New Data Sources and the Future of Transport Data Collection
The transport data collection industry is changing. The needs of modellers and planners have evolved and technology advances have accelerated rapidly, changing the way we collect, store and use data.
There is a sense of a new direction for modellers and planners in using larger, richer data sets in a more efficient way, increasing the usefulness of data and addressing the notion of DRIP (Data rich, information poor)*, which is the focus on being able to handle lots of data sources at the same time, look at the data as a whole across potential multiple users that may have different requirements, and ultimately extract more valuable insight from those data sets. Essentially, users of these data sets are more able to tease out more useful data and can tell a better story using the data.
* The Atkins Report, Vik Bhide- Smart Mobility Manager, City of Tampa
Over the last few years, examples of advances in technology for data collection include:
- Mobile Phone Network Data: this has now become a mainstream data source and has largely replaced the need for large-scale roadside interview census surveys (although there is still a need for a small number per project to validate the MND-derived origin-destination movements).
- Vehicle GPS data harvested from fleet trackers and live SatNav devices (in-built and free-standing units) has reduced the need to undertake vehicle route journey time surveys
- Public Transport Ticketing systems such as Oyster have reduced the need for public transport user interview surveys.
- Database systems: Have gradually been adopted by public sector bodies to store and re-use survey data, reducing the unnecessary collection of fresh data where some exists already.
- Implementation of permanent data capture infrastructure: Highways England and other highway authorities have introduced Smart motorways, permanent ANPR cameras, radar sensors and average speed cameras reducing the need for data collection on some parts of the road network.
There is very strong research and development evidence (as well as real world examples), that further advances in technology and the accessibility of public or big data sources will continue to change the landscape for traditional traffic surveys and data collection. Such emerging and known technologies include, but are not limited to:
- Connected Autonomous Vehicles/Connected & Intelligent Infrastructure: there will be a mass of telematics data flowing between vehicles, other vehicles and the infrastructure controlling traffic and movement as these technologies continue to be developed and integrated into the vehicle fleet.
- Increasing granularity and accessibility of big data sources including from: 5G+ mobile network data (MND) which will improve granularity of these datasets, public transport ticketing systems, public transport automated patronage counting systems (APC) expanding into buses and trams, Mobility As A Service (MAAS) platforms generating end-to-end journey data.
- AI sensor networks linked to traffic control and information systems – primarily utilised to create smarter cities but generating a side-product of data.
The gradual shift
Whilst there will always be a need to undertake ad-hoc and temporary installation of traffic counting equipment, as highlighted above the shift will be towards accessing existing datasets that require no new physical infrastructure, yet can provide greater insight for planners and modellers. By the very nature of this shift in data sources, end users will be able to benefit much more from the data itself, in terms of harvesting, aggregating and visualising this data, and ultimately interpreting it and telling the story.
Real time data
Again, whilst there is still value in an ad-hoc data set that gives an insight to a very limited snap-shot in time, the requirement for real time data is ever increasing. Real time data allows modellers and planners to be much more agile in their approach to each task. The progression from long time interval data to regularly updated data means that reaction can be quicker and based on a cleaner and more thorough data set and change can be implemented in good time, mitigating any negative impacts that may have occurred with a longer time lag between periodic data sources.
Going are the individual reports supplied in .csv format offering very little insight and being unable to be easily cross-referenced with existing data. In future data will be supplied through innovative online dashboards, which develop over time and enable the data user to manipulate and visualise the data with the click of a button. Not only that, the possibility of creating platforms that can accept APIs from any piece of existing equipment or data source and present the data in a single, accessible place for an entire transport team, highlight the value of such reporting tools. This way of using data is far more secure than a ‘traditional’ method, which suits the current regulatory environment.
Given the recent update in data protection laws, clearly data governance and data privacy are big issues, particularly if data is sensitive or personal. Traditional data collection methods such as data capture using temporary camera systems and post fieldwork analysis create many potential issues regarding personal and sensitive data. This is due to the collection of a mass of high quality footage where the features of individuals could be identified, therefore the appropriate transfer, storage, retention and the systematic deletion processes are required, which increases risk. Newer and emerging methods lend themselves to a more robust and secure process.
Integrated AI sensors collect vehicular and active travel modes, transmitting the count data only, therefore being inherently more compliant and require no intermediate physical transferring of raw information. Data can be delivered in a more secured way, such as through a cloud-based system in a compliant IT environment, and the accessing of data can be controlled using firewalls, passwords and partitions, with different users authorised for different levels of access.
We are most certainly at a point in time where these new sources of data, data reporting capability and thus insights from the data are beginning to provide tangible benefits for Transport Professionals. From saving time and cost and generating increased efficiencies, to gathering and reporting on a richer data set, it is an exciting time for the industry. Anywhere that technology can provide enhanced insight into the data we collect should be embraced and the reliance on temporary survey data reduced.
Author: Nick Mather
Nick is the Business Development Director for Tracsis. He has a range of knowledge and 10 years’ experience across the industry from survey design and Project Management, to data analysis and insight for all types of transport surveys and transport data collection. Nick has an MSc in Transport and the Environment and is a CHIT Member.
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