  We present a novel data-driven approach of learning traffic flow patterns of
a transportation network given that many instances of origin to destination
(OD) travel demand and link flows of the network are available. Instead of
estimating traffic flow patterns assuming certain user behavior (e.g., user
equilibrium or system optimal), here we explore the idea of learning those flow
patterns directly from the data. To implement this idea, we have formulated the
traffic-assignment problem as a data-driven learning problem and developed a
neural network-based framework known as Graph Convolutional Neural Network
(GCNN) to solve it. The proposed framework represents the transportation
network and OD demand in an efficient way and utilizes the diffusion process of
multiple OD demands from nodes to links. We validate the solutions of the model
against analytical solutions generated from running static user
equilibrium-based traffic assignments over Sioux Falls and East Massachusetts
networks. The validation result shows that the implemented GCNN model can learn
the flow patterns very well with less than 2% mean absolute difference between
the actual and estimated link flows for both networks under varying congested
conditions. When the training of the model is complete, it can instantly
determine the traffic flows of a large-scale network. Hence this approach can
overcome the challenges of deploying traffic assignment models over large-scale
networks and open new directions of research in data-driven network modeling.
