Optimisation and Data-Driven Modelling for Resilient Public Transport Operations: From Hub Rescheduling to Route-Level Demand Estimation

June 29, 2026

Public transport systems play a fundamental role in enabling sustainable urban mobility, yet their reliability is often affected by disruptions that propagate across interconnected routes and hubs. To address this challenge, this thesis develops and implements optimisation-driven, data-informed frameworks to support real-time hub-level rescheduling and improve route-level demand inference with passenger-centric objectives. At the hub scale, the model builds on a holding control formulation and is extended through two methodological contributions: a robust optimisation approach to account for operational uncertainty, and an online rolling-horizon strategy to incorporate real-time data updates. At the route scale, an origin–destination inference model processes boarding and alighting counts in buses to support route-level demand analysis, while a temporal symmetry variant exploits commuting-period symmetry to further enhance estimation under limited data. The frameworks are implemented in Python and applied to the Ermesinde transport hub in the Porto Metropolitan Area, using operational, infrastructure and demand data. The simulation results indicate that the developed approach can enhance hub-level coordination and waiting time reduction, and provide meaningful insights into passenger flow patterns along routes, supporting more informed operational interventions. These outcomes demonstrate the potential of integrated optimisation and data-driven modelling to improve the reliability and passenger experience in public transport systems.

Authors in the community:

• 

  G

  Gonçalo Bruno Vitorino de Sousa

  ist1100179

Supervisors of this institution:

• 

  G

  Guilherme Filipe Bonifácio Ribeiro

  ist190263

• 

  A

  António Ramos Andrade

  ist153478

mechanical-engineering - Mechanical engineering

por - Portuguese

Instituto Superior Técnico