Integrated timetabling and scheduling of modular autonomous vehicles under uncertainty

Abstract

Addressing the Integrated Timetabling and Vehicle Scheduling (TTVS) problem is important for improving transit operations. Recently, the emerging modular autonomous vehicles composed of modular autonomous units have made it possible to dynamically adjust on-board capacity to better match space-time imbalanced passenger flows. This paper introduces an integrated framework for the TTVS problem in a dynamically capacitated and modularized bus network, considering time-varying and uncertain passenger demand. In this network, units can be decoupled and rerouted across different lines within the network at various times and locations, providing passengers with the opportunity to make in-vehicle transfers -- that is, to transfer between lines while remaining onboard. We formulate a stochastic programming model to jointly determine the optimal robust timetable, dynamic formations of vehicles, and cross-line circulations of units, aiming to minimize the weighted sum of operator and passenger costs. To solve realistic instances, we propose a tailored integer L-shaped method that dynamically solves the model through a rolling-horizon optimization algorithm. Furthermore, we extend our approach into a novel learning-based real-time decision-making framework that fine-tunes timetables and re-optimizes vehicle schedules in response to evolving and new demand realizations during operations. At its core is a scenario-retention method that selects a representative subset of scenarios using a machine learning model trained on scenario-level features. This subset is then incorporated into the optimization, ensuring both computational scalability and solution quality. To validate the effectiveness of our methods, we conduct experiments based on the Beijing bus network.