Microtransit—shared transportation that offers dynamic routing and scheduling to efficiently match demand—is emerging as an ally to fixed-route services. However, its positive impacts are too often constrained by the politics and economics imposed by existing transit infrastructure. This paper proposes a solution that ‘‘flips transit on its head.’’ By rapidly prototyping microtransit services across cities and analyzing supply-demand mismatches, it is possible to launch truly data-driven transit services. To illustrate the framework, a unique dataset generated from a year of Dallas Area Rapid Transit’s GoLink service, one of the largest ondemand microtransit services in North America, is used. Mapping and machine learning are combined to empower planners to ‘‘join the dots’’ when (re)designing fixed-route transit lines. It is shown that microtransit should not simply fill in the gaps left by inefficiently scheduled bus routes: by incorporating it fully into their planning processes, cities and transit agencies could dramatically reverse the fortunes of public transit.

Key findings

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Mayaud et al. write that microtransit can “flip transit on its head” with four important steps.

1. A microtransit prototype must be established and start operating in a community;

2. The demand and supply data should be analyzed to identify hotspots;

3. The hotspot data should be used to “nudge” riders to adapt to more rigid routes and schedules in certain areas;

4. Launch a data-driven microtransit program that optimally uses a mix of both fixed and flexible.

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The authors emphasize that once the fixed route service has been established, there must always be a feedback loop between the demand data and analysis so that as the population changes over time, the service can adapt to be more efficient.

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