  The main question to address in this paper is to recommend optimal signal
timing plans in real time under incidents by incorporating domain knowledge
developed with the traffic signal timing plans tuned for possible incidents,
and learning from historical data of both traffic and implemented signals
timing. The effectiveness of traffic incident management is often limited by
the late response time and excessive workload of traffic operators. This paper
proposes a novel decision-making framework that learns from both data and
domain knowledge to real-time recommend contingency signal plans that
accommodate non-recurrent traffic, with the outputs from real-time traffic
prediction at least 30 minutes in advance. Specifically, considering the rare
occurrences of engagement of contingency signal plans for incidents, we propose
to decompose the end-to-end recommendation task into two hierarchical models:
real-time traffic prediction and plan association. We learn the connections
between the two models through metric learning, which reinforces partial-order
preferences observed from historical signal engagement records. We demonstrate
the effectiveness of our approach by testing this framework on the traffic
network in Cranberry Township in 2019. Results show that our recommendation
system has a precision score of 96.75% and recall of 87.5% on the testing plan,
and make recommendation of an average of 22.5 minutes lead time ahead of Waze
alerts. The results suggest that our framework is capable of giving traffic
operators a significant time window to access the conditions and respond
appropriately.
