First results of the study on the socio-economic impacts of automated mobility
The aim of the study is to evaluate the expected long-term impacts of road vehicle automation in different typologies of urban environments and for two different scenarios: automation of private cars and diffusion of shared self-driving vehicles. Experts have contributed to the assessment of the potential impacts of automation on the economy,transport, the environment and society through different a survey and a workshop held in La Rochelle.
The study evaluates the potential effects of automated mobility in 8 options of urban transport automation, contrasting two caricature scenarios:
- Automation of private cars. Automation becomes a widely spread technology but this does not impact the automobile market since private car remains dominant.
- Diffusion of shared self-driving vehicles. The development of automation leads to a large increase in shared vehicles. Private car is no longer the only norm.
And four different urban typologies:
- Urbans prawl
- Small-medium compact cities
- Connected cities (city networks or polycentric regions)
- Rural and/or touristic areas
The impacts are assessed in the fields of
- Old jobs (potential reduction of employment in traditional car making activities);
- New jobs (creation of new services);
- Personal trips costs;
- Public budget (reduction of fines and parking fee revenues);
- Insurance costs;
- Accessibility enabling the development of remote areas;
- Road capacity and its use;
- Journey comfort and convenience;
- Energy and emissions;
- Land saving (e.g. less need of parking space) and possible new use of public space;
- Social impacts in terms of safety,
- Personal security,
- Health and active travel (automated rides may substitute short distance walking or cycling) and
- Different perception/use of time spent travelling in automated vehicles.
A key assumption underpinning this approach is that urban transport automation challenges, opportunities and impacts will be different in low and high density city contexts, and depending on the available transport infrastructure (in particular the existence of high capacity links). However, the survey was concentrated only on the direct impact on transport patterns in each urban form, while investigating the possible indirect impact of automation on the urban forms themselves – for instance the extent to which automation while facilitating longer journeys may provide a further impulse to urban sprawl -was beyond the scope of the study.
A survey was circulated among selected experts in the field of automated transport,right before the workshop in La Rochelle. 89 participants answered to the survey. The majority of respondent evaluated automation scenarios only in the urban sprawl context. The car ownership centred scenarios collected slightly more answers than the shared self-driving vehicles.
The figure below summarises the main results concerning the expected changes of four key variables characterizing urban mobility – daily trips per capita, average journey distance, occupancy rate and car ownership – for the eight options considered (2 scenarios x 4 urban forms):
The arrows (red for scenario 1 and yellow for scenario 2) represent for each key variable both the direction (increase, decrease or stability) and the intensity (bigger arrows for > 30% change in the base variable, smaller arrows for a change between 10% and 30%) of the likely change, according to the most frequent responses to the survey (modal values).
In a nutshell, the most frequent answers have been more conservative than some impact assumptions presented in the online questionnaire, as according to the majority of respondents, urban transport automation will cause the key variables to change within the range 10%-30% at most – or to stay the same -not changing radically (more than 30%) in one direction or the other. This is because – in the opinion of many - autonomous vehicles are only one of many factors that will affect transport demands and costs in the next few decades,and not necessarily the most important. More in detail, the key insights from the survey are as follows:
- Daily trips per capita are expected to increase in the urban sprawl and rural areas settings, as the self-driving car availability will raise the flexibility and opportunity to combine daily travel schedules for different members of the household. In more compact forms – city network and small compact city – daily trips are expected to increase only in the shared self-driving vehicles scenario, thanks to the new more capillary services offered. The impact pathway presented in the survey assumes that the cars are more often available because of their capability of self-driving, and this alone will induce more daily trips per capita (increasing more than 30%).Most of the respondents to the survey were more prudent, guessing for a more moderate increase, due to the fact that car availability is not the only factor affecting car use, especially in potentially congested urban contexts or wherea good high capacity public transport is available.
- The average journey distance is expected to increase in the private automated scenarios for all urban forms, except in the small compact city, where short distance trips are prevailing and self-driving will not change substantially the range of accessibility choices. On the contrary, the average journey distance is not expected to increase in all shared self-driving scenarios, except in the city network, where the offer of coordinated car sharing and ride sharing options is likely to increase the longer trips between the different cities of the network. The impact pathway presented in the survey assumes that the car use for longer trips is encouraged because the trips become more comfortable and the passengers are free to choose what to do while the car is driving itself. Average distance may increase between 10 and 30% as a result. Most of the respondents to the survey agreed on this assumption.
