How İzmir's transit network found its way back after the pandemic
A data-driven analysis of how İzmir's public transport network recovered after COVID-19, and how the rider mix changed along the way.
Municipal ridership records Monthly recovery modeling Linked React chart system Astro reporting flow
Executive Summary
A recovery analysis of İzmir's transit system built from four years of open data, showing not only when ridership returned but which fare groups came back first.
Role
Data analysis, systems framing, interaction design
Impact
Makes a large public dataset usable for readers trying to understand how an urban system changed after disruption.
Records analyzed
19K+
Daily public transport records across multiple transit modes.
Time window
2021-2024
Four years of recovery behavior after the pandemic collapse.
Linked views
2
Ridership recovery and fare-group composition move on the same monthly cursor.
Context & Problem Space
The pandemic radically altered urban mobility, but simple top-line ridership charts do not explain how public infrastructure recovered. This case study uses municipal transport data to show the timing, scale, and demographic shape of İzmir's recovery.
Reporting Workflow
Pulled open municipal transport data and normalized totals by mode and fare group.
Rebuilt the visuals as native linked charts so overall recovery and rider composition could share one monthly cursor.
Structured the narrative around indexed recovery, absolute totals, and share mode instead of static snapshots.
Article & Visual Analysis
The COVID-19 pandemic reshaped urban mobility everywhere, and İzmir was no exception. Lockdowns, remote work, and shifting daily routines drained volume from buses, ferries, trains, and trams almost at once.
Using published open data, it is now possible to trace the pace of that return. More than 19,000 daily public transport records from 2021 to early 2024 show not just how many riders came back, but which groups rebuilt the network first.
Reading the recovery
The rebuilt visual system keeps two questions together. The upper panel tracks monthly ridership by transit mode, with a trips / index toggle so the recovery curve can be read either in raw volume or relative to each mode’s starting point. The lower panel follows fare groups, with a trips / share toggle that makes the student return legible without letting total volume dominate the frame.
Recovery timeline
How ridership returned, and who returned first
The upper chart tracks how each transit mode came back after the pandemic shock. The lower chart shows which fare groups rebuilt the system month by month.
Metro
Tram
İZBAN commuter rail
Other
Bus (ESHOT, İZULAŞ, etc.)
Ferry (İZDENİZ)
Full fare
Student
Teacher
60+
Free
Other
Selected fare composition
In Mar 2024, the lower view shows whether the rebound was driven more by students, full-fare riders, or the smaller concession groups.
Full fare35.5%
Student37.5%
Teacher0.6%
60+1.8%
Free18.7%
Other5.9%
Buses (ESHOT and İZULAŞ) remained the heavy lifters of the network, carrying the highest volume even during the weakest phase of recovery. The rail systems, İZBAN and Metro, posted steeper rebound curves as regular commuting patterns returned. The ferry system, while smaller in total volume, followed the same broader recovery path across the bay.
The rider mix did not return evenly
The recovery was not uniform across all demographics. When the data is broken down by fare type, the social layers of that return become easier to see.
Full-fare adults were among the slowest to return, likely because hybrid work patterns outlasted the first reopening phase. Students, by contrast, re-entered the system in sharper waves. In share mode, those swings become much easier to read because the chart stops asking whether the month was large and starts asking who made it large.
The steady presence of the 60+, Free, and Other categories also matters. These groups include older residents, transit personnel, and social assistance recipients. For them, public transport was not simply a commuting option; it remained a lifeline through the most disrupted years.
Conclusion
The data tells a story of resilience, but not a uniform one. By 2024, İzmir’s transit network had regained its footing, yet the path back varied by mode and by rider group. That matters because it turns a broad post-pandemic question into a systems story about who depends on the network first, and how a city recovers through its shared infrastructure.
Data Source: İzmir Metropolitan Municipality Open Data Portal Note: Interactive visualizations are built as native React views so recovery totals and rider mix can be read on the same monthly timeline.
Related Work
Selected adjacent work that extends the same problem space from a different angle.
