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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.

168,982 papers · 148 categories

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5 results for “motorway”

Identifying subdominant collective effects in a large motorway networkphysics.soc-ph

Study reveals subdominant correlations in motorway network dynamics.

problem Understanding correlations between motorway sections for network functionality.

method Spectral analysis of correlation matrix to identify subdominant collectivities.

result Subdominant collectivities affect large parts of the traffic network.

motorway network correlation analysis collectivity traffic dynamics North Rhine-Westphalia

2022-02-15abs ↗pdf ↗

Transitions between quasi-stationary states in traffic systems: Cologne orbital motorways as an examplephysics.soc-ph

Study identifies transitions between traffic modes on Cologne motorways.

problem Understanding transitions between different traffic modes.

method Constructed state transition network, identified dominant states using PageRank algorithm.

result Identified seasonal dependence in traffic modes.

traffic systems quasi-stationary states state transition network PageRank Cologne motorways

2023-02-28abs ↗pdf ↗

Motorway Traffic Flow Prediction using Advanced Deep Learningcs.LG

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.

traffic prediction deep learning motorway Sydney CNN-LSTM

2019-07-15abs ↗pdf ↗

QTIP: Quick simulation-based adaptation of Traffic model per Incident Parametersstat.ML

QTIP improves traffic prediction in sudden disruptions.

problem Traffic models fail during sudden disruptions.

method Simulation-based framework for real-time adaptation.

result QTIP improves traffic prediction in critical minutes of incidents.

traffic prediction sudden disruptions simulation-based real-time adaptation incident parameters

2020-03-09abs ↗pdf ↗

Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boostingcs.LG

Predicts arterial road incident duration with extreme gradient boosting.

problem Predicting incident duration on arterial roads, especially with limited data.

method Bi-level framework combining classification and regression models.

result Extreme gradient boosting outperformed other models by 53%.

incident duration arterial roads traffic flow classification regression

2019-05-29abs ↗pdf ↗