Fields of spatial research on urban areas in the context of organisational solutions to new forms of transportation

Bartosz Czarnecki 1
  • 1 Bialystok University of Technology, Faculty of Architecture, Bialystok, Poland
Bartosz Czarnecki


The paper discusses the spatial consequences of the widespread use of self-driving cars and the resulting changes in the structure of urban areas. Analysing present knowledge on the technology, functionality and future forms of organisation of mobility with this type of means of transportation, conclusions are presented concerning the expected changes in the organisation of space in urban areas. The main achievement of the investigation is an outline of the fields of future research on the spatial consequences of a transportation system with a large share of self-driving cars.


The contemporary model of transportation systems in urban areas is generally based on two main pillars: systems of collective means of transportation for massive, public use and secondly: individually owned private cars. There are additional means of transportation such as taxi cabs and other ones also referred to as alternative, but these are of much lower significance. Each of the two main kinds of means of transportation has its well known pros and cons. Public transportation means dominated from about the mid-19th century until about the middle of the following century. In that period towns became much larger than previously, but still kept within small or medium dimensions. The main limitation was an acceptable walking distance from stops on the network of the public means of transportation. This limitation disappeared during the process of the private car becoming widely accessible as means of individual transportation. It had a large and important influence on the way development was carried out and gave a new dimension to cities, which also often became urbanised areas rather than classic towns. The process could be very clearly seen from the beginning of the post-war “American Dream”, through the development of contemporary, so-called, highly developed countries, until today with the intensive motorisation process occurring in developing countries. In all of these places the large territorial increase of urban areas and appearance of suburbanisation could be seen. The average degree of individual mobility has greatly increased leading to the opening of new possibilities for almost all of the inhabitants. On the other hand, it has brought accidents, air pollution, spatial disorder, costs of the overdevelopment of transportation and technical infrastructure. Now, thanks to further technological development (Internet, AI – Artificial Intelligence), we stand at a turning point in the new model of organisation of transportation systems. It will combine new technology (self-driving vehicles) with a new form of organisation of transportation systems (sharing not owning individual means of transportation). The first tests were initiated in the early 2000’s (Urmson et al. 2008) and the Google company has undertaken intensive studies and tests on these matters since ca. 2010. Nowadays almost all the important car manufacturers, such as Tesla, Volvo, Mercedes, BMW, Ford and many others, are developing such studies and tests. The start-up company nuTonomy lent out a dozen self-driving cars (with driver assistance) in 2016 for a limited circle of commercial users to use on the streets of Singapore and it is soon expected to carry out tests in Boston and Santa Monica (nuTonomy website). The Tesla company has given their customers limited autonomic functions on the cars it produces. The State of Nevada, USA allowed the commercial use of driverless trucks in 2015 (Autonomiczne ciężarówki juz jeżdżą…. 2015; Smith 2012a). If this new model spreads widely, it will bring many changes to our life. It will change the dimensions of space consumption in cities. For urban planners, it is very interesting to consider how these changes will influence the:

  1. utilisation of space in cities (urban areas);
  2. changing structure of space;
  3. ways and dynamics of spatial development of urban areas;
  4. dominant model(s) of future housing.

This article, based on data already known about new transportation technologies and the directions of their development, tries to approach answers to the above questions and describe the fields of future research need based on the spatial impacts of the widespread use of self-driving cars. The main objective is to describe important fields of future urban research as a consequence of change in the dominant model of transportation systems. Additional objectives are to indicate the main groups of features which will determine future changes in urban structures after self-driving cars become widespread and to describe the influence of such vehicles on different aspects of urban life.

The methods used, were the inference method, based on the present state of knowledge: reports, technical reports and other literature analysis.


For a few years now autonomous vehicles, especially self-driving cars, have no longer been fantasy, but are already reality under test. The organisation of the transport system based on them is also a topic of analysis and discussion in scientific journals and at international congresses. Probably the most influential are the annual International Transport Forum summits. The problems of a future with self-driving cars were apparent, in particular in the 2015 and 2016 events. In the 2017 summit the issue of formal regulations for autonomous cars was discussed (ITF Summit 2017 website). In Poland, on September 19th, 2017, the Motor Transport Institute held an international conference: Autonomous, the future of Road Transportation (International conference…).

