To investigate how people from different socio-demographic groups perform on the preference to own an electric vehicle in the future and attitudes to EV related values, based on more matured experience or knowledge of EVs, by considering the type of fuel their current cars use, second-hand EV market situation as well as confidence of EV battery technology.

• There are four steps of data analysis in this study:

  • Data cleaning of National Travel Attitudes Survey (NTAS) dataset by removing blank space and indicating missing values;
  • Distribution plots of each attitudinal questions to illustrate potential groups;
  • Principal Component Analysis (PCA) is utilized to identifying directions (principal components) that capture the maximum variance in the dataset;
  • Testing the reliability of PCA components and evaluate whether it is necessary to use normalization or not.

• There are four principal components generated from the PCA model:

  • Component 1 – Resistances to EV transit: the concerns when people decide to choose electric vehicles as their main form of transportation;
  • Component 2 – Awareness of EV related policy, benefit & EV owning preference: government incentives of purchasing electric vehicles and policy to increasing the availability of ultra-rapid charge points in motorway service area by 2030, as well as the impacts of agreements to the value of EV on choice preference.
  • Component 3 – Pre-owned EV & Maintenance Concerns: represents individuals who concerned with the battery technology of second-hand electric vehicles as well as the further maintenance cost.
  • Component 4 – EV Purchase Price & Affordability: including respondents with attitudes towards affordability of both new and second-hand EV purchase price and the choice diversity in pre-owned EV market.

• Understanding how different groups of consumers evaluate EVs; whether through economic, environmental or lifestyle preferences, is crucial for designing equitable and effective policies.
• Capturing this kind of diversity through attitudinal analysis can offer insights into segment specific barriers and enablers, guiding more efficient allocation of incentives and infrastructure investments.

The study analyzed UK travel attitude survey data to understand EV adoption. It identified four key factors shaping views: resistance to EV transition, policy awareness and incentives, concerns about second-hand EVs and maintenance, and affordability/purchase price. These insights can guide equitable policy and investment decisions.

In progress

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