Abstract
ABSTRACT We present a method for identifying bus rapid transit routes optimized for serving the greatest number of passengers. Because formulating a relevant model is a complex problem, a particle swarm optimization scheme that improves on previous such approaches is applied. The optimization contains some more advanced strategies, such as dual population, self-adaptive inertia weight, and crossover operation, to enhance the abilities of the algorithm. Data collected from Dalian City, China, are then used for examining the proposed model and the optimization. Computational results demonstrate that the model is feasible and the improved particle swarm optimization is an effective method for optimizing bus rapid transit routes.
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