Heterogeneous Pointer Network for Travelling Officer Problem

Abstract

Traveling Salesman Problem (TSP) is a classic NP-hard problem in Combinatorial Optimization (CO), which has been widely studied. Traveling Officer Problem (TOP) derived from illegal parking in urban areas is a variant of TSP. Its solution aims to capture as many illegally parked vehicles as possible in a limited time. However, traditional methods of solving TSP cannot be applied to TOP because the illegally parked vehicle may leave before the officer arrives. Existing methods to solve TOP include heuristic search and deep learning algorithms such as ant colony optimization and feed-forward neural network. However, the performance based on capture rate and traveling distance of these algorithms is still comparably low. Hence, in this paper, we propose the heterogeneous pointer network to address this problem by modifying the encoder of the traditional pointer network to suit the spatial-temporal features of TOP. We conduct experiments using real-world datasets from Melbourne open data platform to show that our method achieves significant improvement and outperforms the existing algorithms based on capture rate and traveling distance.