Abstract

The customized bus (CB) service of large transport terminals can provide passengers with convenient transfers and door-to-door services, which has the potential to help ease the pressure of arriving passenger flow in large transport terminals. Considering the costs of operators and passengers, this paper establishes a multi-commodity network flow optimization model to simultaneously obtain departure schedule, passenger-to-vehicle assignment and routing under the condition of multiple vehicle types. It provides more passengers with high-quality CB service and obtains higher vehicle capacity utilization. Accordingly, the solution is proposed that combines a branch-and-cut algorithm with grid-density-based clustering. An illustrative example verifies the effectiveness and tests the impact of the departure time-window. The case study of Chengdu city compares the results under different types of vehicle schemes and conducts the sensitivity analysis of penalty and minimum load ratio. The experiments conclude: (i) The proposed algorithm stably improves computation speed (76.38%) without affecting the optimal results; (ii) a loose time-window can serve more people, but the excessively loose one no longer has impacts on the results. (iii) The multi-type of vehicle scheme performs best compared to the single type of vehicle schemes.

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