A two-phase optimization model for the demand-responsive customized bus network design. (February 2020)

• Record Type: Journal Article
• Authors: Huang, Di; Gu, Yu; Wang, Shuaian; Liu, Zhiyuan; Zhang, Wenbo
• Is Part Of: Transportation research. Volume 111(2020)
• Page Start: 1
• Page End: 21
• Publication Date: 2020-02
• Subjects: Customized bus -- Demand-responsive transit -- Hierarchical decision-making problem -- Dynamic insertion -- Branch-and-bound algorithm; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2019.12.004
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Propose an integrated decision-making framework for the demand-responsive customized bus network design. Formulate the network design problem as a two-phase optimization model. Formulate the interactive mechanism between the operator and passengers in a hierarchical decision-making model. Abstract: This paper proposes a new optimization model for the network design problem of the demand-responsive customized bus (CB). The proposed model consists of two phases: inserting passenger requests dynamically in an interactive manner (dynamic phase) and optimizing the service network statically based on the overall demand (static phase). In the dynamic phase, we propose a hierarchical decision-making model to describe the interactive manner between operator and passengers. The CB network design problem is formulated in a mixed-integer program with the objective of maximizing operator's revenue. The CB passenger's travel behavior is measured by a discrete choice model given the trip plan provided by the operator. A dynamic insertion method is developed to address the proposed model in the dynamic phase. For the network design problem in the static phase, the service network is re-optimized based on the confirmed passengers with strict time deviation constraints embedded in the static multi-vehicle pickup and delivery problem. An exact solution method is developed based on the branch-and-bound (B&B) algorithm. Numerical examples are conducted to verify the proposed models and solution algorithms.