TransitExplorer: a bus network analysis tool

Improving public transport networks in developing countries relies upon a detailed understanding of what already exists. This can be captured in timetable data formats, the most common being the General Transit Feed Specification (GTFS). Current technology available to work with these formats is focused upon transport _operations_ -- franchising, payments, etc. -- or analyses from only a 'top down' view of how accessible city regions are in terms of travel time. Such technology is also designed for the back-office, and often requires large amounts of training. We have begun to develop simple technology that records GTFS timetable data using the local street network (drawn from the free worldwide OpenStreetMap project), which in turn offers a potential step change in online analysis tools for understanding individual city streets, transport routes, or neighbourhoods.

This analysis can take a series of bus or transit routes (captured as stop locations) and, by estimating their routing on the local road network, analyse the routes that traverse every single section of road in a city. This granularity is incredibly powerful, as it allows aggregation to build a picture of

* the busiest streets visited by a particular route, indicating where performance may be problematic or capacity is constrained
* the most congested transport routes in a neighbourhood, identifying priority areas for improvements
* the aggregation of route data for a whole city, confirming the corridors and routes that need the most significant investment or identifying 'bottlenecks' in specific major roads.

This online, _interactive_ analysis using a combination of road network data and bus or transit routes in GTFS format is incredibly powerful for planning local public transport, and can be used in conjunction with a number of derived statistics: number of vehicles (per time period), seat capacity by route, seat capacity by road corridor, or vehicular emissions. Aggregating these statistics in different ways is a key part of accurate, evidence-led transport planning, but current tools available require large amounts of geographic or mathematical expertise to operate and may take many days to produce usable outputs. The simplicity of the proposed software instead means query data can be calculated in real-time.

Improving these processes to the point where we could train local partners in developing countries to benefit from interactive analytical tools would both improve the efficiency with which we could complete transport planning projects as a company, and have knock-on effects for clean urban transport around the world.