City Science developed a tool to assist in the planning of Mobility Hubs, focussing on determining the optimum location to place them. Mobility Hubs are sites where a collection of transport modes and non-transport services meet, examples include park and rides or railway stations which have bus connections, shops and bike parking.
Introducing a Mobility Hub into a space can be highly beneficial, with potential benefits including:
- Convenience: Boosting convenience for multi-modal trips through the provision of multiple services in the same location which reduces connection times
- Sustainable First-Last Mile Trips: Providing sustainable short-distance connections to public transport services
- Improving Safety: Offering a safer and more comfortable dwell time, improving the experience for more vulnerable users
- Improving Accessibility: Providing space for adapted and inclusive modes
- Raised Profile of Sustainable & Shared Modes: Providing a clear, distinct area with well-advertised services
The above benefits can only be achieved if the Mobility Hub is planned and designed sufficiently. However, the specific planning and design of Mobility Hubs is currently left to individual Local Authorities with limited modelling or data support. As such, we proposed the development of an evidence-based tool which would help identify potential locations for the development of a mobility hub.
Our project aligned with the following DfT Priorities:
- Improving Connectivity
- Building Confidence in The Network
- Tackling Climate Change
Our project was split into four stages:
- A Literature Review of current policies, guidance and best practice in the planning of Mobility Hubs
- User Engagement with Local Authorities to discuss how they currently plan for Mobility Hubs and ideas they may have to improve the process
- Undertaking a thorough Data Collation exercise to ascertain what data was required, what data was available and where data gaps existed
- Development of a Tool which helps to determine the optimum location for a Mobility Hub
Our user engagement and the literature review shaped the way in which our tool was developed, providing insight that the locating the mobility hub was the task in most need of an evidence-based approach. Service provision is already well catered for in guidance and Local Authorities have a consistent approach to planning for it. The aim was to provide a solution that could be rolled out across Local Authorities with the outputs published on their own websites.
We developed a usable tool to support in the planning of Mobility Hubs. The tool uses a mode-choice, logit model to calculate the potential number of users of a mobility hub based on its location as well as associated costs such as city centre parking, bus fares and bus waiting times. The initial model looks at the choice between driving and using a park and ride site. The tool considers a set area around a specified region, be that a specific point (e.g. a town centre) or a corridor (e.g. the route between a town centre and a large residential area). It assesses multiple locations within the assessment area, looking at regularly spaced points to create a grid-based output. These grid areas can then be analysed further to identify the best place within them to locate the mobility hub (considering other factors such as the location of existing infrastructure).
The outputs of the tool can be overlaid with supporting data sets such as demographic data (e.g. deprivation indices) or infrastructure availability (e.g. existing bus routes). This then helps users make an informed decision on the best location of a mobility hub based on both potential usage as well as deliverability.
Following the successful completion of this project we have utilised the tool to aid in the assessment of a park and ride site as well as impacts on parking demand in a city centre following parking charge increases. We shared our findings at our industry webinar session, and will continue to engage with interested Local Authorities to share learnings further. The integration of wider data sets is planned to help inform decision making processes as well as increasing the types of transport modes available (e.g. walking and cycling) to further increase the utility of this tool.