The Committee on Climate Change (CCC) has called on the UK government to bring forward a ban on fossil fuel-powered vehicles from 2035 to 2032 and instead focus attention on the rollout of electric vehicle (EV) technologies and associated infrastructure. This would undoubtedly reshape the future of mobility in the UK and have serious ramifications on decision-making in the automotive sector.
With a ban now likely in the next 12 to 15 years, the industry must begin to harness enriched datasets to support the efficient transition to a ‘green’ mobility sector. Mobility data is harnessed from mobile network operators to form an understanding of population movement, routes taken and mode of transport. Combined with a variety of alternative datasets, either those in the public domain or through private partnerships, this can assist with predictive modelling of demand and supply for infrastructure.
The industry must begin to harness enriched datasets to support the efficient transition to a ‘green’ mobility sector
Before the UK can consider the mass rollout of personal EVs or the transition of company fleets to all-electric, the necessary charging infrastructure must be in place, without which range anxiety will hinder the successful uptake of green vehicles. Local authorities and private forecourt operators would do well to consider the demand in a geographic area for charging infrastructure before spending thousands of pounds on costly installations.
Fortunately, predictive analytics is able to inform the decision-making processes of private and public bodies to ensure that infrastructure is installed where it will be most utilised. Mobility data can indicate key commuter routes in towns and cities, and can help to pinpoint which areas are likely to experience a quicker EV uptake, with increasing requirements for enabling infrastructure. For example, by assessing movement data by means of transportation on the M1 between Leeds and London, key user routes can be identified to ensure that the required number of charge points are installed at service stations where they will receive most use—and provide the greatest return on investment (ROI).
Supply and demand
What is becoming increasingly evident is the demographic makeup of those who own or are likely to purchase an EV in the near future. Younger people between the ages of 18 and 24 are demonstrating the greatest willingness to invest in all-electric vehicles. As such, the installation of EV infrastructure should take precedence in university towns and cities to cater to that given demographic.
Predictive analytics is able to inform the decision-making processes of private and public bodies to ensure that infrastructure is installed where it will be most utilised
At the same time, areas with a high proportion of older residents or low motor vehicle ownership should be considered as a lesser priority for the installation of infrastructure in the run up to 2032/2035. This is not about favouring one generation over the other but rather, making data-driven decisions.
The government’s decision to phase out fossil fuel powered vehicles by 2035 (or 2032—the decision is still very much up for debate) will depend on a significant number of variables, some of which are within our power to predict and prepare for. We do not have long to get this right, which makes our next steps even clearer: embrace the art of the prediction through quality data and machine learning to support the rollout of ‘green’ technology in the UK.
The opinions expressed here are those of the author and do not necessarily reflect the positions of Automotive World Ltd.
Geoff McGrath is the Managing Director of data innovation company, CKDelta
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