# Building a Data-Driven Workflow for a Municipal Transit Planner

A municipal transit planner spends less time drawing lines on maps than most people assume. The real work is reconciling service goals, vehicle availability, labor rules, street constraints, ridership patterns, and political commitments into schedules that can actually be operated. A good planning workflow treats every route change as a testable hypothesis, not just a cartographic edit.

## Start With Operational Data

The practical baseline is usually GTFS, automatic vehicle location data, passenger counts, and stop-level dwell observations. GTFS tells us what the agency intended to operate, while AVL and APC data show what riders and operators actually experienced. Comparing scheduled travel time against observed travel time by segment is often the fastest way to find where reliability is being lost.

## Model Service Changes Explicitly

For each proposed change, the planner should define the assumptions in a versioned configuration file. This keeps scenario work reproducible and makes it easier to explain why one proposal requires more buses than another.

```yaml
scenario: "route_12_peak_frequency"
route_id: "12"
headway_minutes:
  weekday_peak: 10
  weekday_base: 20
layover_percent: 12
minimum_recovery_minutes: 6
fleet_buffer_percent: 8
```

## Validate Against Real Constraints

The model needs to account for cycle time, recovery time, pull-in and pull-out time, operator relief points, and terminal capacity. A frequency improvement that looks affordable in a spreadsheet can fail if the terminal only has space for two buses or if the proposed layover location conflicts with curb regulations. This is where transit planning becomes an engineering problem as much as a policy problem.

## Communicate Tradeoffs Clearly

The final output should not just be a map. Decision-makers need to see frequency, span, fleet requirement, operating cost, expected passenger impact, and reliability risk side by side. A municipal transit planner earns trust by showing the tradeoffs plainly: more coverage may mean less frequency, faster trips may mean longer walks, and better reliability often requires service hours that are invisible to riders but essential to operations.