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

• Published in: Transportation research. Part C, Emerging technologies Emerging technologies, 2022-08, Vol.141, p.103721, Article 103721
• Main Authors: Grigorev, Artur, Mihaita, Adriana-Simona, Lee, Seunghyeon, Chen, Fang
• Format: Article
• Language: English
• Subjects: Arterial road versus motorways incident management; Classification; Extreme-boosted decision-trees; Incident duration prediction; Intra–extra joint optimisation; Light gradient boosting modelling; Machine learning; Regression
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2022.103721

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 using quantiled duration groups and varying threshold split. 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. •We propose a novel bi-level framework for predicting the incident durations.•We predict incident duration on three data sets with different road networks.•Short-term and long-term traffic accidents should be modelled separately.•Different incident duration extrapolation scenarios analysed.•Our proposed IEO-ML approach outperformed baseline ML models in 66% of cases.