# Building a Municipal Transit Planner Around Real-World Constraints

Municipal transit planning is less about finding the mathematically shortest route and more about balancing service coverage, operating cost, accessibility, and reliability. A useful planner must combine street geometry with schedules, stop locations, vehicle capacities, transfer rules, and local policies such as minimum service levels for hospitals or low-income neighborhoods. Most systems ingest General Transit Feed Specification (GTFS) data, then enrich it with road closures, ridership counts, and historical travel times.

## Modeling the Network

The routing layer typically represents stops and intersections as nodes, with walking paths and transit segments as weighted edges. Unlike a static road graph, edge costs change throughout the day: a bus trip may take 12 minutes at 10:00 a.m. and 25 minutes during the evening peak. Frequency-based services also require expected wait-time calculations, while scheduled services need exact departure matching. Accessibility constraints add another dimension because a route that includes stairs or a non-accessible vehicle cannot be offered to every rider.

## Configuring Planning Policies

Operational policies should remain outside the routing code so planners can adjust them without rebuilding the application. A small configuration file can define acceptable walking distance, transfer penalties, and the minimum buffer used when changing vehicles:

```yaml
routing:
  max_walk_meters: 700
  transfer_penalty_seconds: 240
  minimum_transfer_seconds: 120
  wheelchair_accessible_only: false
```

## Generating and Comparing Scenarios

When evaluating a proposed route change, the system runs the same set of origin-destination trips against both the current and candidate networks. Results are aggregated by neighborhood, time period, and rider profile to reveal who benefits and who loses service. Useful metrics include median travel time, missed-connection risk, jobs reachable within 45 minutes, required fleet hours, and the percentage of residents within walking distance of frequent transit.

## Operating the Planner in Production

A production deployment usually separates slow network preparation from fast trip queries. GTFS imports, map matching, and travel-time calibration can run overnight, producing a versioned graph that is loaded into memory by routing workers. Real-time vehicle positions and service alerts are applied as temporary overlays, allowing delayed or cancelled trips to affect results without rebuilding the entire graph. Each response should record the graph version, data timestamp, and policy configuration so staff can reproduce decisions during public review.