Building the Ontra Planning Platform

What the platform helps agencies do

In practice, the Planning Platform helps agencies move from scattered inputs to clearer service decisions.

• Bring fragmented planning data into one workflow.
• Forecast how riders may respond to service changes.
• Generate options within real-world constraints.
• Compare scenarios by ridership, cost, access, equity, and operations.
• Explain recommendations to stakeholders.

This work is already shaping real planning efforts, including our work with Madera Metro and the City of Fresno.

This overview covers the system as a whole. The next three posts go deeper on demand modeling, optimization, and results analysis.

Why transit planning needs a connected system

Transit planning teams are asked to make decisions in an environment that is both data-rich and deeply constrained. They need to understand:

• Where people want to go.
• How current service performs.
• Which changes are operationally feasible.
• How different communities will be affected.

They also need to move quickly from an idea to a defensible recommendation that can survive technical review, stakeholder scrutiny, and real-world implementation.

Too often, the tools available to planners are not built for that full loop. Many systems are useful for drawing routes, visualizing data, or analyzing a manually created scenario. But they do not connect the full chain:

• Fragmented data.
• Demand forecasting.
• Constrained recommendation generation.
• Scenario comparison.
• Stakeholder communication.
• Implementation planning.

The data challenge makes planning harder

The data challenge makes the work even harder. Planning inputs are often spread across GTFS feeds, APC and AVL systems, spreadsheets, reports, demographic datasets, consultant analyses, and institutional knowledge. When no single system connects those inputs to actionable recommendations, agencies are left with long cycles of manual analysis, expensive external studies, and months of iteration before they can align around a plan.

We have been building the Ontra Planning Platform to close that gap: not by replacing planners, but by giving planning teams a better working environment. It brings together data, assumptions, modeling, optimization, collaboration, and explanation so agencies can move from complex planning questions to clear recommendations with more confidence.

Recommendations at the center of the workflow

The platform began with a familiar planning need: evaluate service ideas and compare them against a baseline. A user could define a set of options, run those options through the model, and see which alternative performed best.

That remains important. Planners still need to test specific ideas, compare stakeholder proposals, and understand the impact of changes they already have in mind.

From scoring ideas to surfacing options

But the product has been moving toward a broader workflow: helping agencies surface stronger options, not just score the options already drawn. A planner can still manually enter:

• A route change.
• A stop pattern.
• A zone.
• A frequency adjustment.
• A service policy they want to test.

At the same time, the platform can help generate additional options that may not have been obvious at the start of the process.

That is the core shift. User-defined ideas and system-generated recommendations can be evaluated side by side. The planner defines the goals, constraints, and planning context. The system helps expand the set of possibilities.

Demand modeling: predicting how riders may respond

A service recommendation is only useful if it reflects how people are likely to use the network.

That is why demand modeling is central to the platform. Ontra combines network, ridership, demographic, and travel pattern data to forecast how different service designs may affect rider behavior. This allows planners to evaluate both existing ridership and latent demand: not just who rides today, but who could be better served by a different network.

That distinction matters. Some corridors may have strong underlying demand but poor service quality. Some routes may look productive in isolation but create network-level inefficiencies. Some changes may reduce duplication while improving access to more destinations. Others may expand coverage but add cost or complexity.

By forecasting rider response across scenarios, the platform helps agencies compare alternatives based on expected outcomes rather than intuition alone. Planners can see how proposed changes may affect ridership, travel times, access to jobs and services, equity outcomes, operating cost, and fleet needs before committing to a plan.

Optimization: searching within real-world limits

Demand forecasts are only one part of the planning problem. A recommendation also has to work within the limits agencies actually face.

That is where optimization fits into the platform. Rather than asking planners to manually test every possible service design, the system can search across a larger set of options while respecting the rules of the project:

• Budget.
• Fleet.
• Service standards.
• Locked routes.
• Coverage needs.
• Equity priorities.
• Implementation constraints.

What optimization changes in the workflow

For this overview, the most important point is not the technical machinery behind the optimizer. It is the role optimization plays in the workflow. It helps move planning from “which of these few scenarios performs best?” toward “what stronger options are possible under the goals and constraints we care about?”

