Leveraging user data from demand-responsive transit

Collecting user data is where DRT really comes into its own. By nature, traditional fixed-route-and-schedule transit is accessed anonymously. Beyond information about ticket sales and where and when a user accesses a service, transport planners often have little to go on. They can take a guess, of course—anyone boarding a bus at 8 am is likely to be a commuter on their way to work. Then again, they could be a visitor to the city, on their way to a business meeting, so not a regular user. Either way, you’re still largely in the dark about their day-to-day transit needs.

By contrast, DRT users must sign up to a platform to access the service, providing (anonymized) personal data in the process. This instantly yields better insights into user demographics, location, and preferences that can be used to create user profiles. Once signed up, users book via app, so each ride is traceable to a specific user. This lets you know exactly who accesses the service, when, where, and for how long. Put together, these datasets allow transport planners to analyze in-depth the movements of specific individuals or groups and configure services accordingly. For example, by putting on more for heavy users, or providing active micromobility for younger demographics.

There are, however, some challenges with this approach. For example, the need to register can be a barrier to certain user groups like the elderly. It also excludes occasional visitors like tourists or those on a business trip or attending an event, hampering the ability to get a 360º view of transport use year-round. In addition, it’s always necessary to provide a backup phone line for anyone struggling to use the app but it can be harder to identify callers versus app users, which leads to further gaps in the data.

The key to overcoming these challenges is to get as many people as possible on board, literally and figuratively, with DRT. By raising awareness of the service and making it as easy as possible to register and use it, even for occasional visitors, we can achieve a more even distribution of users that better reflects society, minimizing data gaps relating to age, gender, social status, and digital literacy.

For more thought on transit services and accessibility, see our previous post on the subject.

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