Sarah Popelka is a Research Assistant at Urban AI and is currently pursuing a Master's in Urban Governance from SciencesPo. In this article, she analyzes the insights of our Urban AI Conversation #11 with Aurélien Cottet (Instant System) and Scott Shepard (Asistobe) : “Beyond Mobility as a Service”.
What lies in store for the fate of urban transportation, as more and more private mobility providers and ancillary technology implementations disrupt the traditional ways in which we move about our cities? Aurélien Cottet and Scott Shepard, in conversation with Urban AI, envision a safer, more democratic, more sustainable, and more inclusive future for urban mobility. This future, they argue, will be made possible through well-governed, publicly-managed mobility-as-a-service (MaaS) platforms, which can support the thoughtful use of artificial intelligence and machine learning technologies (AI/ML).
MaaS, which Shepard defines as a “digital layer that enables micromobility and other shared mobility offer to be presented to consumers, whether they’re directly through an individual micromobility app or through an aggregation of services,” refers to the use of information system technology to mediate trip planning and transportation access, entirely within a digital platform. MAAS has existed in many iterations since the introduction of MaaS Finland to the market in 2016. Cottet and Shepard describe this trajectory as progressing from Business-to-Consumer (B2C, characterized mobility operators directly providing service to consumers) to Business-to-Business-to-Consumer (B2B2C, characterized by mobility operators providing service to consumers through partnerships with their employers) and Business-to-Government-to-Citizen (B2G2C, characterized by mobility operators providing service to consumers through apps managed by public authorities. It is in the B2G2C MaaS model that Cottet and Shepard see the most potential for building robust transportation networks that work for both people and the planet.
From conflict to cooperation
Historically, an air of contention has tarnished the relationship between government entities and private mobility providers. Some accounts have referred to the early micromobility days as the “scooter wars” or the “Wild West,” with each entity vying to define the shape of mobility in the digital age, often to the disservice of the other. This pervasive lack of cooperation manifested in degraded service for some fixed-route public transit systems, destructive competition among mobility providers, fragmented mobility offerings, and siloed data systems. B2G2C MaaS, however, provides a mutually beneficial common ground for coordinated implementation that supports more effective and cooperative mobility implementations.
If designed well, the collaborative nature of B2G2C MaaS benefits both private mobility operators and government entities. On the operator side, standing up a MaaS platform and marketing the service both come with considerable overhead costs. In the B2G2C model, since government transportation agencies foot the bill for platform development and maintenance and have a stake in ensuring that the mobility offerings on their platform are widely utilized by citizens, the barriers to entry for nascent and smaller mobility operators significantly decrease. On the public sector side, public authorities can benefit from a novel regulatory instrument — holding a provider’s participation in the MaaS platform contingent on their policy compliance. Additionally, the data sharing and interoperability that MaaS platforms necessarily require serve to contribute to increased regulatory oversight, by virtue of the fact that a majority of mobility providers are consolidated under one integrated, democratic platform, rather than dispersed between a number of tightly-held operator-specific compartments.
Towards sustainable and inclusive mobility
However, Cottet and Shepard make clear that building a comprehensive, government-aggregated MaaS is not the sole answer when it comes to building transportation systems that support efficient, sustainable, and equitable mobility, but rather a singular infrastructural layer upon which a broader transportation planning framework can flourish. Procedurally, enabled by MaaS, planners have unprecedented access to comprehensive data on mobility across numerous modes, data that they can exploit to refine service offerings and assume a more proactive approach in optimizing network flows and responding to disruptions. Shepard breaks this process into three stages. The first stage consists of exploring the network: with comprehensive data on mobility usage at the hands of citizens and policy makers, urban mobility stakeholders can gain a much deeper understanding of how to balance the transportation landscape, including identifying transit deserts (a geographically-constrained dearth of service) and locations with an oversaturation of shared mobility offerings. The second stage consists of optimizing the network: using predictive analytics to make short-term improvements along environmental, economic, or socially-driven lines of intervention. The last stage consists of incorporating AI/ML technologies as a continued tool in holistic sustainable urban mobility plans, as one component of an interdisciplinary approach to ensuring sustainable public transportation and complementarity between fixed route transit and microtransit.
The massive volume and comprehensive, intermodal nature of the data that B2G2C MaaS platforms make available provide a rich basis for powerful AI/ML implementations, which can extend beyond the functioning of the platform itself to the design and promotion of lasting network interventions. Possibilities for AI/ML-enabled planning improvements include demand and need prediction, real-time network responsiveness and adaptability to collisions and other disruptions, and need-based customization to improve accessibility across lines of difference. Thoughtful, well-governed, democratic MaaS implementations enable all urban mobility stakeholders- public agencies, private mobility operators, and civil society- to collaboratively leverage AI/ML to improve transit offerings and transportation planning, and collectively ensure a future in which mobility services are efficient, sustainable, and accessible to all.
By Sarah Popelka
Urban AI Conversations are webinars during which members of the Urban AI Community present their latest research, solutions, or ideas.
