Dynamic Adaptive Policymaking for implementing Mobility as a Service (MAAS)

Abstract

Mobility as a Service (MaaS) is an emerging innovative transport concept. It offers its users a tailored transport service package, similar to a mobile phone package, that provides a seamless journey within their city-region. Proponents believe that MaaS can provide a high-level of convenience that can appeal to vehicle-drivers enough for them to give up their private vehicles. It presents a shift away from the current ownership based transport system toward a consumption based one, thus has a potential to increase public transport usage. Additionally, it can contribute to the transport system performance by increasing its efficiency, reducing congestion, decrease the need for parking space, and enhancing the level of accessibility. However, there are several uncertainties, which can limit its large-scale implementation, such as preferences of public transport operators, travelers acceptance, liability, concern about privacy, and MaaS’ contribution towards the overall sustainability of the transport system. In order to cope with these uncertainties, we put forward an adaptive approach to implementing MaaS system for an urban area of Nijmegen, the Netherlands. The approach allows policymakers to create policies that are more robust for future situations and can adapt as the future unfolds and uncertainties resolve. This so-called Dynamic Adaptive Planning (DAP) focuses on addressing opportunities and vulnerabilities of implementing an initial MaaS-system and the how to take advantage of respectively protect against these. According to the DAP-scheme, we start with the implementation of a BrengFlex system, a demand responsive taxibus service that replaced current inefficient bus lines. The necessary conditions for success for this basic policy include sufficient support from local public transport providers and other stakeholders, technology for reservation and dynamic routing/scheduling is available, and the acceptance of the new transport service by the city’s residents. We then propose certain actions that should be taken right away to increase the robustness of the basic policy, including actively collaborate with stakeholders, such as public transport providers, governments, and citizen institutes (Shaping Action), soft launching the system to iron out any possible bug before official launch (Hedging Action), and organization of public consultation and working groups to address potential conflicts (Mitigating Action). In addition, a monitoring system is specified to trigger future adaptations of the basic policy as additional knowledge, such as acceptance and performance of the DRT, emerge. For example, the stakeholders’ levels of acceptance or, the level of ridership, and cost-revenue ratio are monitored and responsive actions are prepared to be implemented in case trigger-events occur. For example, a too low level of acceptance by travelers may prompt a Corrective Action to adjust the basic plan, such as an expansion of service-region or a decrease in ticketing pricing, whereas a high level of acceptance and usages will trigger a roll out of MaaS as a Capitalizing Action. The application of an adaptive approach to a hypothetical case of implementing a MaaS system is presented in this paper. We demonstrate how DAP can offer an alternative planning method, which in contrary to the traditional approach, addresses uncertainty explicitly and incorporates adaptation as part of the process. This approach enhances the robustness of a given plan, amid surrounding dynamic changes and unpredictability, thus enhance the likelihood of its success.