# Traffic incident duration prediction via a deep learning framework for text description encoding

arXiv: https://arxiv.org/abs/2209.08735 · HTML: https://mlsanity.com/paper/2209.08735/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 19 Sep 2022.

## Builds on (most cited first, 9 of 9)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Parametric Exponential Linear Unit for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1605.09332/) (2016)
- [Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights](https://mlsanity.com/paper/1909.09638/) (2019)
- [A Countrywide Traffic Accident Dataset](https://mlsanity.com/paper/1906.05409/) (2019)
- [A deep learning approach for detecting traffic accidents from social media data](https://mlsanity.com/paper/1801.01528/) (2018)
- [Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation](https://mlsanity.com/paper/2205.05197/) (2022)
- [Motorway Traffic Flow Prediction using Advanced Deep Learning](https://mlsanity.com/paper/1907.06356/) (2019)
- [MSE Loss with Outlying Label for Imbalanced Classification](https://mlsanity.com/paper/2107.02393/) (2021)
- [Graph modelling approaches for motorway traffic flow prediction](https://mlsanity.com/paper/2006.14824/) (2020)

## Built on by (most cited first, 3 of 3)

- [Enhancing Traffic Incident Management with Large Language Models: A Hybrid Machine Learning Approach for Severity Classification](https://mlsanity.com/paper/2403.13547/) (2024)
- [A Bi-level Framework for Traffic Accident Duration Prediction: Leveraging Weather and Road Condition Data within a Practical Optimum Pipeline](https://mlsanity.com/paper/2311.00634/) (2023)
- [Proactive Road Safety Intervention in Australia: Predicting Risky Driving Hotspots from Connected Vehicle Data](https://mlsanity.com/paper/2608.16913/) (2026)

## Abstract (arXiv)

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).