  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.
