Overcrowding is a universal low point of commuting. Although most mass transit schedules respond to peak traffic times, they do so in static ways that don’t account for real-time crowd changes and do little to prioritize rider comfort.
Passive rider data can play a pivotal role in analyzing patterns and optimizing shared space on mass transit. Crowd-sourced data could also contribute—it’s already being used in other areas of transport, for example, Boston’s Street Bumps app allows drivers to automatically report road hazards to the city. Thinking along those lines: Could leveraging rider data reap its own set of benefits? Could we eliminate rush hour by tailoring schedules to fit real-time commuter needs?
James is visiting a friend for the week, and has just arrived in the city during rush hour. He assumes space will be an issue, so he uses his transit app to indicate his accessibility preferences for the trip.
The transit authorities crowdsource rider input to designate an appropriate amount of accessible space within train cars. James identifies and boards a specific car by using the electronic displays on the platforms and cars.
James finds a seat easily. For even more comfort, he folds up a nearby seat to make space for his luggage.
To increase public mass transit ridership, embracing and harnessing emerging technologies will create adaptive and efficient systems that will provide additional value and enjoyment for riders. Moment has taken the first steps by exploring the possibilities in five key areas of impact: shared space, productivity, safety and supervision, ticketing, and wayfinding.
