Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Public Transport Under Epidemic Conditions: Nonlinear Trade-Offs Between Risk and Accessibility

Gerhard Hiermann, Joana Ji, Ana Moreno, Rolf Moeckel, Maximilian Schiffer

TL;DR

This work tackles the clash between public health and urban mobility during epidemics by coupling an agent-based epidemic simulator ($EpiSim$) with an optimization-based passenger-flow model on a transit network. Using Munich as a case study, it shows that interventions shift infection risk toward households, that epidemic and transport policies interact nonlinearly, and that peak-hour and peripheral populations bear the brunt of restrictions. The integrated framework combines MITO-generated demand with epideictic dynamics and a column-generation–based routing solver to assess how facility closures and capacity cuts affect accessibility under epidemic constraints. The findings argue against blanket restrictions, advocating time- and space-differentiated, equity-aware policies that coordinate demand suppression with supply adjustments to preserve mobility and fairness. The approach provides a transparent, scalable basis for epidemic preparedness in urban transport planning and can be extended to adaptive behavior and multimodal networks.

Abstract

Epidemics expose critical tensions between protecting public health and maintaining essential urban mobility. Public transport systems face this dilemma most acutely: they enable access to jobs, education, and services, yet also facilitate close contact among travelers. We develop an integrated modeling framework that couples agent-based epidemic simulation (EpiSim) with an optimization-based public transport flow model under capacity constraints. Using Munich as a case study, we analyze how combinations of facility closures and transport restrictions shape epidemic outcomes and accessibility. The results reveal three key insights. First, epidemic interventions redistribute rather than simply reduce infection risks, shifting transmission to households. Second, epidemic and transport policies interact nonlinearly - moderate demand suppression can offset large capacity cuts. Third, epidemic pressures amplify temporal and spatial inequalities, disproportionately affecting peripheral and peak-hour travelers. These findings highlight that blanket restrictions are both inefficient and inequitable, calling for targeted, time- and space-differentiated measures to build epidemic-resilient and socially fair transport systems.

Public Transport Under Epidemic Conditions: Nonlinear Trade-Offs Between Risk and Accessibility

TL;DR

This work tackles the clash between public health and urban mobility during epidemics by coupling an agent-based epidemic simulator () with an optimization-based passenger-flow model on a transit network. Using Munich as a case study, it shows that interventions shift infection risk toward households, that epidemic and transport policies interact nonlinearly, and that peak-hour and peripheral populations bear the brunt of restrictions. The integrated framework combines MITO-generated demand with epideictic dynamics and a column-generation–based routing solver to assess how facility closures and capacity cuts affect accessibility under epidemic constraints. The findings argue against blanket restrictions, advocating time- and space-differentiated, equity-aware policies that coordinate demand suppression with supply adjustments to preserve mobility and fairness. The approach provides a transparent, scalable basis for epidemic preparedness in urban transport planning and can be extended to adaptive behavior and multimodal networks.

Abstract

Epidemics expose critical tensions between protecting public health and maintaining essential urban mobility. Public transport systems face this dilemma most acutely: they enable access to jobs, education, and services, yet also facilitate close contact among travelers. We develop an integrated modeling framework that couples agent-based epidemic simulation (EpiSim) with an optimization-based public transport flow model under capacity constraints. Using Munich as a case study, we analyze how combinations of facility closures and transport restrictions shape epidemic outcomes and accessibility. The results reveal three key insights. First, epidemic interventions redistribute rather than simply reduce infection risks, shifting transmission to households. Second, epidemic and transport policies interact nonlinearly - moderate demand suppression can offset large capacity cuts. Third, epidemic pressures amplify temporal and spatial inequalities, disproportionately affecting peripheral and peak-hour travelers. These findings highlight that blanket restrictions are both inefficient and inequitable, calling for targeted, time- and space-differentiated measures to build epidemic-resilient and socially fair transport systems.

Paper Structure

This paper contains 28 sections, 6 equations, 21 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Data and Methods
  3. Scenario Definition
  4. Travel Demand Generation
  5. Epidemic Simulation
  6. Combinatorial Optimization Approach (CO)
  7. Price and branch solver
  8. Discussion
  9. Results
  10. Epidemic Validation and Dynamics
  11. Macroscopic Analysis
  12. Temporal accessibility and bottlenecks
  13. Spatial Inequalities
  14. Implications
  15. Synthesis
  16. ...and 13 more sections

Figures (21)

  • Figure 1: Overview of the integrated framework. MITO generates individual travel demand, EpiSim simulates infection dynamics under epidemic measures, and the optimization model evaluates public transport accessibility under capacity constraints.
  • Figure 2: Public transport network of Munich used in the study, separated by mode (Bus, Tram, Subway, Rail). The network representation is based on GTFS schedule data and provides the spatial and modal framework for the travel demand generation and subsequent epidemic--transport analyses.
  • Figure 3: Validation of epidemic dynamics: Comparison of simulated epidemic trajectories (EpiSim) with official statistics from the Robert Koch Institute (RKI). (a) Daily infections reproduce the epidemic curve but peak earlier and at higher levels, consistent with under-reporting in official data. (b) Daily hospitalizations align closely with observed values.
  • Figure 4: Infection events by day by activity type, 7-day moving average: Daily infections disaggregated by activity setting. Before restrictions, workplaces, schools, and leisure activities contribute substantially to infection events. After restrictions, transmission shifts predominantly to households. The figure shows how epidemic measures redistribute rather than eliminate infection risks.
  • Figure 5: Total number of routed and restricted passengers across scenarios. Laissez-faire settings with halved PT capacity generate severe mismatches, while facility closures reduce demand and alleviate bottlenecks. The figure illustrates the nonlinear interaction of epidemic measures and transport restrictions in shaping accessibility.
  • ...and 16 more figures