  Understanding and predicting the duration or "return-to-normal" time of
traffic incidents is important for system-level management and optimisation of
road transportation networks. Increasing real-time availability of multiple
data sources characterising the state of urban traffic networks, together with
advances in machine learning offer the opportunity for new and improved
approaches to this problem that go beyond static statistical analyses of
incident duration. In this paper we consider two such improvements: dynamic
update of incident duration predictions as new information about incidents
becomes available and automated interpretation of the factors responsible for
these predictions. For our use case, we take one year of incident data and
traffic state time-series from the M25 motorway in London. We use it to train
models that predict the probability distribution of incident durations,
utilising both time-invariant and time-varying features of the data. The latter
allow predictions to be updated as an incident progresses, and more information
becomes available. For dynamic predictions, time-series features are fed into
the Match-Net algorithm, a temporal convolutional hitting-time network,
recently developed for dynamical survival analysis in clinical applications.
The predictions are benchmarked against static regression models for survival
analysis and against an established dynamic technique known as landmarking and
found to perform favourably by several standard comparison measures. To provide
interpretability, we utilise the concept of Shapley values from the domain of
interpretable artificial intelligence to rank the features most relevant to the
model predictions at different time horizons. For example, the time of day is
always a significantly influential time-invariant feature, whereas the
time-series features strongly influence predictions at 5 and 60-minute
horizons.
