Traffic incident duration prediction via a deep learning framework for
text description encoding
- URL: http://arxiv.org/abs/2209.08735v1
- Date: Mon, 19 Sep 2022 03:16:13 GMT
- Title: Traffic incident duration prediction via a deep learning framework for
text description encoding
- Authors: Artur Grigorev, Adriana-Simona Mihaita, Khaled Saleh, Massimo Piccardi
- Abstract summary: This paper proposes a new fusion framework for predicting the incident duration from limited information.
The application area is the city of San Francisco, rich in both traffic incident logs and past historical traffic congestion information.
- Score: 9.424574945499842
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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 $60\%$ when compared to standard linear or support vector
regression models, and a further $7\%$ 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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