We explore that work in detail in our optimization post, including support for service configurations, flexible cost modeling, reliable runs, traceable inputs, and faster iteration. Here, the key idea is simpler: optimization helps generate recommendations that are not only high-performing, but implementable.

Building the full planning workflow

As the recommendation engine became more capable, the product around it also had to expand.

Real planning work requires more than a model run. A useful planning platform has to support the messy parts of agency planning:

• Incomplete GTFS feeds.
• Multiple agencies.
• Overlapping route variants.
• Service configurations.
• Disabled routes.
• Changing project assumptions.
• Large workspaces.
• Stakeholder questions.
• The need to preserve what happened in previous runs.

Supporting the full planning loop

Since the platform unveiling, we have been turning Ontra into a system that supports the full planning loop:

• Import and prepare network data.
• Build and edit routes, stops, zones, and service configurations.
• Create scenarios and variants.
• Set agency-defined objectives, budgets, fleet assumptions, service rules, fares, and KPIs.
• Run ridership forecasts and optimization.
• Compare results against baselines.
• Explain impacts through maps, metrics, equity views, and exports.
• Iterate again without losing context.

GTFS feeds need to be imported, validated, linked to agencies, and preserved across workspaces. Census and employment data need to be joined to routes, zones, and demand models. Travel times need to be prepared consistently. Scenario outputs need to be traceable back to the inputs, assumptions, and constraints that produced them.

It also has to support the way agencies actually work. Transit planning is rarely a solo exercise: a planner may sketch a route, an analyst may validate demand, an operations lead may review feasibility, and leadership may need a clear explanation of the tradeoffs. That is why the platform supports shared workspaces, scenario history, import/export flows, run management, and reliable synchronization for large planning documents.

The important shift is that planners are not only evaluating ideas they already have. They are discovering, refining, comparing, sharing, and explaining better options inside one connected workflow.

Analysis: explaining recommendations

Once a recommendation has been generated, planners still need to understand it, compare it, and explain it.

A transit plan is never judged by one number. Ridership matters. So does cost. So do:

• Travel times.
• Access.
• Coverage.
• Equity.
• Load factors.
• Stop-level impacts.
• Reliability.
• Implementation risk.

We expanded the results experience so planners can inspect a scenario from multiple angles. The platform now supports richer route and stop detail views, baseline comparisons, OD-flow analysis, census and demographic overlays, isochrone exploration, and equity-oriented accessibility analysis.

Turning results into a planning story

That matters because the best planning conversations are not just about whether a scenario "wins." They are about why it performs well, who benefits, who may be worse off, what tradeoffs are being made, and whether the result is operationally and politically credible.

A recommendation is only useful if it can be explained. Maps, metrics, exports, and scenario comparisons help planners move from model output to a story that stakeholders can understand: what changed, why it changed, and what the expected impact would be.

That explanation also helps agencies connect short-term decisions to long-term strategy. A strong plan is not only a high-performing network on paper. It is an implementation path: which changes should happen first, how many changes are reasonable in a given phase, and how early actions build toward the larger network vision.

From platform work to real projects

This work is already shaping real planning efforts, including our work with Madera Metro and the City of Fresno.

In Madera, Ontra Mobility is supporting a microtransit feasibility study with Madera Metro, Flexlynqs, and Southwest Strategies Group. That work explores how on-demand service can complement existing routes, shorten waits, and better connect riders to jobs, schools, healthcare, and daily needs.

In Fresno, Ontra Mobility is supporting a Transit On-Demand Feasibility Study with the City of Fresno, Flexlynqs, and Southwest Strategies Group. The study is evaluating opportunities to modernize Handy Ride paratransit and explore new on-demand public transit models, with a focus on flexibility, accessibility, and equity.

These projects are exactly why we built the Planning Platform: to help agencies move from big questions to clear, testable options.

What comes next

The work ahead is about making the platform even more useful in the field.

We are continuing to improve scenario building, results interpretation, equity analysis, demand modeling, optimization performance, GTFS workflows, and collaboration for larger planning teams. We are also learning from partners as they use the platform on real projects, where the constraints are sharper and the stakes are higher.

The mission is clearer than ever: help agencies design better transit networks faster, with more confidence and more transparency.

We are proud of how far the Planning Platform has come. We are even more excited about what it will help agencies do next, including some exciting news we will have to share later this week.