Paper develops a method to detect abnormal road user trajectories using deep autoencoder.
problem Insufficient abnormal data for training machine learning models.
method Used a deep autoencoder network trained on augmented normal data.
result Performs better than classical outlier detection methods on various scenes.
New radar-based method improves multiclass classification of road users, especially in challenging conditions.
problem Accurate classification of multiple road users in challenging scenarios.
method 50 features extracted from radar data, subset chosen, tested on random forest and LSTM classifiers, addressed data imbalance issues.
result Substantial improvements in multiclass classification compared to ordinary methods.
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.
Study proposes GRU-D networks for missing value handling in road surface friction prediction.
problem Missing values in road surface friction data affect prediction accuracy.
method Gated Recurrent Unit (GRU) network with decay mechanism.
result GRU-D networks outperform baseline models in road surface friction prediction.
Deep Learning detects cyclists' orientation for safer roads.
problem Ensuring safety for cyclists in intelligent transportation systems.
method Transfer Learning with pre-trained models, multi-class detection, new dataset creation.
result Faster R-CNN with ResNet50 proved precise but slower; SSD with InceptionV2 provided good trade-off.
Deep learning generates addresses for under-addressed cities.
problem Lack of street addresses in developing countries.
method Deep learning applied to satellite images to generate addresses.
result Algorithm generates addresses for neighborhoods and regions.
Machine learning detects road anomalies and aggressive driving from smartphone data.
problem Road quality assessment and aggressive driving detection.
method Machine learning techniques applied to smartphone acceleration data.
result Robust platform for road transport evaluation.
Radar-based classification improves accuracy of autonomous driving by identifying new classes.
problem Challenges in classifying sparse radar data for autonomous driving.
method Ensemble of classifiers using one-vs-one and one-vs-all strategies, with feature selection.
result Improved classification performance and identification of novel classes.
We study data-driven assistants that provide congestion forecasts to users of shared facilities (roads, cafeterias, etc.), to support coordination between them, and increase efficiency of such collective systems. Key questions are: (1) when and how much can (accurate) predictions help for coordination, and (2) which as…
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.
GCNs adapted for road networks improve performance on edge prediction tasks.
problem Improving machine learning on road networks for edge prediction tasks.
method Introducing Relational Fusion Network (RFN) for road networks.
result RFN outperforms state-of-the-art GCNs on road segment regression and classification tasks.
RFN improves GCNs for road networks, outperforming state-of-the-art by 21%-40%.
problem Leveraging the structure of road networks effectively in machine learning tasks.
method Introducing RFN, a novel GCN specifically designed for road networks.
result RFN outperforms state-of-the-art GCNs by 21%-40% on road network tasks.
System predicts ice formation to improve road safety.
problem Improving road safety through accurate ice formation prediction.
method Combining physical models and machine learning for data correction.
result Machine learning effectively corrects incorrect data in forecasts.
Network embedding helps predict speed limits on incomplete Danish road network.
problem Incomplete speed limit data on Danish roads limits machine learning applications.
method Applied node2vec network embedding to Danish road network.
result Network embedding can derive useful features for predicting speed limits.
Paper presents a self-supervised method to infer road lane networks.
problem Difficult and costly to create lane maps for autonomous vehicles.
method Self-supervised learning using neural and search-based model.
result Model can generalize to new road layouts, unlike previous approaches.
Deep learning maps road safety features from streetview imagery.
problem Accurate mapping of road safety features for safer roads.
method Proposes a deep learning approach with LSTM to capture spatial autocorrelation.
result Model outperforms baseline methods in mapping road safety features.
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.
Model predicts road vehicle accidents with high resolution.
problem Accidents on roads in Montreal.
method Big data analytics, machine learning (Balanced Random Forest algorithm).
result 85% of road vehicle collisions detected with 13% false positives.
Automated road infrastructure mapping using connected vehicle data and deep learning.
problem Manual identification of intersections is laborious and time-consuming.
method Geohashing, YOLOv5 algorithm for classification of road segments and intersections.
result Overall classification accuracy of 95%, with high F1 scores for straight roads and intersections.
Generative model extracts road networks from images.
problem Extracting road networks from image data.
method Developed Generative Graph Transformer (GGT) for image-conditioned graph generation.
result Demonstrated effective road network generation from semantic segmentation data.
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.
iDriveSense offers safer trip recommendations by considering road anomalies.
problem Drivers seek safer routes despite shortest/fastest path recommendations.
method Crowdsensing, vehicle sensors, fuzzy systems for road quality assessment.
result Proposes a system for dynamic route planning considering road anomalies.
Model uses satellite imagery to assess road quality.
problem Inefficient monitoring of road investments in developing regions.
method Convolutional neural networks on high-resolution satellite imagery.
result Models achieve 88% binary and 73% 5-category classification accuracy.
Game theory improves smart road sign security against small perturbations.
problem Ensuring smart road signs are secure from small-scale adversarial attacks.
method Integrates game theory into smart road sign classification to detect imperceptible perturbations.
result Proposes a randomized detection strategy to ensure robustness against worst-case attackers.
New methods detect roads in low-res satellite data, overcoming visibility challenges.
problem Detecting roads in low-resolution satellite imagery, especially those hard to see.
method Two deep learning frameworks for ordinal classification of road types from satellite time series data.
result Models can identify large and medium-sized roads from Sentinel-2 imagery.
Study improves prediction of UK road accidents' severity using AI.
problem Improving prediction of UK road traffic accident severity.
method Combination of machine learning, econometric, and statistical methods on historical data.
result XGBoost model with RMSE of 0.176 and MAE of 0.087 outperforms naive forecasting.
