Tabu search strategies for the public transportation network optimizations with variable transit demand

Systematic tabu search (TS)-based heuristic methods are put forward in this article and applied for the design of public transportation networks with variable demand. A multi-objective nonlinear mixed integer model is formulated. Solution methodologies are proposed, which consist of three main components: an initial candidate route set generation procedure (ICRSGP) that generates all feasible routes incorporating practical bus transit industry guidelines; a network analysis procedure (NAP) that decides transit demand matrix, assigns transit trips, determines service frequencies, and computes performance measures; and a Tabu search method (TSM) that combines these two parts, guides the candidate solution generation process, and selects an optimal set of routes from the huge solution space. Comprehensive tests are conducted and sensitivity analyses are performed. Characteristics analyses are undertaken and solution qualities from different algorithms are compared. Numerical results clearly indicate that the preferred TSM outperforms the genetic algorithm used as a benchmark for the optimal bus transit route network design problem without zone demand aggregation.