Rethinking Public Transport Through Ridership Analysis

In times of tighter budgets and shifting mobility patterns, public transport agencies are under increasing pressure to make better-informed planning decisions. One of the most critical questions they face: How can we align services more closely with actual demand without compromising accessibility or efficiency?

One useful answer lies in ridership analysis, and more specifically, in tools that allow planners to calculate and understand passenger flows.

What PTV Lines Enables

PTV Lines is a platform developed for data-supported public transport planning. It helps plan, visualize and evaluate transit services through a variety of operational and demand-based indicators. Among its capabilities:

• Analyzing passenger volumes using origin-destination (OD) data or demand models
• Calculation and visualizing boardings, alightings, and transfers at stops
• Calculating occupancy rate and passenger distribution
• Evaluating metrics such as passenger kilometers/miles, load factors, and capacity kilometers/miles

The tool is compatible with GTFS and PTV Visum inputs, and it allows for planning scenarios based on different times of day, directions, or service variants.

Understanding Passenger Flows

At its core, PTV Lines models how passengers move through a transit network based on actual service characteristics: travel time, transfer options, and convenience (e.g., walking or waiting time). It assigns demand from OD matrices to the existing network and shows how changes, like new routes or altered frequencies impact ridership patterns.

This makes it possible to see not just where people are traveling, but how they interact with services across the network.

From Data to Planning Decisions

Ridership analysis in PTV Lines can inform a wide range of planning tasks, including:

• Identifying under- or over-utilized segments
• Adjusting frequencies by time of day and direction
• Redesigning routes to cover demand
• Supporting capacity planning and vehicle allocation
• Comparing scenarios in terms of costs, service kilometers/miles, and occupancy

For example, in a scenario presented during a recent webinar, a planner restructured a transit line by splitting it into two segments. They adjusted vehicle sizes and frequencies based on ridership insights – reducing number of required vehicles and service costs while improving capacity utilization.

Supporting Broader Questions

The insights generated through PTV Lines can help address questions that are currently relevant across many agencies:

• Which services deliver relatively high value at a lower operational cost?
• Where is it possible to reduce service without compromising core accessibility?
• What happens to transfer quality if frequencies are thinned out?

Methodological Foundations

The algorithms behind the ridership calculations in PTV Lines are built on scientific methods developed and refined over more than 40 years. These are not abstract models – they are grounded in practice and have been applied in real-world planning contexts across many regions.

The approach benefits from the experience of a team with deep roots in demand modeling. For example, Dr. Michael Bundschuh and Sebastian Sielemann have contributed their longstanding expertise from working with PTV Visum to the development of PTV Lines, ensuring both continuity and practical relevance.

A Practical Planning Tool

PTV Lines provides a lightweight, interactive environment for service planners to explore, test, evaluate the quality, and communicate planning ideas. Its scenario functionality and clear visual outputs also make it easier to involve stakeholders early in the process.

Final Thoughts

Ridership analysis isn’t just about numbers; it’s about understanding the lived reality of how people use public transport. With tools like PTV Lines, planners are better equipped to make data-informed decisions, test ideas quickly, and respond flexibly to changing conditions.

If you’re working in transit planning or policy and are interested in learning more about demand-responsive service design, it may be worth taking a closer look at what this kind of analysis can offer.