Travel demand is ‘derived’, being driven by employment, shopping, education etc.. Traffic surveys always need to be taken at ‘representative’ times to be valid. They’ll typically be done midweek during school term-time, avoiding holiday periods and extreme weather.
Covid-19 restrictions have removed these drivers of demand, and travel has reduced by more than 60%. The use of buses is 10% of what it was, and tube and rail journeys have reduced by 97%. Hardly ‘representative’! So, what does this mean where forecasting traffic is essential?
There are a range of surveys that are typically needed, such as:
- Automatic traffic counts, augmented by manual counts to identify movements at junctions or flows of different vehicles (cars, HGVs, buses etc.). This now is more commonly being done using CCTV and Automatic Number Plate recognition (ANPR)) technologies.
- Ticketing and revenue information from operators, typically via use of smartcards.
- Household interviews or travel diaries for trip purpose and passenger categorisation (e.g. residents/non-residents) and stated preferences. Phone/web-based interviews are already often used for this. Intercept surveys at cordons use roadside interviews of drivers and passengers, as do on-board public transport passenger surveys. Non-resident interview surveys at hotels, airports, stations etc. are also sometimes used.
- Travel time surveys can be done using GPS data, ANPR, Bluetooth sensors/mobile phone data detected from vehicles or using data from mapping/route guidance data (e.g. Google Maps, TomTom etc.). Sourcing data using drones and video technologies is also increasingly common.
Providing continuity on infrastructure and development projects currently, and collecting any data is challenging. Remote sourcing of data has become increasingly popular because it is more efficient and cheaper. It now also has the merit of allowing compliance with Government guidance on business operations and maintaining social distancing.
However, any data collected currently is useless as it cannot be representative. In urban areas, particularly where congestion is prevalent, it may be appropriate to use historic data, as annual growth rates and changes can be minimal. Data collected within the last two years could be considered valid. This often is already the case in London, and data not ‘box fresh’ is commonly used.
For forecasting, the problem may be more intractable. Established mathematic relationships for predicting the future may not hold in a post-virus world. For example:
- New forms of travel behaviour forced upon us may become permanent, with an emphasis on staying at home or staying local. The willingness to use public transport, even in congested areas, may also take a hit.
- The experience of dealing with Covid-19 may bring political imperatives prioritising environment and sustainability.
- Economic bailouts may provide an opportunity to encourage lower-carbon activities, industries and business models.
- The crash in demand will mean energy and carbon prices will remain low for some time, potentially undermining assumed values used in economic appraisal.
Overall, a combination of faster uptake of technologies (as we have seen for remote working generally) and a systematic analytical approach to sourcing and using data should help avoid a major hiatus on development and infrastructure projects. Hopefully, the political will and funding solutions will also be in place to help achieve this.
Please note: the views expressed in this blog are those of the individual contributing member, and are not necessarily representative of the views of IEMA or any professional institutions with which IEMA is associated
To find more advice on key policy and practice issues visit our 'Adapting to COVID-19' resource hub, where we provide resources for professionals with topics of Impact Assessment Environmental Management & Environmental Auditing, Corporate Sustainability & Climate change & energy.
Posted on 28th April 2020
Written by Chris Ferrary
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