This paper uses a genetic algorithm to systematically examine the underlying characteristics of the optimal bus transit route network design problem (BTRNDP) with variable transit demand. A multiobjective nonlinear mixed integer model is formulated for the BTRNDP. The proposed solution framework consists of three main components: an initial candidate route set generation procedure (ICRSGP) that generates all feasible routes incorporating practical bus transit industry guidelines; and a network analysis procedure (NAP) that decides transit demand matrix, assigns transit trips, determines service frequencies, and computes performance measures; and a genetic algorithm procedure (GAP) that combines these two parts, guides the candidate solution generation process, and selects an optimal set of routes from the huge solution space. A $C++$ program code is developed to implement the proposed solution methodology for the BTRNDP with variable transit demand. An example network is successfully tested as a pilot study. Sensitivity analyses are performed. Comprehensive characteristics underlying the BTRNDP, including the effect of route set size, the effect of demand aggregation, and the redesign of the existing transit network issue, are also presented.

This paper uses a simulated annealing algorithm to solve the optimal bus transit route network design problem (BTRNDP) at the distribution node level. A multiobjective nonlinear mixed integer model is formulated for the BTRNDP. The proposed solution framework consists of three main components: An initial candidate route set generation procedure that generates all feasible routes incorporating practical bus transit industry guidelines; and a network analysis procedure that assigns transit trips, determines service frequencies, and computes performance measures; and a simulated annealing procedure that combines these two parts, guides the candidate solution generation process and selects an optimal set of routes from the huge solution space. Three experimental networks are successfully tested as a pilot study. A genetic algorithm is also used as a benchmark to measure the quality of the simulated annealing algorithm. The presented numerical results clearly indicate that the simulated annealing outperforms the genetic algorithm in most cases using the example networks. Sensitivity analyses are performed and related characteristics and tradeoffs underlying the BTRNDP are also discussed.