In this thesis, we focus on modeling the traffic congestion in the city of Atlanta. We are
trying to predict future congestion events on the main highways in Atlanta. We present a
novel framework for modeling traffic congestion events over road networks based on mutually
exciting Spatio-temporal point process models. We use multi-modal data by combining
traffic sensor networks data with police reports, which contain two types of triggering
mechanisms for congestion events. To capture the non-homogeneous temporal dependence
of the event on the past, we introduce a novel attention-based approach for the point process
model. To incorporate the directional spatial dependence induced by the road network, we
adapt the “tail-up” model from the spatial statistics context. We demonstrate the superior
performance of our approach compared to the state-of-the-art for both synthetic and real
data.M.S
