New research article-The transition to autonomous cars, the redesign of cities and the future of urban sustainability

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UGAutonomous cars controlled by an artificial intelligence are increasingly being integrated in the transport portfolio of cities, with strong repercussions for the design and sustainability of the built environment. This paper sheds light on the urban transition to autonomous transport, in a threefold manner. First, we advance a theoretical framework to understand the diffusion of autonomous cars in cities, on the basis of three interconnected factors: social attitudes, technological innovation and urban politics. Second, we draw upon an in-depth survey conducted in Dublin (1,233 respondents), to provide empirical evidence of (a) the public interest in autonomous cars and the intention to use them once available, (b) the fears and concerns that individuals have regarding autonomous vehicles and (c) how people intend to employ this new form of transport. Third, we use the empirics generated via the survey as a stepping stone to discuss possible urban futures, focusing on the changes in urban design and sustainability that the transition to autonomous transport is likely to trigger. Interpreting the data through the lens of smart and neoliberal urbanism, we picture a complex urban geography characterized by shared and private autonomous vehicles, human drivers and artificial intelligences overlapping and competing for urban spaces.

New open access research paper on car-sharing

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Major cities in developing countries are increasingly becoming motorized. Thus, effective solutions to address the negative impacts that come with rising car-ownership are needed as part of an overall travel demand management strategy. In developed and emerging economies, shared-mobility in the form of car-sharing is becoming popular as potentially low-cost and environmentally sustainable alternative to car-ownership. Yet, our understanding of car-sharing adoption and diffusion factors in developing countries is limited. In this study, we fill this gap by examining car-sharing adoption intentions among young adults aged between 18 and 35 years in Ghana, Sub-Saharan Africa. Using structural equation modelling, we model car-sharing adoption intentions based on a framework that integrates individuals’ perception of the benefits of car-sharing, attitudes towards the environment and technology, trust of stewardship in car-sharing, perception of innovativeness of car-sharing, travel expectations and socio-demographic factors. We found that pro-technology and pro-environmental attitudes correlate positively with perceived benefits of car-sharing. Perceived benefits of car-sharing, in turn, has the largest predictive effect on intentions to car-share. Other factors, including individuals’ previous experience using Uber on-demand taxi services, gender, education, driver’s licensure and expectation of comfortable and fast travel options, all predict car-sharing adoption intentions. While there exists an interest in both station-based and free-floating car-sharing services, more of the would-be users favour the latter than the former. Also, majority of the potential adopters (62%) would join a car-sharing service within the first one year of its introduction. An important finding is that dissatisfaction with existing public transit services underpins car-sharing intentions, implying that relying on car-sharing alone to meet travel needs, without a holistic strategy of providing quality and affordable public transit services, could lead to unsustainable outcomes

Special Issue Call for Abstracts on “Autonomous mobility transitions: socio-spatial dimensions and the role of urban planning and policy”

Special Issue of Cities, the International Journal of Urban Policy and Planning 



Advances in Artificial Intelligence (AI) and digital technologies are set to transform many facets of society, including the way we travel and interact in cities. Today, AI-driven and Information and Communication Technology (ICT)-enabled fully autonomous vehicles are being introduced into a number of cities around the world.

As automated driving becomes pervasive in cities, profound societal and spatial impacts will be inevitable. An important socio-spatial dimension of automated driving is the likely impact that this new form of mobility will have on the structure of cities and on the configuration of streets and public spaces.

Introducing AVs into existing built-environments and transportation systems could cause major disruptions and worsen problems of unequal access to opportunities, especially if investments needed in providing public transit are diverted into building infrastructure for driverless vehicles.

Moreover, the transition to autonomous mobility will have implications for creating inclusive and age-friendly urban futures. However, to do so, innovative urban development and transportation planning strategies that could leverage autonomous vehicles to respond to the mobility needs of different groups of people, will be critical.

In the logistics and local goods delivery sector, urban planning must respond to new challenges associated with the technology, including planning for the supporting infrastructure such as road and street systems, electric vehicle charging stations and warehousing and bulk-breaking facilities.

Economic impacts, including potential job losses in the transportation sector are also expected because of automation. In addition, fundamental questions regarding the ownership, management and access to data that users of autonomous transport services would generate, need addressing.