Enhanced travel time prediction using deep neural networks and road network information.
problem Improving travel time estimation using deep learning models.
method Proposes incorporating road network information into deep learning models for travel time prediction.
result Improved travel time prediction, especially with limited training data.
Study shows 'Belt and Road' node cities boost digital finance in China.
problem Impact of 'Belt and Road' node cities on digital inclusive finance.
method Descriptive analysis, literature review, theoretical model, differential differential method.
result Establishment of 'Belt and Road' node cities promotes digital inclusive finance in China, with Internet development as a mediating variable.
Foresee predicts future road environments with neural networks.
problem Predicting future chaotic road environments for autonomous driving.
method Unidirectional gated recurrent units (GRUs) with attention for online training.
result Foresee outperforms state-of-the-art methods in future projection accuracy.
3D-TGCN learns road graphs from time series similarity for spatio-temporal traffic forecasting.
problem Challenging spatio-temporal prediction in traffic networks due to dependency and dynamics.
method Proposes 3D-TGCN with novel components: spatial information-free road graph and 3D graph convolution.
result 3D-TGCN outperforms state-of-the-art baselines in traffic forecasting.
Road Network Metric Learning improves ETA prediction accuracy by addressing data sparsity.
problem Data sparsity in road network embedding vectors affects ETA prediction accuracy.
method Proposes Road Network Metric Learning (RNML-ETA) framework with an auxiliary metric learning task and triangle loss.
result RNML-ETA outperforms state-of-the-art models and improves prediction accuracy for cold links.
Study improves traffic prediction intervals for minor roads.
problem Uncertainty in traffic data for underrepresented minor roads.
method Quantile Random Forest with PCA for interval prediction.
result Achieved 88.22% interval coverage and Winkler Score of 7,468.47.
Study optimal bidding strategies for digital ads targeting purchases and health campaigns.
problem Optimizing advertising strategies in digital channels.
method Continuous-time models encoding user behavior and auction mechanisms, semi-explicit formulas for optimal bidding.
result Semi-explicit formulas for optimal value and bidding policy for different types of advertising.
Develops a deep clustering framework for large-scale road traffic prediction.
problem Challenges in modeling diverse traffic patterns and handling high-dimensional time series with low latency.
method Combines deep clustering with CNNs and RNNs to predict road traffic at large-scale networks.
result The DeepCluster framework effectively clusters road segments and improves prediction performance.
China's rapid economic growth resulted in serious air pollution, which caused substantial losses to economic development and residents' health. In particular, the road transport sector has been blamed to be one of the major emitters. During the past decades, fluctuation in the international oil prices has imposed signi…
Model predicts road traffic using high-dimensional time-series with L1-penalization.
problem Predicting high-dimensional road traffic data with limited observations.
method Vector autoregressive model with L1-penalization for high-dimensional regression.
result The approach identifies the most important road sections and is competitive in prediction.
NL-LinkNet improves road extraction from satellite images with fewer parameters and faster training.
problem Efficiently extracting roads from high-resolution satellite images.
method Integrates non-local blocks into LinkNet for better feature context.
result NL-LinkNet outperformed state-of-the-art models in the DeepGlobe Challenge.
We provide a model to understand how adverse weather conditions modify traffic flow dynamic. We first prove that the microscopic Free Flow Speed of the vehicles is changed and then provide a rule to model this change. For this, we consider a thresholded linear model, corresponding to an application of a MARS model to r…
Improved road segmentation on low-res LIDAR data for autonomous vehicles.
problem Low-resolution LIDAR data affects road segmentation accuracy in autonomous vehicles.
method Subsampled LIDAR data transformation into feature maps, use local normal vector with spherical coordinates.
result Improves road segmentation accuracy on low-resolution LIDAR data.
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.
Modern vehicles are equipped with increasingly complex sensors. These sensors generate large volumes of data that provide opportunities for modeling and analysis. Here, we are interested in exploiting this data to learn aspects of behaviors and the road network associated with individual drivers. Our dataset is collect…
Deep learning agent improves pedestrian navigation in urban environments.
problem Autonomous driving among pedestrians in urban areas.
method Multi-objective deep reinforcement learning using a deep Q-learning variant.
result The multi-objective DQN agent outperforms single-objective DQN in various environments.
World wide road traffic fatality and accident rates are high, and this is true even in technologically advanced countries like the USA. Despite the advances in Intelligent Transportation Systems, safe transportation routing i.e., finding safest routes is largely an overlooked paradigm. In recent years, large amount of …
Study uses Android smartphones to measure road roughness, finds ML better than RMS.
problem Measuring road roughness using smartphones.
method Three smartphones, Android OS, RMS and ML methods, comparison with IRI.
result ML method outperforms RMS in detecting road roughness.
Wisard neural network detects cracks in asphalt roads.
problem Automatic detection of cracks in paved roads.
method Weightless neural network (Wisard) with transfer learning.
result 85.71% accuracy in detecting cracks.
PIP-Net predicts pedestrian crossing intentions with up to 4-second lead.
problem Accurate pedestrian intention prediction for autonomous vehicles in real-world scenarios.
method Recurrent and temporal attention-based model using kinematic and spatial features.
result PIP-Net predicts pedestrian crossing intentions up to 4 seconds in advance.
ROAD-EnKFs use learned low-dimensional models to improve state reconstruction and forecasting.
problem Reconstructing and forecasting states of unknown or expensive systems.
method Learned low-dimensional surrogate models and ensemble Kalman filter integration.
result ROAD-EnKFs achieve higher accuracy at lower computational cost than existing methods.
Polarimetric images enhance object detection in adverse weather conditions.
problem Object detection in road scenes is challenging in adverse weather conditions.
method Combining polarimetric imaging and deep learning.
result Polarimetry improves object detection by 20% to 50% compared to conventional RGB images.