| Summary: | In 2014, the emergence of public on-demand, ride-sharing services, known as microtransit, (re)captured the attention of techno-positive urbanists. Echoing the same arguments for demand- response transit in the 1970s, new transit technology startups like Via, Chariot, and Bridj touted microtransit as a more affordable alternative to private ride-hailing services, while promising greater efficiency and improved customer experiences compared to traditional bus services. Proponents believed this "disruptive transportation innovation" could alleviate traffic congestion and reduce vehicle emissions if scaled successfully.
Following mixed results from early pilot programs over the previous five years, only the truly disruptive Covid-19 pandemic launched microtransit into an accelerated phase of adoption. Many transit agencies replaced underperforming bus routes with microtransit, while others used federal funding to launch new pilots designed to connect riders to existing transit nodes. Yet the sparsity of public data on microtransit services prevents researchers unaffiliated with any major technology providers from establishing baseline service metrics or comprehensively evaluating the performance of these new programs in relation to each other, let alone assess any broader effect on travel patterns.
This thesis provides the first comprehensive documentation of microtransit's growth and trends in service design in the U.S. as a first step toward assessing its current state. A newly compiled dataset reveals the diversity and variability of microtransit programs in their service goals, types, and designs. Finally, this thesis proposes a new assessment framework to help microtransit administrators balance competing trade-offs like cost-efficiency, reliability, and flexibility based on their service goals and transit needs.
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