  Deep learning has been extended to a number of new domains with critical
success, though some traditional orienteering problems such as the Travelling
Salesman Problem (TSP) and its variants are not commonly solved using such
techniques. Deep neural networks (DNNs) are a potentially promising and
under-explored solution to solve these problems due to their powerful function
approximation abilities, and their fast feed-forward computation. In this
paper, we outline a method for converting an orienteering problem into a
classification problem, and design a customised multi-layer deep learning
network to approximate traditional optimisation solutions to this problem. We
test the performance of the network on a real-world parking violation dataset,
and conduct a generic study that empirically shows the critical architectural
components that affect network performance for this problem.
