Context
Shifting workplace attendance patterns following the pandemic created uncertainty about transit demand and revenue. Available signals pointed in different directions, and a single-point forecast was unlikely to resolve the planning question.
The organization needed a structured way to frame the question, evaluate the available signals, and model the potential scenarios—so that planning discussions could be grounded in structured evidence rather than an unverified single projection.
Business question
What does a shift in remote-work norms mean for transit ridership and revenue—and how do you structure the analysis so that decision-makers can act on a range of plausible scenarios rather than waiting for certainty that may not arrive?
Role — exactly what I did
- Framed the business question and scoped the analysis
- Compared relevant sources and signals on a consistent basis
- Structured and applied the scenario modeling approach
- Translated scenario outputs into decision-support material
- Presented findings and implications to relevant stakeholders
Approach
- Framed the analysis around the decision-relevant question: what range of scenarios is plausible, and what do they imply for planning?
- Compared relevant labor market and transit signals from available sources on a consistent scope
- Built a scenario structure capturing a range of outcomes without requiring a single-point forecast
- Translated scenario outputs into demand and revenue implications for planning use
Personal contribution
- Scoped and framed the analysis to keep it decision-relevant and appropriately bounded
- Gathered and compared relevant signals from available sources
- Built the scenario modeling structure and applied it to demand and revenue implications
- Produced the decision-support outputs and presented findings to relevant stakeholders
Conceptual overview
The diagram below is a conceptual reconstruction for illustration purposes only. It does not reproduce internal documentation.
Findings
- Available signals pointed to a range of plausible scenarios rather than a single trajectory
- Scenario framing helped clarify which planning decisions were robust across scenarios and which depended on specific assumptions
- The analysis grounded planning discussions in structured evidence rather than a single-point forecast
- The approach identified where additional signal monitoring would improve future decision quality
Decisions influenced
- Financial and commercial planning assumptions for the relevant period
- Communication approach around uncertainty and planning confidence
- Monitoring priorities for ongoing demand signals
- Framework for revisiting assumptions as more information became available
Learnings
- Scenario modeling is most useful when framed around planning decisions, not just forecasts
- Presenting a range of outcomes with structured implications is more actionable than defending a single number
- Scoping the analysis carefully prevents scope creep while maintaining decision relevance
- Acknowledging uncertainty explicitly builds more credibility than concealing it