In this paper, a concept of an IoT based solution for a Bike Sharing Platform is presented. The developed platform implements a new layer of instrumentation that allows data collection that could be used for, for instance, route suggesting algorithms, fleet management and distribution, traffic analysis, crashes detection, crashes reconstruction, and driver profiling. All these possibilities are of major interest in today’s socioenvironmental context. The use of bicycles and sharing platforms should be promoted and encouraged, but for that to happen, some limitations regarding safety and insufficient user engagement must be overcome. The work developed under this paper aims to mitigate those barriers and provide a functional, innovative, and efficient solution.

Shared autonomous vehicles (SAV) are expected to become part of mobility on-demand systems in the near future. The vehicles’ ability to move autonomously has the potential to change the way transportation is perceived. For instance, a system with autonomous vehicles can be used by persons without a drivers’ license and the fact of not needing staff to drive or relocate vehicles enables services to expand into less dense areas. In an interurban context SAVs can contribute to increase the availability of public transport. This service that nowadays is performed mainly by bus, with the payment of drivers’ wage and the need of diverting from the direct path to cover more demand, could be performed by smaller vehicles using faster routes making it more attractive to the daily commuter.

Urban mobility is under pressure, resulting from the still ongoing urbanization, urban densification, urban expansion, car-dominated cities and increasing mobility demand. In order to protect the accessibility, livability, safety, sustainability and efficiency of the cities of the future, transport policies focus on increasing the utilization rate of public transport and shared mobility as these are sustainable modes of transport in urban areas. This research is focused on an application of a demand-responsive autonomous one-way carsharing service as a first- and last mile solution to increase the attractiveness of public transport, currently suffering from last-mile connectivity problems. Multiple researches are published which aim at modeling this Autonomous Mobility on-Demand (AMoD) systems for certain case studies in order to assess the impact of AMoD systems on urban mobility. The far most popular transport modeling paradigm in doing this is agent-based modeling (ABM), in which the urban mobility system performance results from the interaction of agents according to their individual behavior. However, there are certain shortcomings in research on AMoD systems. Firstly, these researches primarily focus on the supply-side of AMoD systems, often assuming certain extreme demand situations like a replacement of all taxi demand for AMoD demand. An alternative for assuming such demand situations is predicting the demand using existing travel demand estimation models. Secondly, the impact of operational variables on the financial viability of AMoD systems is not yet clear, while the financial viability plays an essential role in the implementation of automated vehicle applications. Therefore, this research aims to predict the financial viability of AMoD systems based on the main costs and revenues for various operational scenarios of AMoD systems. The objective of this research is to answer the main research question: What is the financial viability of Autonomous Mobility on-Demand operations as a first- and last-mile solution for public transport in urban areas?