- The occupancy rate is expected to decrease in the urban sprawl context, as an effect of the empty trips to relocate the self-driving cars to the next users – i.e. another member of the household in the private automated scenario or another user in the car-fleet scenario. This effect is not considered significant in other urban contexts(small compact cities, rural/tourist areas), with the exception of the car-fleet scenario in the city network, where fleet based car sharing and ridesharing services are assumed to optimize the journeys and bring an increased occupancy rate (between 10% and 30% more). The impact pathway presented in the survey assumes that empty trips will increase substantially (causing an average occupancy rate increase of 30% or more) in the private automated mobility scenario, to allow different members of the household to use the same car during different hours of the day. The same effect will not be produced by automation in the car fleet scenarios, because fleet owners will be motivated to minimize empty running, e.g. through dynamic pricing. Most of the respondents to the survey consider the assumption for the private automated mobility scenario too pessimistic. Occupancy rates – some respondent claimed –are already low especially in the urban sprawl context (around 1.3), it is difficult to reduce them further. In addition, the operating costs of"dead-heading” empty private vehicles will become something households examine,pushing for a more efficient use of the car.
- According to the majority of respondents, car ownership will not be substantially affected in the private automation scenario – whatever the urban form. On the contrary, it is obviously likely to decrease in the shared self-driving vehicles scenarios, but the latter not in the rural area context, where the car will remain a key asset to hold (with more opportunities however for ride sharing or peer-to-peer sharing). However,some respondents to the survey highlight that car ownership could decrease substantially also in the private automated mobility scenarios, because self-driving cars may serve the mobility need of more than one family member in the same day, and the ownership of second or third cars could drop for this reason. If the autonomous vehicles are more expensive than the conventional ones, new vehicles purchase will be also limited, with a detrimental effect on car ownership.
Finally, as it concerns the expected changes of modal share, between private car use, shared transport, public transport and walking &cycling, the results of the survey are summarized for the two scenarios in the figure below:
Unsurprisingly,in the private automated scenario, the private car use is expected to increase for all urban contexts, as a consequence of the greater comfort of using and travelling with a self-driving car. The only exception is in the rural area context, where the majority of respondents think private car use will remain the same. A reduction of the public transport share will compensate the increase of the private car use, while most of the respondents think that walking and cycling shares will remain stable, as automated transport should not attract those who enjoy walking or cycling.
Shared mobility will be higher if cars become available to younger people who currently travel by public transport, and the acquisition of private – and expensive – automated vehicles will probably encourage their owners to propose more ride sharing to others to amortize the purchase costs (look to the current success of services like blablacar). Some peer-to-peer car sharing will be also encouraged – although less than ride sharing - as connected and automated features of the new cars will reassure owners and let them share their cars more easily, reducing the risks of accidents, thefts, etc., and ensuring that the cars come back to the owners when needed. Finally, some respondents questioned the expected reduction of the public transport share. This will be influenced by what will happen with the costs of the different options for the user: self-driving, shared transport, public transport. Insofar as the prices of automated vehicles will be higher, this will reduce private car usage by raising public transport and shared mobility. In addition, if the circulation of self-driving private vehicles in the urban areas will be more easily controlled and managed, this may have a positive effect also on the reliability of public transport in the same areas, increasing its use. In the shared self-driving vehicles scenario public transport might see even a rise of high capacity arterials (e.g. metro rides) since publicly run and maintained automated vehicles might serve as feeders thus offering for the first time – especially in sprawled areas – a competitive public transport option. However, one comment"out of the chore” highlighted that shared mobility services would not hold the same characteristics (e.g. response time) in central/high demand and in peripheral/low demand areas, and the same applies to conventional public transport services. Although it is true indeed that shared vehicles could offer a solution to the last mile problem, this would not dramatically change the level of service between central and peripheral zones, and the households living in peripheral locations might choose to still own and use a private vehicle.Finally, a potential positive side-effect on walking and cycling has also been mentioned,as the new free space due to less need of parking space for the self-driving cars (which are expected to circulate more continuously during their lifetime)may shift behaviours, e.g. with reconversion of parking lot space to more attractive pedestrian zones. This means that more people might prefer to walk due to enhanced safety, walking space and less pollution.