incidents, and sensor failures.
We propose a probabilistic graphical model realizing a minimal encoding of real variables dependencies based on possibly incomplete observation and an empirical cumulative distribution function per variable. The target application is a large scale partially observed system, like e.g. a traffic network, where a small pr…
Proposes three decentralized multi-agent reinforcement learning algorithms to reduce network congestion.
problem Finding a joint policy maximizing long-term return in a decentralized multi-agent system.
method Three fully decentralized multi-agent natural actor-critic (MAN) algorithms using linear function approximations.
result The proposed algorithms achieve performance comparable to or better than standard methods in reducing network congestion.
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.
Decentralized algorithm reduces regret and converges to Nash equilibrium in online congestion games.
problem Online congestion games with exponential action sets and strict Nash equilibria.
method CongestEXP algorithm using exponential weights method.
result CongestEXP achieves O ( k F T ) O(kF\sqrt{T}) O ( k F T ) regret bound and almost exponential convergence to strict Nash equilibrium. 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.
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.
Deep learning predicts traffic flow on Sydney motorways.
problem Challenging traffic flow prediction due to inter-dependencies.
method Advanced deep learning framework using CNN-LSTM.
result Deep learning models outperform traditional methods.
Proposes a dynamic model for urban traffic volume prediction.
problem Urban traffic volume prediction for better traffic management and driver planning.
method Combines bidirectional LSTM, attention mechanism, and external features.
result Improves prediction precision by 3-7 percent on NYC-Taxi and NYC-Bike datasets.
Iroko enables RL for datacenter CC, outperforming TCP on fat-tree and dumbbell topologies.
problem Stability and over-fitting issues in RL for datacenter networks.
method Developed Iroko emulator to support various network conditions and algorithms.
result Deep RL algorithms outperform TCP on fat-tree and dumbbell topologies.
Hybrid model improves traffic flow prediction accuracy.
problem Predicting traffic flow with high accuracy in short-term future.
method A hybrid model combining hidden Markov model and LSTM.
result Significant performance gains over conventional methods.
Proposes a method to find traffic regulations efficiently.
problem Finding appropriate traffic regulations in congested events.
method Graph Convolutional Networks for modeling regulation effects.
result The method can find a road to close that reduces travel time.
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.
This paper tackles traffic volume estimation challenges with a deep learning method.
problem Underdetermined and non-equilibrium traffic flows.
method Graph-based deep learning method with adaptive attention mechanisms.
result The proposed model achieves high accuracy even with low sensor coverage.
Paper optimizes traffic signal control for better traffic flow.
problem Optimizing traffic signal control to reduce congestion and improve safety.
method Value-based reinforcement learning with interpretable policy functions (polynomial functions).
result Deep Regulatable Hardmax Q-learning variant reduces vehicle delay by up to 19.4%.
A new model predicts dynamic O-D matrices using graph neural networks and Kalman filters.
problem Predicting dynamic O-D demand matrices from traffic flow data.
method Combines graph neural networks and Kalman filters to recognize spatial and temporal patterns.
result The proposed model outperforms other methods in various prediction scenarios.
New method improves traffic speed estimation from sparse data.
problem Incomplete and noisy traffic speed data from sparse sensors.
method Laplacian-enhanced low-rank tensor kriging (LETC) framework.
result LETC achieves state-of-the-art kriging performance under low observation rates.
Proposes a framework to handle missing data in traffic forecasting with sensor blackouts.
problem Missing data in traffic forecasting due to sensor blackouts, especially when correlated with traffic conditions.
method Latent state-space framework that models traffic dynamics and sensor dropout probabilities.
result Improves traffic forecasting by reducing blackout imputation RMSE from 7.02 to 4.23, with MNAR modeling providing additional gains.
This paper uses machine learning to estimate how different types of crashes affect highway traffic.
problem Estimating the heterogeneous causal effects of crashes on highway traffic.
method Neyman-Rubin Causal Model, Conditional Shapley Value Index, Structural Causal Model, Doubly Robust Learning.
result Different types of crashes have varying impacts on traffic, with rear-end crashes causing the most severe congestion.
Predicts path failures in evolving networks using deep learning.
problem Predicting path failures in time-evolving graphs.
method LRGCN, SAPE
result LRGCN outperforms other methods in path failure prediction.
Predicting transportation modes from GPS (Global Positioning System) records is a hot topic in the trajectory mining domain. Each GPS record is called a trajectory point and a trajectory is a sequence of these points. Trajectory mining has applications including but not limited to transportation mode detection, tourism…
Intelligent AQM uses ECN to predict and control network congestion.
problem Challenges in finding optimal AQM parameters due to network complexity.
method Machine Learning (Neural Network for prediction, Reinforcement Learning for parameter tuning).
result Enhanced performance of AQM using ECN and existing TCP congestion control.