  The provision of paratransit services is one option to meet the
transportation needs of Vulnerable Road Users (VRUs). Like any other means of
transportation, paratransit has obstacles such as high operational costs and
longer trip times. As a result, customers are dissatisfied, and paratransit
operators have a low approval rating. Researchers have undertaken various
studies over the years to better understand the travel behaviors of paratransit
customers and how they are operated. According to the findings of these
researches, paratransit operators confront the challenge of determining the
optimal route for their trips in order to save travel time. Depending on the
nature of the challenge, most research used different optimization techniques
to solve these routing problems. As a result, the goal of this study is to use
Graph Convolutional Neural Networks (GCNs) to assist paratransit operators in
researching various operational scenarios in a strategic setting in order to
optimize routing, minimize operating costs and minimize their users' travel
time. The study was carried out by using a randomized simulated dataset to help
determine the decision to make in terms of fleet composition and capacity under
different situations. For the various scenarios investigated, the GCN assisted
in determining the minimum optimal gap.
