1. Introduction The unprecedented progress in Artificial Intelligence (AI) and robotics over the last two decades and the continuous cost reductions in technology production empowered innovation adoption in every industry and occupation. Driverless vehicles are the automotive industry’s response to autonomous technology innovation, emerging as a cultureshifting intervention destined to change the way mobility is perceived and the way cities function (Gavanas, 2019; Milakis et al., 2017; Thomopoulos & Givoni, 2015). According to the driverless paradigm, the human driver will no longer be the epicentre of driving but will be replaced by powerful safety-enhancing autopilots. Adopting driverless vehicles means that human driver errors that have been the leading root for road traffic accidents for a century now (Crayton & Meier, 2017; Waldrop, 2015) will be eliminated. Driverless technologies could also potentially, as Nikitas et al. (2020) suggests, improve accessibility, invehicle riding experience, energy savings, car-sharing and ride-sharing business models and reduce traffic congestion, environmental degradation, air pollution, noise nuisance and social exclusion for those currently unable to drive. For these reasons, Autonomous Vehicles (AVs) and Connected and Autonomous Vehicles (CAVs) are widely projected to become the cornerstone of smart urban transport systems (Nikitas et al., 2017; Papa & Ferreira, 2018) and one of the prime areas for research and development investments in urban planning (Arakawa et al., 2018; Knowles et al., 2020; Strand et al., 2014). However, AVrelated impacts constitute an uncharted territory and many gaps exist in understanding how this transition will be managed (Csiszar ´ & Foldes, ¨ 2018; Foldes ¨ et al., 2018; Foldes ¨ & Csisz´ ar, 2016). The notion of an AV, by definition, refers to vehicles that operate in the absence of any human involvement (Nikitas et al., 2019). Autonomous driving currently entails six varying levels of automation: Level 0 refers to standard vehicles without automated driving functions while Level 5 refers to self-driving cars capable of completing the full dynamic driving activity deprived of limitations (Skeete, 2018). Today, AVs are still on the path to realise their full driving potential (Katrakazas et al., 2015) but there is already fierce competition, particularly among automotive manufacturers, for fulfilling the promise of fully developed automation. CAVs are likely to be the most captivating, innovative but also disruptive development that ever happened in the field of mobility * Corresponding author. E-mail address: a.nikitas@hud.ac.uk (A. Nikitas). Contents lists available at ScienceDirect Cities journal homepage: www.elsevier.com/locate/cities https://doi.org/10.1016/j.cities.2021.103203 Received 23 August 2020; Received in revised form 8 December 2020; Accepted 18 March 2021 Cities 114 (2021) 103203 2 (Bansal et al., 2016; Nikitas et al., 2017). Today, driverless vehicles are not a fiction anymore (Bansal et al., 2016; Hancock et al., 2019). AVs, usually under close human supervision (i.e. human pilots are on board ready to take over control in emergency situations) and in segregated conditions are trialled worldwide, to test whether they can function effectively in complex scenarios (Nikitas et al., 2017). In part, the recent breakthrough in AI technology has fuelled hopes that self-driving vehicles may be seen on the roads in the nearby future. By 2050, the overall worldwide passenger economy of self-driving vehicles is predicted to reach 7 trillion (Wang et al., 2018) with governments in USA, UK, Germany, Australia and New Zealand actively supporting already their research and development (Nikitas et al., 2019). Eventually, AVs will cultivate a novel sector of mobility, having much more profound influences than the simple replacement of present-day cars. Nonetheless, foreseeing how AVs may fundamentally transform the future of cities and societies, is a strenuous and conflicting process (Gonzalez-Gonz ´ alez ´ et al., 2019). This explains the inconsistent and discordant interpretations about where AV technology is heading. Even if the potential to see AVs driving on well-defined and pre-determined pathways in a handful of years is highly likely, CAVs capable of taking individuals from their household to any place and vice versa whatever the weather and in uncertain road traffic conditions is a more distant future. A fully autonomous driving future involves a longer time span and substantial effort that goes beyond technology per se reflecting and affecting legal, moral, education and business aspects among others. Despite their immense potential for positive change, AVs could also generate immense challenges (Bergmann et al., 2018) that include according to Nikitas et al. (2020) and Liu et al. (2020): increased vulnerability to