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
Data Sources
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.
