Big Data is transforming areas of our transport industry, from delivering more efficient bus timetables through the modelling of bus routes to using asset data to deliver intelligent maintenance. There are clearly some outstanding areas of innovation but too often data forms are inconsistent, meaning insights are isolated to single projects and inaccessible to others. The challenge therefore is consolidating all this data in a consistent and meaningful way, and making it available for customers to access it.
‘Data hubs’ can address this challenge and as we have seen on Midlands Future Mobility (MFM), provide Transport Authorities with a single verifiable source of data, easily shareable and commoditised, which can ultimately deliver connected transport networks of the future.
What is Big Data?
In Big data: the lifeblood of future mobility, I unpicked the various sources of big data for transport, such as people movement, ITS transport and weather, and how when combined and analysed correctly, they enable us to create accurate models of effective transport networks. This data will also enable us to ‘build back better’ following Covid-19, such as the opportunity to deploy data driven clean air zones enabling cleaner, greener communities.
The transport industry currently lacks the tools and capabilities to make sense of this big data and this is something the development of transport data hubs can overcome. A transport data hub brings together data from multiple data sources, analyses it and visualises it in ways to help authorities make better sense of it. Data Hubs enable network operators to consolidate multiple static (Road geometry/layout) and dynamic (traffic speed, flow, congestion, air quality) data sources to give a single view of the network whilst also providing this data to customers planning tests on the road. The introduction of data hubs is a game changer for the transport industry.
The MFM Data Hub
The Data Hub acts as the location for running all test activities on the MFM network, a real-world testbed for CAM technology in the Midlands. Users can plan the test they want to run, execute the test, download the relevant Intelligent Transportation System (ITS) and GIS data to understand how their vehicle responded and then upload the results, enabling them to share their findings with others within their organisation and beyond.
Furthermore, the ability of the Data Hub to ingest a range of data sources enables data brokerage and sharing. This is critical to the future of the operation of a data driven transport network as it will enable both operators and users to develop new services based on shared ITS and GIS data, which in turn will commoditise it, generating new sources of income for transport authorities.
Challenges and opportunities
The Data Hub enables transport authorities to address number of traditional problems with transport data.
- No single version of the truth: Transport authorities are awash with big data, but the problem is that this data is not consolidated to enable effective analysis. The Data Hub overcomes this problem through acting as a central repository for transport data in a standardised format which can be added to, manipulated and visualised. Data can also be viewed in different ways through the linkage of static and dynamic data together. Transport authorities will be increasingly expected to understand the data they possess, identify issues with the data and share it easily and this is enabled by the Data Hub.
- Mixed capabilities around mapping data: Data without context is useless. The vast majority of data sets transport authorities possess are geographic and yet the ability for this data to be easily consumed differs from authority to authority. The MFM Data Hub is built around a simple map format, enabling transport authorities to visualise static and dynamic data. For example, a number of map-based scenes have a virtual 3D digital twins. Users can request this in a standard format. enabling them to model how their technology will respond in a virtual environment before real world testing.
- No easy way to simulate and model the data: Transport authorities also struggle to take their data and model it through running simulations. This capability is essential when designing major road improvements. Ultimately, this is due to the disparate way in which transport data is held. The Data Hub overcomes this by consolidating and standardising the data, enabling modellers to access several data sources in one place. This also allows transport authorities to run scenarios focused on understanding how autonomous vehicles will operate in different environments.
- No easy way to share data: The ability to share data is key to delivering improvements as it enables analysts both within and outside the transport sector to make sense of the data and make recommended improvements. Most transport authorities lack the platform or capabilities to open up their data, meaning they cannot deliver improvements. This is something which the Data Hub overcomes. The Data Hub is primarily structured to act as a sharing platform for Local Authority data.
- No current way to commoditise data: The current challenge for transport authorities is that they lack the platform and tools to commoditise the data they possess. This is becoming increasingly important as authorities look for new and inventive ways to maintain infrastructure investment in the current climate of budgetary constraints. The Data Hub overcomes this challenge by enabling transport authorities to establish charging mechanisms for each data set they share. This can be based on a simple ‘pay to access’ model or a more complex ‘pay per MB downloaded’ model.
Conclusion
Data is key to the future of transport, as is the need to combine, visualise and present that data effectively. Data Hubs are central to unlocking this future as they are flexible in operating as both test bed data systems and as transport data network management tools. Their adoption is essential in accelerating the growth of connected data powered transport networks.