One of the keys for a successful MaaS (Mobility as a Service) ecosystem is support for multi-modal transportation using micromobility solutions. MaaS solutions include services such as carpool and ridesharing companies, bicycle-sharing systems programs, scooter-sharing systems and carsharing services as well as on-demand “pop-up” bus services. Micromobility refers to a range of small, lightweight vehicles operating at speeds typically below 25 km/h (15 mph) and driven by the users personally, unlike rickshaws.
For micromobiliy MaaS to succeed, two factors need to be addressed – increasing usage and maximizing ROI. Micromobility companies aim to inspire a global mobility revolution by making the urban space more accessible for everyone. They are using Mobility Intelligence insights to increase ridership revenue and optimize ROI in both existing sites and on new city expansion through station placement and pinpointing demand. Striving to provide optimal value to customers while increasing revenue.
MaaS intelligence is all about a better understanding of the demand for different modes of transportation. In the case of existing docking stations this means understanding the surrounding area ridership demand. Then an optimum placement of stations can be improved and expansion to new cities and areas supported.
Micromobility patterns and demand are hard to predict
Understanding micromobility patterns requires looking at both micro and macro movement and transportation and cannot be achieved just by looking through one lens – e.g., data from any single public MaaS company – be it ride sharing, scooter, or ebike solution. Only by fusing mobility data from multiple sources can we identify the thousands of points where station reallocation would result in an improved ROI and increased ridership.
MaaS Intelligence uses OD metrics,multi-layered data, location intelligence, trip duration, trip distance, frequency of visits, length of stay and many more parameters to offer insights crucial for optimal service deployment.
Data driven decision making means MaaS providers need visibility not only into their existing riders, but also into potential riders and other mobility modes. Seeing a full picture of the entire journey provides a better understanding of customers’ needs and preferences in order to identify and anticipate potential future demands.
Yet, most mobility service providers depend solely on their own data when making decisions and entering new markets or regions. Census data or urban movement data is available, but it is typically outdated and comes from surveys and other antiquated data collection methodologies, and it leaves a great deal of room for misinformation and lack of an accurate and complete mobility picture. Relying solely on first party data and somewhat outdated survey data isn’t enough for forward thinking technology companies. To make informed, strategic, and data driven decisions that better service customers, increase ridership, and optimize future expansion MaaS Intelligence is a must-have.
The challenge is made worse because when using limited data that lacks appropriate depth for real-time market visibility, it is impossible to reach actionable mobility intelligence insights.
MaaS intelligence generates actionable insights
In order to provide micromobility companies with more insights and visibility into their riders’ behavior (which is the only way to create a viable service) a number of sources of data need to come together.
- Multi-modal patterns
- Vehicle traffic
- Door to door transit routes
- Proximity to POIs
- Logistics insights, and
- Demand ranking
This data is analyzed in relation to businesses in areas that have a great deal of foot traffic due to attractions like stores and restaurants, and allocated intent plus ridership share. The main focus of the analysis is to assess how many trips were done by MaaS, specifically trips that range from 1km to 8km.
This type of information includes where people are riding to, and where they came from, important for linkage. Linking the first and last contact point of the journey is essential for providing a complete solution where the commuter has a station to start and end their journey. Without origin and destination there is no continuity as to where to place the next station. By mapping these crucial insights, a user centric, holistic experience can be provided.
Through the collection and merging of qualitative and quantitative data, visibility into the complete rider journey can be achieved focusing on, first and last mile. Specifically for MaaS micromobilty solutions like bike sharing that can gain a lot from MaaS intelligence that extracts key data points on origin and destination demand for optimum deployment of new stations in new cities, and relocation of existing stations to provide maximum value and the best experience for their riders.
Are you interested in learning more about MaaS intelligence and its impact, schedule a chat with one of our data experts here.