The quick expansion of emerging mobility services is attracting increasing attention from cities, large corporations and entrepreneurs. Shared mobility systems, demand responsive transport, the concept of mobility as a service (MaaS) and other mobility innovations are regarded as a means to reduce the use of private cars. At the same time, smart mobility solutions come with a number of challenges: on the one hand, their financial viability depends on their ability to create, attract and satisfy demand while minimising operational costs; on the other hand, cities face the challenge of developing regulatory frameworks that maximise the potential of these new services to contribute to sustainable mobility and prevent undesired effects like the abandonment of public transport and the abusive use of public space.
Big data for the planning and management of smart mobility solutions
The combination of big data sources able to describe the potential demand for new mobility solutions, such as mobile network data, with the data generated by smart mobility services and MaaS applications (recorded trips, customer profiles, etc.) opens exciting opportunities for developing machine learning models and simulation frameworks that help understand and predict the adoption and use of new mobility services. Nommon integrates such predictive models into decision support tools with tailored user interfaces and visualisation frameworks to help our clients plan and manage the mobility of the future:
- We help transport operators and mobility service providers make strategic planning decisions (e.g., choose the best cities for new deployments) and optimise operations (e.g., design vehicle relocation and pricing policies that take into account the interaction between demand and supply).
- We support city authorities in the design of policies and regulations that maximise the synergies of emerging mobility services with public transport and active mobility.