Thus, the urban fabric, as well as different groups of people in different parts of the city may be affected differently in the transition to autonomous mobility. How can urban planning and policy respond to the wider socio-spatial implications of autonomous mobility?

Focus of the special issue and themes

This special issue seeks to bring together a collection of critical perspectives on the social and spatial implications of the diffusion of autonomous vehicles in cities. In particular, the special issue will seek to publish contributions that stimulate debates and improve our understanding of how urban planning and policy can respond to this potentially disruptive technology as it intersects with cities. The contributions can be empirical, theoretical and methodological.
Topics can include (but are not limited to):

  • autonomous mobility and urban spatial structure
  • affordability, equity and inclusivity implications of autonomous mobility
  • accessibility implications of autonomous vehicles for motorized travel
  • accessibility implications of autonomous driving for different age groups including children, young adults, people in old age, and for individuals with disabilities and the urban poor
  • implications of autonomous driving for vulnerable road users, including cyclists and pedestrians
  • implications of autonomous mobility for mass transit in cities
  • possible travel behaviour changes around driverless vehicles
  • governance of autonomous mobility transitions
  • the nexus between autonomous mobility and public health
  • privacy and security concerns around autonomous vehicles
  • the nexus among autonomous vehicles, emerging mobility concepts (e.g. shared-mobility, mobility-as-a-service) and urban sustainability
  • autonomous mobility transitions and employment in the city
  • urban planning implications of automation in freight movement in the logistics sector

Abstract submission guidelines

Interested authors are invited to submit an abstract (maximum 400 words), describing the rationale, methods, data and expected results of their papers. Please email your abstracts to The deadline for abstract submission is July 31, 2019.

Important dates

Abstract submission deadline: July 31, 2019

Decision on abstract proposal: September 13, 2019

Manuscript submission deadline (6,000 – 8,000 words): February 29, 2020

Reviewers’ Feedback: May 31, 2020

Revised paper’s submission deadline: August 30, 2020

Reviewers’ final feedback and editorial decisions: September 30, 2020

Final manuscript due: October 30, 2020

Publication with Cities: January 2021

Guest editors

Ransford A. Acheampong—University of Manchester, UK []

Federico Cugurullo—Trinity College Dublin, Republic of Ireland []

Luca Staricco— Politecnico di Torino, Italy [‎]

Elisabetta Vitale Brovarone —Politecnico di Torino, Italy [‎]


Prof Pengjun Zhao

For more information about the aims of the journal and submission guidelines please see

New research article looks at how we can determine whether people will use self-driving cars through sharing, ownership or public transit

Autonomous Vehicles (AVs) have the potential to make motorized transport safer and more sustainable, by integrating clean technologies and supporting flexible shared-mobility services. Leveraging this new form of transport to transform mobility in cities will depend fundamentally on public acceptance of AVs, and the ways in which individuals choose to use them, to meet their daily travel needs.

Empirical studies exploring public attitudes towards automated driving technologies and interest in AVs have emerged in the last few years. However, within this strand of research there is a paucity of theory-driven and behaviourally consistent methodologies to unpack the determinants of user adoption decisions with respect to AVs. In this paper, we seek to fill this gap, by advancing and testing four conceptual frameworks which could be deployed to capture the range of possible behavioural influences on individuals’ AV adoption decisions.

The frameworks integrate socio-demographic variables and relevant latent behavioural factors, including perceived benefits and perceived ease of use of AVs, public fears and anxieties regarding AVs, subjective norm, perceived behavioural control, and attitudinal factors covering the environment, technology, collaborative consumption, public transit and car ownership.

We demonstrate the utility and validity of the frameworks, by translating the latent variables into indicator items in a structured questionnaire, and administering it online to a random sample of adult individuals (n = 507). Using the survey data in confirmatory factor analyses, we specify and demonstrate scale reliability of indicator items, and convergent and discriminant validity of relationships among latent variables.

Ultimately, we advance four measurement models. These theory-grounded measurement models are intended for application in research aimed at understanding and predicting (a) AV interest and adoption intentions, and (b) user adoption decisions regarding three different AV modes: ownership, sharing and public transport.

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