Abstract
An efficient data-driven public transportation system can improve urban potency. In this research, we propose IntRoute, an Integer Programming (IP) based approach to optimize bus route planning. Specifically, IntRoute first contracts bus stops via clustering and then derives a new bus route via a mixed integer linear program (ILP). This two-phase strategy brings three major merits, i.e., a single bus route without any transfer, the minimal total time consuming, and an efficient optimization algorithm for large-scale problems. Experimental results show that our IntRoute significantly reduces the traditional commuting time in Sydney from 31.53 min down to 18.06 min on average.