hacking, software and hardware flaws; loss of privacy and travel data exploitation; liability allocation challenges; increased car usage from more populations and unoccupied vehicles; increased traffic accident rates during the transition period when CAVs will co-exist with simpler AVs, semi-autonomous and conventional vehicles; more pollutant emissions; behavioural adaption, situational awareness and user resistance problems; and more importantly for the context of this research labour market disruption. Undertaking premature, yet inclusive investigation and appraisal about the future dangers of self-driving is imperative for responsible research and innovation (European Commission, 2014) and can have a decisive influence on individuals and societies’ agreement or disagreement with AVs wide-scale implementation (Maurer et al., 2016). At present there is very little known about the nature, magnitude and severity of the AV-related impacts on labour market (Frisoni et al., 2016). Typically, socio-technological innovations have implications for the working world. Automation, in general, changed employment through job destruction, changing working requirements and flexibility, as well as standardisation (Nathan & Ahmed, 2018). Frey and Osborne (2017) estimate that around 47% of total US employment is in the high-risk category over the next two decades because of the computerisation phenomenon including all the jobs related to the transport and logistics industry. Thus, the introduction of AVs has the potential as Pettigrew et al. (2018) suggested to completely disrupt employment as known now. This means that the general public attitudes about AVs may oscillate between enthusiasm and doubt (Kyriakidis et al., 2015); doubt regarding not only technical fixes, but also regarding the possible employment disruption that AVs could convey (Acheampong & Cugurullo, 2019). Historically, technological developments usually end up generating more jobs (Halteh et al., 2018) but in the short-term are perceived as ‘creative destructions’ and ‘force change’ (Nathan & Ahmed, 2018). Although substantial disruption is often the consequent effect, on the opposite spectrum arise new opportunities. For instance, today, because of technology’s creative destruction effect on employment, a New York investment bank employee could be easily living and working in Vancouver, instead of moving to New York (Messer, 2010). AI is omnipresent in people’s lives via internet and smartphones, and especially in developed countries most employees interact daily with computers, as well as robotic devices (Halteh et al., 2018). However, web and mobile app developers, social media designers, and other intelligence-related professions constitute a still surprisingly small segment of the total employment needs and refer to roles typically linked with high-tech specialisations. The recently established techno-economic model inflicts new patterns of work at both intellectual and physical levels, challenging the old-fashioned production norms and producing consistent mistrust (Nathan & Ahmed, 2018). It may thus be argued that automation developments might entail a significantly more pronounced effect on employment than what has ever been recorded before, increasing concerns that mass redundancies will prevail over job creation. Research also predicts that there will be a significant mismatch between today’s required employee skillset and the one needed in an AIdefined era (Snyder, 2016). Thus, studies looking into exploring these new skillsets and helping societies, industries and authorities to recalibrate their employment needs are of crucial importance. However, it is problematic to inform policy-making in the absence of concrete social preference data reflecting and affecting AV scenarios (Lu et al., 2017). This study aims to examine the public perceptions of the effects that the introduction of AVs will generate to employment in the transport and logistics industry. More specifically, the paper intends to:
(i) identify people’s perceptions of the after-effects of a full-scale AV launch on employment,
(ii) identify new opportunities and challenges that will arise and skills that will be sought after once AVs will be fully launched,
(iii) provide policy recommendations about how to ease labour market disruption for the societies, in general, and those employed by the transport sector, in particular, after the transition to an automated transport paradigm. Henceforth, the study presents: an overview of the limited AVs and employment literature, a description of the method employed, a systematic examination of the results, a discussion benchmarking our key findings against the literature that includes policy and industry recommendations, limitation acknowledgement and future research directions and a conclusion section which discusses our main contributions.
