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.
29.05.23
Come meet with the Shotl team at the upcoming UITP Summit in Barcelona, from 4th to 7th of June.
24.01.22
2022 is off to a good start and packed with good news. To begin with, Shotl is expanding its Demand Responsive Transportation (DRT) operation in Barcelona. The chosen area covers the neighborhoods of Montbau and Vall d'Hebron.
24.12.18
Our algorithmic solution matches multiple passengers, that are heading in the same direction, with a moving vehicle, minimizing ride distances and waiting time, thus offering a better transportation service and experience.