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Found 13 papers, 3 papers with code
Integrating Large Language Models for Severity Classification in Traffic Incident Management: A Machine Learning Approach
Incorporating features from language models with those directly obtained from incident reports has shown to improve, or at least match, the performance of machine learning techniques in assigning severity levels to incidents, particularly when employing Random Forests and Extreme Gradient Boosting methods.
Training Physics-Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction
Physics-informed neural networks (PINNs) are a newly emerging research frontier in machine learning, which incorporate certain physical laws that govern a given data set, e. g., those described by partial differential equations (PDEs), into the training of the neural network (NN) based on such a data set.
Traffic Accident Risk Forecasting using Contextual Vision Transformers
This problem is commonly tackled in the literature by using data-driven approaches that model the spatial and temporal incident impact, since they were shown to be crucial for the traffic accident risk forecasting problem.
Traffic incident duration prediction via a deep learning framework for text description encoding
Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature of incident occurrence in space and time, a lack of information at the beginning of a reported traffic disruption, and lack of advanced methods in transport engineering to derive insights from past accidents.
Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation
Predicting the duration of traffic incidents is a challenging task due to the stochastic nature of events.
How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach
This paper explores the impact of electric vehicles (EVs) on traffic congestion and energy consumption by proposing an integrated bi-level framework comprising of: a) a dynamic micro-scale traffic simulation suitable for modelling current and hypothetical traffic and charging demand scenarios and b) a queue model for capturing the impact of fast charging station use, informed by traffic flows, travel distances, availability of charging infrastructure and estimated vehicle battery state of charge.
Boosted Genetic Algorithm using Machine Learning for traffic control optimization
Lastly, we propose a new algorithm BGA-ML combining the GA algorithm and the extreme-gradient decision-tree, which is the best performing regressor, together in a single optimization framework.
Graph modelling approaches for motorway traffic flow prediction
Traffic flow prediction, particularly in areas that experience highly dynamic flows such as motorways, is a major issue faced in traffic management.
Traffic congestion anomaly detection and prediction using deep learning
Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling.
Motorway Traffic Flow Prediction using Advanced Deep Learning
Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling.
Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications
The study focuses on estimating and predicting time-varying origin to destination (OD) trip tables for a dynamic traffic assignment (DTA) model.
Traffic signal control optimization under severe incident conditions using Genetic Algorithm
Secondly, we apply the optimal signal timings previously found under severe incidents affecting the traffic flow in the network but without any further optimization.
Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting
Predicting traffic incident duration is a major challenge for many traffic centres around the world.
