# Building a Municipal Transit Planner That Works in the Real World

## From Street Data to Service Decisions

A municipal transit planner combines road geometry, stop locations, schedules, ridership, and operational constraints into one decision-support system. The difficult part is rarely finding a mathematically shortest route. Planners need to evaluate whether proposed service is accessible, reliable, affordable, and practical for operators to deliver. That means accounting for transfer penalties, walking distance, wheelchair-accessible stops, school dismissal peaks, construction detours, and minimum recovery time at route terminals.

## Modeling the Network

We represent the transit network as a time-dependent graph. Stops and selected intersections become nodes, while walking paths, road segments, and scheduled trips become edges. Each edge carries attributes such as travel time, distance, allowed vehicle type, service calendar, and accessibility status. Scheduled travel times provide a baseline, but observed automatic vehicle location data supplies more realistic estimates for congested corridors and unreliable intersections.

```yaml
routing:
  max_walk_meters: 800
  transfer_penalty_minutes: 6
  wheelchair_accessible_only: false
  minimum_layover_minutes: 8
```

## Planning for Demand and Equity

Ridership totals alone can produce misleading recommendations because high-demand corridors tend to reinforce their own advantage. The planner therefore evaluates access to essential destinations—hospitals, schools, grocery stores, and major employment areas—within defined travel-time thresholds. Results are segmented by neighborhood, income proxy, mobility needs, and time of day so staff can identify proposals that improve average travel time while unintentionally reducing service for vulnerable communities.

## Testing Service Changes

A proposed route or timetable is run through multiple scenarios before it reaches public consultation. The system estimates passenger travel times, missed connections, fleet requirements, operator hours, stop-level crowding, and on-time performance under typical and adverse traffic conditions. Monte Carlo simulations vary dwell times and segment delays, revealing whether a schedule that looks efficient on paper collapses after one late departure during the afternoon peak.

## Keeping Humans in the Loop

The planner does not automatically publish schedules or replace local judgment. Its purpose is to make assumptions visible and comparisons reproducible. Transit staff can inspect why a route scored poorly, override questionable inputs, and compare alternatives using the same service and equity metrics. When the municipality eventually approves a change, the model configuration, source-data versions, and evaluation results are retained so the decision can be audited and revisited after real-world performance data arrives.