56,616 research outputs found
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. The
increasing amounts of generated traffic data have been used to train machine
learning predictors for traffic, however, this is a challenging task due to
inter-dependencies of traffic flow both in time and space. Recently, deep
learning techniques have shown significant prediction improvements over
traditional models, however, open questions remain around their applicability,
accuracy and parameter tuning. This paper brings two contributions in terms of:
1) applying an outlier detection an anomaly adjustment method based on incoming
and historical data streams, and 2) proposing an advanced deep learning
framework for simultaneously predicting the traffic flow, speed and occupancy
on a large number of monitoring stations along a highly circulated motorway in
Sydney, Australia, including exit and entry loop count stations, and over
varying training and prediction time horizons. The spatial and temporal
features extracted from the 36.34 million data points are used in various deep
learning architectures that exploit their spatial structure (convolutional
neuronal networks), their temporal dynamics (recurrent neuronal networks), or
both through a hybrid spatio-temporal modelling (CNN-LSTM). We show that our
deep learning models consistently outperform traditional methods, and we
conduct a comparative analysis of the optimal time horizon of historical data
required to predict traffic flow at different time points in the future.
Lastly, we prove that the anomaly adjustment method brings significant
improvements to using deep learning in both time and space
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. This paper proposes a new fusion framework for predicting the
incident duration from limited information by using an integration of machine
learning with traffic flow/speed and incident description as features, encoded
via several Deep Learning methods (ANN autoencoder and character-level LSTM-ANN
sentiment classifier). The paper constructs a cross-disciplinary modelling
approach in transport and data science. The approach improves the incident
duration prediction accuracy over the top-performing ML models applied to
baseline incident reports. Results show that our proposed method can improve
the accuracy by when compared to standard linear or support vector
regression models, and a further improvement with respect to the hybrid
deep learning auto-encoded GBDT model which seems to outperform all other
models. The application area is the city of San Francisco, rich in both traffic
incident logs (Countrywide Traffic Accident Data set) and past historical
traffic congestion information (5-minute precision measurements from Caltrans
Performance Measurement System)
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