Oxfordshire Mobility Model: MIMAS project update – December 2020

Making it easier to be data-driven

MIMAS (https://mimas.services/) is a flexible, scalable transport modelling tool. The tool is being developed for Oxfordshire and is designed to scale to other counties in the future.

Previous updates explored the innovative agent-based approach and the intuitive user interface. This update focuses on the data flows that link the components and how customers benefit from the data flows.

A new base model every year

Typically, updating a base model is a big data discovery and formatting exercise, meaning models often use a base year which is years old. MIMAS allows users to run a model which is filled with the most up-to-date, accurate data. This is achieved every second, by ingesting the data required to re-form a new base model.

Why is pulling in new model data normally such a big exercise?

Firstly, it involves accessing data on a number of metrics, for example:

  • traffic flows
  • traffic composition
  • public transport routes
  • public transport performance
  • parking utilisation
  • origin-destination
  • and more
Figure 1: a sample of parking sensor locations and utilisation data

Secondly, this data is captured from the network in different ways, for example:

  • under-road sensors
  • manual surveys
  • on-vehicle tracking
  • cameras
  • floating vehicle data
  • and more
Figure 2: a sample of under-road sensor locations and traffic flow data

Finally, this data is stored across different systems (google drive, supplier platforms), in different formats (excel, csv, json, datex ii, SIRI-VM, proprietary), some is not stored for use after-the-fact, and this often requires re-mapping from one data format to another model-friendly format.

Figure 3: a sample of scheduled bus routes

MIMAS is overcoming these issues, to allow users to run on a model which is filled with the most up-to-date, accurate data possible.

This automation comes with its own challenges. What happens, for example, if the council decides to upgrade a selection of sensors or use a new supplier to collect parking data? MIMAS is developing a flexible ability to switch between these data sources, to both minimise missing data for the model and provide freedom in choosing data suppliers for data owners.

Observed data to provide confidence

When the MIMAS model is updated every year, the output is validated to ensure it is  correct and accurate. Understandably, users want to gain confidence into how the output compares to observed data near the new development being planned (e.g. there may be an important road near the development where it’s really important to get the results right). This is true of any model, but current models leave it up to the user to find such data for manual comparison with the model output.

Part of MIMAS’s self-service paradigm is to provide users with an easy way to compare the model output with observed data – within the MIMAS tool. The user is provided access to the observed data from surveys and under-road sensors, so they can compare this data to model output.

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