Economic challenges: The obvious economic impact creates one of the biggest data challenges facing the transport planning industry as we move through the COVID19 pandemic. Other than for monitoring disruption itself, uncertainty on how and when the economy will recover, thus how transport is impacted is a key driver in ruling out fresh data collection at this current time of writing.
Furthermore, uncertainty of how business and commerce changes will impact transport demand is having a significant impact on changes in shopping habits and the decline of the high street, leading to a significant increase in goods movements and commercial vehicles.
The uncertainty of how working practices and travel patterns will evolve and stabilise also plays its part. With more people working from home, something that certainly looks like it will be a feature of modern day working life moving forward, and the genuine prospect of a four-day working week, such changes will have a huge impact on travel demand, travel patterns and contribute to the “new normal” way of thinking. Of course we don’t really know when all of this will happen.
The threat of a continuing pandemic surely has more twists and turns to come. Will there be a second wave? When will this happen? Will there be local lockdowns? A second national lockdown? We do not therefore know when working practices and travel patterns will evolve and stablilise, so what transport data should or can we use? And when can we use it?
It’s all certainly very uncertain.
Social and behavioural challenges: We know that there have been a lot of changes to the way people are travelling. The Government messaging on public transport, along with the ‘fear factor’, and the uptake in active travel modes (mainly walking and cycling), look to have been embedded for long enough that new patterns are formed, and to some degree, may be possibly here to stay?
Recent data from July from The Transport Technology Forum (TTF), shows a general increase in cycling, albeit with some fluctuations on weekdays vs weekends, and spells of bad weather.
Data Discoverability is a key barrier to progressing problem identification, monitoring change, evaluating options, monitoring effectiveness and communicating within the industry and to wider audiences
The capture of accurate active travel activity however, presents a challenges in itself. More technically advanced equipment is required, and there are less readily available sources of data for active travel modes.
Active travel is typically more variable and so there are also challenges in needing to collect data over extended periods to account for day to day variability.
On vehicle traffic, a recent report from technology company Vivacity Labs, suggest a flattening of the curve of total vehicular traffic at 80% of the pre-COVID level. Data was taken from their network of AI sensors from a range of UK cities.
Again, a lot of the current data we have suggests a high level of uncertainty, around behavioural travel factors and an ever evolving landscape where collecting data would be illogical applying normal criteria.
Epidemiological/legislative challenges: The epidemiology and responsive legislation also presents a lot of problems with collecting data. Spikes and different rates of change in the transmission of the virus could lead to local lockdowns such as the recent lockdown imposed in Leicester, which ultimately leads to variation in travel demand across the UK and across different regions.
'Data Discoverability' is a phrase coined to cover a spectrum of issues and questions raised when considering looking for data to use in Transport Planning, including:
What data exists: content and geographical coverage
Where is the data held and who owns it? How do we access it?
What format is the data in?
Does the data comply to any standards and then is it usable? How accurate is the data?
Is there any associated meta-data?
Can the data be previewed, searched or browsed?
Is the data continuous or ad-hoc?
Is it in real time?
Is there a cost to access and use it?
Is there a quality feedback mechanism?
Paul Jackson of Tracsis states: 'Data Discoverability is a key barrier to progressing problem identification, monitoring change, evaluating options, monitoring effectiveness and communicating within the industry and to wider audiences.'
Data is available from a large network of permanent hardware, for example:
Inductive loops or radar
Bluetooth and wifi equipment
permanent ANPR/CCTV cameras
WEBTRIS and other national sources
It is also possible to mine existing dynamic or crowd sourced data including;
GPS e.g. Inrix, Tom Tom, HERE, teletrac etc.
Mobile network data (MND) from the main UK networks: Vodafone, Telefonica and EE
Contactless payment data from Oyster, T
Telematics, for example black boxes and other devices
Historic data, including ad-hoc traffic count data, MND, ticketing data, census data, National Travel Survey (NTS) and the DfT National Rotating Traffic Counts (NRTC)
So whilst there is a large degree of uncertainty, there are still a lot of potential data sources, however, in the discoverability of this data lies the problem.
The lack of access to data and the complex picture of where it is stored, means that in general there is a lack of usability of the data that exists. Nick Mather of Tracsis states: 'During the COVID19 period, whilst some data collection companies have pulled together data portals or interactive maps, the process of identifying historic data is still largely a laborious process. It still sits across many platforms, with a distant end client, or simply not able to be located. This has presented somewhat of a headache for Transport Planners and users of detailed data.'
The transport data landscape in England remains fragmented, with large amounts of data being collected and stored in silos by both local authorities and the private sector. Whilst the benefits of publishing open data have been demonstrated by Transport for London – where the release of open data has been shown to be generating annual economic benefits and savings of up to £130m a year – this success has not yet been replicated on a wider basis.
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