Mobility-On-Demand is one of the most important fields in transportation and mobility research in the recent years. Some providers of ride-pooling services – such as MOIA – are already actively offering services to customers. Ride-pooling services are strongly oriented towards the individual users. As individual travel patterns are evolving from the classic individual car-ownership to collective travel patterns including carsharing or ride-pooling, it is necessary to focus on correlations between different individual mobility demands. Furthermore, mobility is increasingly influenced by national and regional or local laws and regulations. General regulations such as dynamic pricing, also need to be considered in the setup of future transportation models.
In Hamburg, MOIA currently operates a fleet of 450 electric vehicles, offering ride-pooling services which combines ride requests of different users. Within the first phase of the accompanying scientific research, an empirical online-survey was carries out, gathering information of MOIA users up to now (nation wide for Germany) and non-users (in the Hamburg area) with a total of more than 12,000 successfully completed questionnaires. The survey was designed to examine the socio-demographics of users, their travel behavior as well as their evaluation of the services. The survey also included a choice experiment in order to analyze pricing and travel time sensitivity relating to new mobility services (such as MOIA, E-Scooters, bike- and carsharing).
In the next step, a microscopic agent based travel model will be set up using mobiTopp. The model is able to represent different individual travel demand scenarios, their effects on an individual and collective level as well as their correlations with local or urban regulatory approaches. It will reflect all options for decision making within a travel chain, from the first decision to make the trip to the mode choice within the transportation system. This kind of scenario cannot sufficiently be modelled using standard conventional macroscopic traffic models, as they are only scarcely able to represent correlations on an individual level. Hence, for this accompanying research, we will especially design a custom-made, integrated, multi- and intermodal travel demand model using mobiTopp.