# Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation

arXiv: https://arxiv.org/abs/2205.05197 · HTML: https://mlsanity.com/paper/2205.05197/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 54 references that are in the map.
- First posted on arXiv 10 May 2022.

## Builds on (most cited first, 5 of 5)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Countrywide Traffic Accident Dataset](https://mlsanity.com/paper/1906.05409/) (2019)
- [Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting](https://mlsanity.com/paper/1905.12254/) (2019)
- [Boosted Genetic Algorithm using Machine Learning for traffic control optimization](https://mlsanity.com/paper/2103.08317/) (2021)

## Built on by (most cited first, 6 of 6)

- [Enhancing Traffic Incident Management with Large Language Models: A Hybrid Machine Learning Approach for Severity Classification](https://mlsanity.com/paper/2403.13547/) (2024)
- [Inferring Heterogeneous Treatment Effects of Crashes on Highway Traffic: A Doubly Robust Causal Machine Learning Approach](https://mlsanity.com/paper/2401.00781/) (2024)
- [Traffic incident duration prediction via a deep learning framework for text description encoding](https://mlsanity.com/paper/2209.08735/) (2022)
- [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)
- [Predicting the duration of traffic incidents for Sydney greater metropolitan area using machine learning methods](https://mlsanity.com/paper/2406.18861/) (2024)
- [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 duration of traffic incidents is a challenging task due to the stochastic nature of events. The ability to accurately predict how long accidents will last can provide significant benefits to both end-users in their route choice and traffic operation managers in handling of non-recurrent traffic congestion. This paper presents a novel bi-level machine learning framework enhanced with outlier removal and intra-extra joint optimisation for predicting the incident duration on three heterogeneous data sets collected for both arterial roads and motorways from Sydney, Australia and San-Francisco, U.S.A. Firstly, we use incident data logs to develop a binary classification prediction approach, which allows us to classify traffic incidents as short-term or long-term. We find the optimal threshold between short-term versus long-term traffic incident duration, targeting both class balance and prediction performance while also comparing the binary versus multi-class classification approaches. Secondly, for more granularity of the incident duration prediction to the minute level, we propose a new Intra-Extra Joint Optimisation algorithm (IEO-ML) which extends multiple baseline ML models tested against several regression scenarios across the data sets. Final results indicate that: a) 40-45 min is the best split threshold for identifying short versus long-term incidents and that these incidents should be modelled separately, b) our proposed IEO-ML approach significantly outperforms baseline ML models in 66% of all cases showcasing its great potential for accurate incident duration prediction. Lastly, we evaluate the feature importance and show that time, location, incident type, incident reporting source and weather at among the top 10 critical factors which influence how long incidents will last.