Operationalizing modular autonomous customised buses based on different demand prediction scenarios
Rongge Guo (University of Huddersfield)
Saumya Bhatnagar (University of Huddersfield)
Wei Guan (Beijing Jiatotong University)
Mauro Vallati (University of Huddersfield)
Shadi Sharif Sharif Azadeh (TU Delft - Transport and Planning)
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Abstract
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.
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