Autonomous Vehicle Impacts on Travel Behavior

The emergence of autonomous vehicles (AVs) promises to transform travel behaviour by altering perceptions of in-vehicle time, reshaping mode choice and influencing land-use patterns. By relieving occupants of driving tasks, AVs reduce the disutility of travel and lower the value travellers place on time spent in transit. This shift can encourage longer trips, increase vehicle miles travelled and redistribute demand away from traditional public transport and active modes. At the same time, shared AV services have the potential to mitigate some negative impacts by pooling trips, reducing individual vehicle ownership and optimising fleet utilisation. Changes in trip length, frequency and modal share are likely to interact with housing decisions, potentially promoting urban sprawl or, conversely, enabling more compact development if road capacity is managed effectively. Onboard activities such as work, leisure and rest will become central to traveller utility, further decoupling travel from conventional notions of productivity loss. Understanding these dynamics is crucial for policymakers seeking to harness AV benefits—improved safety, accessibility for mobility-restricted populations and enhanced productivity—while containing second-order effects such as congestion, energy use and inequitable land-use change. Integrating AVs into transport models and behavioural studies remains an active frontier, requiring interdisciplinary approaches that span engineering, urban planning, economics and social science.

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Autonomous Vehicle Impacts on Travel Behavior publication trend

The graph below shows the total number of articles in autonomous vehicle impacts on travel behavior across all publications each year (not limited to Nature Index journals).

Technical terms

Value of travel time (VOTT): The perceived cost or disutility that travellers assign to time spent in transit, used to model mode choice and demand.

Modal share: The percentage distribution of trips among different transport modes within a specific area or population.

Shared autonomous vehicles (SAVs): Driverless vehicles offered on an on-demand basis, either as pooled services or exclusive hires, without requiring private ownership.

Mixed logit model: A statistical framework for discrete choice analysis that captures variation in individual preferences by allowing parameters to vary randomly across respondents.

References

1. Relax on the way to work or work on the way to relax? Influences of vehicle interior on travel time perceptions in autonomous vehicles. Transportation Research Part A Policy and Practice (2024).
2. Impacts of automated vehicles on travel behaviour and land use: an international review of modelling studies. Transport Reviews (2018).
3. Investigating the decision to travel more in a partially automated electric vehicle. Transportation Research Part D Transport and Environment (2021).