Operationalizing modular autonomous customised buses based on different demand prediction scenarios

Rongge Guo, Saumya Bhatnagar, Wei Guan, Mauro Vallati*, Shadi Sharif Azadeh

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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This paper presents a novel framework for customised modular bus systems that leverages travel demand prediction and modular autonomous vehicles to optimise services proactively. The proposed framework addresses two prediction scenarios with different forward-looking operations: optimistic operation and pessimistic operation. A mixed integer programming model in a space-time-state network is developed for the optimistic operation to determine module routes, schedules, formations and passenger-to-module assignments. For the pessimistic case, a two-stage optimisation procedure is introduced. The first stage involves two formulations (i.e., deterministic and robust) to generate cost-saving plans, and the second stage adapts plans with control strategies periodically. A Lagrangian heuristic approach is proposed to solve formulations efficiently. The performance of the proposed framework is evaluated using smartcard data from Beijing and two state-of-the-art machine learning algorithms. Results indicate that the proposed framework outperforms the real-time approach in operating costs and highlights the role of module capacity and time dependency.

Original languageEnglish
Article number2296498
Number of pages31
JournalTransportmetrica A: Transport Science
Publication statusPublished - 2023


  • a mixed integer programming model
  • a two-stage optimisation procedure
  • Customized modular bus
  • machine learning
  • travel demand prediction


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