Multi-objective Demand Responsive Transit Scheduling in Smart City: A Multiple Populations Ant Colony System Approach

The demand responsive transit (DRT) is a type of bus that does not have a fixed route and provides flexible travel services within a certain area range based on passengers’ needs. Due to its service flexibility and cost economy, DRT has become an important component of smart city construction. Essentially, DRT is an NP-hard combinational optimization problem, and traditional deterministic methods are not suitable for solving practical DRT problems. At the same time, most existing studies concern minimizing operational costs as the main optimization objective, with insufficient consideration of passenger demand satisfaction. This article treats minimizing operational costs and minimizing penalty costs as two optimization objectives, so as to model the DRT as a multi-objective optimization problem. To solve the multi-objective DRT scheduling problem efficiently, this article follows the novel and efficient multiple populations for multiple objectives (MPMO) framework and proposes a multiple populations ant colony system (MPACS) approach. Two ant colony populations are used to optimize the two objectives (i.e., minimize operational costs and minimize penalty costs), and an information share mechanism is designed to achieve co-evolution between the two populations, thereby finding Pareto solutions for the multi-objective DRT problem. The proposed MPACS has been tested on five datasets and compared with the first-come-first-served greedy algorithm and the nearest neighbor greedy algorithm, verifying its effectiveness and efficiency in solving the multi-objective DRT problems.