This WP aims at moving towards a more efficient and sustainable mobility system by combining travel
options into individually optimized multimodal services, ultimately tailored for each user and each trip
(MaaS). This WP will also explore how to use Smart Hubs in urban areas to facilitate last-mile
To deal with these complex problems of smart mobility, we plan to build on the Albatross activity-based
mobility model. NEON will extend Albatross to (1) encompass MaaS, (2) integrate freight transport
though the development of smart hubs, and (3) integrate energy and climate models.
These research efforts will achieve substantial efficiency gains by allowing stakeholders (customers and
operators) to cooperate and bundle passenger’s trips as well as freight on the last mile. A better
coordination between freight and passengers flows within an electric mobility (WP5) will lead to less
environmental pressure in cities. This will lead to new business models that better exploit the shared
existing infrastructure in space and time. With over a hundred publications and thousands of citations,
Albatross has a solid scientific basis. The main risk associated with this WP is to not have the relevant
data to calibrate the model. However, the involved parties have the needed experience, as well as the
required network to collect the relevant data and create their own case-studies.
- Calibrate the MaaS and Smart Hubs adoption models (econometric based model) with the Stated
preference (SP) data (M48) and determine which MaaS services (bundle configuration, pricing
scheme and price) should be offered to whom in order to maximize the adoption rate (M12).
- Investigate the societal boundaries to multi-modal travel behaviour (M12).
- Define mobile smart hubs as a system, including the demand to be serviced, the type and number of
facilities used, the layout of the network and the transport modes supporting the operations of the
- Calibrating daily use of MaaS and Smart Hubs (Probabilistic Decision Tree based model, Dynamic
Programming, Multi objective optimization algorithm) with revealed data combined with SP (M36)
- Determine the optimum locations for the smart hubs within cities in light of forecasted demand
- Determine how mobile smart urban freight logistics hubs can be used dynamically in time and space
as flexible consolidation points in urban centres (M36).
- Determine which fleet configuration is the best in each hub with respect to the demand of the users
- Research regulatory boundaries to the integration of multi-modal MaaS systems and smart hubs
- Determine how dynamic pricing can be employed to coordinate the use toward a more
environmentally friendly passenger and freight travel behaviour (M36).
- Develop city logistic algorithms (freight and personal mobility) including the smart mobility concepts
- Model and simulate the location requirements of smart hubs in neighbourhoods (M36).
- Describe the administrative and business models adopted (M48).
- Implement the new model heuristics in the formalism of Albatross so it can drive smart mobility
behaviour in the integral model (M48).
- At least 4 papers on MaaS
- At least 6 papers on Smart Hubs
- At least 4 papers on Smart Mobility
- Algorithms for the optimum locations of the smart hubs and optimum configuration of services
within each hub.
- Algorithms to optimize locations of smart hubs including smart mobility concepts.