As part of an Innovate UK initiative in 2020, we set out to deliver a scalable innovation based on new open datasets providing a consistent national analytical framework and MaaS deployment solution. Our innovation is then to bring together multi-modal user routing, a centralised system-optimal incentive system and network management systems to enable real-time operational, on-street and app-based influences and behavioural nudges, to maximise the uptake of sustainable transport.
Our Response
City Science developed a cutting-edge model to forecast travel demand within a MaaS system, based on a range of interventions. In the back end, the system uses a traffic model comprising microsimulation with dynamic routing, a mode choice model, a carbon calculator and an optimisation engine. The optimisation engine allows us to simulate many thousands of intervention scenarios (in this case mobility credits) to specific passenger groups, travel corridors, or journey-modes. Results from each simulation are automatically analysed to understand the impacts on key outcomes (in this case the focus was on carbon emissions).
Through a system of incentives (such as mobility credits) and disincentives (such as parking charges), the system is able to prioritise the most beneficial modes of transport to influence behaviour change and reduce carbon emissions across the system.
Outcomes
The research successfully concluded that for different sets of permissible incentives and penalties the model is able to produce a range of optimal carbon incentive schemes – many of which give large carbon savings and many that deliver benefits at no net cost (a sustainable business model). Users are able to explore these different options to find those with the preferred trade-off for their financial and carbon budget.