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Comment: Behavioral insights and the path to 2035

By / 3 weeks ago / Features / No Comments

By 2035, the UK will ban the sale of new petrol, diesel and hybrid cars, opting instead to focus efforts on encouraging greater uptake of electric vehicles (EV) to meet government commitments to net-zero emissions targets. Simone Torino, head of product and business development at CKDelta, discusses the role of anonymised data in supporting a smooth transition to a sustainable transportation sector.

Simone Torino, head of product and business development at CKDelta

Simone Torino, head of product and business development at CKDelta

To meet net-zero emissions targets the UK is going to have to rapidly adopt alternatives to fossil fuel-powered vehicles. There is appetite for such a change from the wider public. A recent YouGov poll found that 57% of respondents would consider buying an EV if they were affordable. However, 81% of those polled raised concerns about the range of the vehicles and the lack of existing charging infrastructure.

With the adoption of EVs depending on a wide set of variables and a broad swathe of stakeholders, leveraging anonymised data is proving extremely valuable to the ones who are planning and supporting the localised rollout of necessary infrastructure. As part of CK Hutchison Holdings, CKDelta has access to a diverse portfolio of businesses which provides a source of anonymised data – including mobility data.

This data serves a critical purpose in that it allows for estimates and predictions of where (geographically) and when (time of day) there is likely to be spikes in electricity usage. For example, mobility data could indicate key commuter routes in towns and cities, and it can help to understand which areas are likely to experience a quicker EV uptake, with increasing requirements for enabling infrastructure.

The benefit of such predictive analytics is twofold. Firstly, it ensures that local authorities and private forecourt operators can deploy charging infrastructure in areas of high demand where they will be of most need to drivers. This would allay fears from buyers of EVs around a lack of infrastructure and reduce the so-called ‘range anxiety’ experienced by many today. This strategy could also complement the objectives of private businesses and fleets by affording them a means of attracting further custom and maximising their returns.

Secondly, it would ensure that the UK’s six licensed distribution network operators (DNOs) can better understand when and where to reinforce their networks in order to increase capacity and meet consumer demand. This readiness will be critical to the successful rollout of EV technology in the UK, as a failure to prepare for heightened demand could leave the DNOs unable to meet expectations and regulatory targets, as well as potentially increasing costs for the final customer.

EV analytics showing catchment areas around London

EV analytics showing catchment areas around London

Harnessing anonymised data also has the potential to create commercial opportunities for retailers by understanding trends and behavioural patterns such as, locations where drivers dwell for longer times for instance. A perfect example of a win-win situation which can only be identified through a deep understanding of – and access to – high quality data. With CKDelta working at the intersection of mobility, retail and behavioural modelling, our diverse data sets can provide insight into the changing behaviours of the public and model how and when this might change to better influence the decision-making of commercial and public sector bodies.

An understanding of the demographics in a given area coupled with the mobility patterns of those people will provide purveyors of EV charging infrastructure with a clearer insight into local needs and demand. For example, in the aforementioned YouGov poll, 73% of those polled aged between 18 and 24 indicated that they would purchase an EV.

With this in mind, areas where this demographic makes up a high proportion of the population – such as university towns and cities – could require EV infrastructure to be deployed at a greater rate than a coastal town populated by retirees. Leveraging anonymised demographic and mobility data can assist with predictive patterning that will play a fundamental role in the rollout of EV technology.

This is not to say that leveraging data is not without its challenges. Currently, data sources employed are fragmented as there is no one singular source of data that can be called upon. However, by leveraging data where it is available, a degree of confidence can be garnered through statistical methodologies which help to create high confidence around what the data indicates.

Investment in EV infrastructure and sustainable transportation methods played a starring role in the Chancellor’s Spring Budget. To build a cleaner, greener future, we need to better understand behaviour and model how it might change to deliver usable real-world insights.

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