  We present a novel framework for modeling traffic congestion events over road
networks. Using multi-modal data by combining count data from traffic sensors
with police reports that report traffic incidents, we aim to capture two types
of triggering effect for congestion events. Current traffic congestion at one
location may cause future congestion over the road network, and traffic
incidents may cause spread traffic congestion. To model the non-homogeneous
temporal dependence of the event on the past, we use a novel attention-based
mechanism based on neural networks embedding for point processes. To
incorporate the directional spatial dependence induced by the road network, we
adapt the "tail-up" model from the context of spatial statistics to the traffic
network setting. We demonstrate our approach's superior performance compared to
the state-of-the-art methods for both synthetic and real data.
