For a long time now it has been possible to get a view of road traffic and whether given routes are currently congested. Some routing applications have even begun to take into account traffic patterns when suggesting routes.
At the same time, public transport is lacking a display of how congested its systems are. While solutions exist to tell us which modes of transport to choose and where to change, the critical information that is missing is whether it will actually be possible to get on those buses and trains as directed, or how far along the platform to stand in order to do so
Collect, analyse, predict, display
OpenCapacity is a system to collect, analyse, predict and display the occupancy, accessibility and performance of public transport.
We measure passenger load by using existing public transport data sources, such as weight sensors, CCTV cameras, door sensors, and ticketing information. Cross-referencing these sources helps us strengthen the overall accuracy of passenger numbers.
Machine learning and predictive modelling
Displaying the occupancy of an approaching train is not enough. At large transport hubs many passengers will alight from a busy train, making room for others waiting on the platform.
It is therefore essential to analyse the data for patterns and cross-reference it against other data sources like weather and event calendars to model and predict passenger flow.
A particularly pertinent piece of information is the capacity after alighting, which is the result of OpenCapacity’s predictive modelling.
Real-time information display
Our system provides real-time information on available space inside train carriages, buses, and other modes of transport to passengers and operators via information screens, mobile apps and dashboards.
This will allow passengers to better plan their journeys and travel more comfortably, as well as allowing operators to further optimise their systems.