The question of when self-driving cars will available for everyone isn’t a matter for this paper. The main car companies and scientific institutions are involved in the process of developing that way of thinking about the future of transportation. S. Currie (2017) and B. Lutz (Bellon 2017) think that the deciding factor promoting the widespread use of self-driving vehicles will be safety. When the public sees that self-driving cars are much safer than traditional ones (ca. 90% of accidents are caused by the human factor) they will take over. It would appear that this will occur when the share of autonomous vehicles reaches a proportion of between 20 and 30% of all vehicles. So, most work on new ideas for transportation is based on the elimination of the human factor. Replacement of a human driver by a computer based system allows one to replace a human decision chain with just-in-time reactions (Yilmazcoban 2016), however, the self-driving cars so far tested show higher rates of accidents than human-driven cars - 5.5 vs. 1.2 accidents per million kilometres (Schoettle & Sivak 2015). It is still impossible to estimate the real influence of self-driving vehicles on road safety (Obinata, Suzuki & Wada 2012), especially in relation to the strange fatal accident of the Uber Company self-driving vehicle in March 2018 (Wakabayashi 2018).

The Corporate Partnership Board report for the OECD: Urban Mobility System Upgrade (CPB Report 2015) tries to define the scale of change of some parameters describing traffic and land-use changes in towns dominated by autonomous vehicles. The report examines such parameters as: the number of cars, volume of car travel, impacts on congestion depending on the system configuration, parking needs, ride sharing vs. car sharing. The results are, of course, not certain and obvious: the number of cars should decrease significantly: it could be as many as 9 out of each 10 cars could be removed. Under the same conditions the volume of car travel should increase between 6 and 89% depending on whether the system is configured with or without high capacity public transport. The new model of the transportation system will (according to the report) completely remove on-street parking and up to 80% of off street parking (CPB Report 2015: 5).

These results are reached on the basis of certain conditions, like the car downtime in Lisbon being assessed at a level of 95% (CPB Report 2015: 26) and with two different transport system configurations: with and without the operation of high capacity, mass public transport.

Predicting the exact spatial consequences of the widespread use of this new model of transportation should be possible if four data packages are available:

  1. technological solutions and potential;
  2. a model for the organisation of autonomous transportation;
  3. the consequences of both the above for functionality and lifestyle, as well as other social aspects;
  4. the rate of supply of such vehicles in the future.

We generally know about the first of the above factors: we know the expectations of this technology and its capabilities, what the prototypes actually tested demonstrate and what is presented below. The next two we can foresee on the basis of just operating indirect solutions like different forms of car sharing or alternative taxi services like Uber or Bla-Bla Car. The fourth one is of course unknown today, the expectations are also discussed below.

Technological background: what do we know so far

The main elements of the technology of self-driving cars have recently advanced, but in practice are still under development and testing. There are different systems of classification of the autonomy of cars: the American (Newman 2018), the Far East one (for example), and others (Martin 2016). There currently is a discussion on the formal limitations and ways of introducing them (ITF Summit 2017 website).

The investigation on the future values of urban planning parameters in an era of self-driving cars can be based on the knowledge of their expected functionality and the operating features of such vehicles. These parameters are suitable both to present existing technological possibilities and for the organisational models just developed in the transportation economy and some other areas (Fagnant & Kockelman 2014) (Fig. 1). We can list those expected and/or just really existing in the cars being tested or as organisational models of the technologies, functional and organisational features of self-driving car-based transportation systems. It will allow one to describe spheres and directions of change in creating urban space under the rules of that future era, whenever it comes.

Figure 1
Figure 1

Vehicle of the car-sharing system in Kaunas, Lithuania

Source: photo by B. Czarnecki (April 2017)

Citation: Urban Development Issues 58, 1; 10.2478/udi-2018-0023

These features are:

  1. technological aspects:
  2. vehicle self-reliance in traffic decision making;
  3. an advanced, intuitive communication interface, easy to use for everyone;
  4. distant just-in-time interchange of information with the operating centre, other vehicles on the road, other elements of the road (Shahzad 2016); functional aspects:
  5. possibility of remote call-up of vehicles: the mobile phone as a distant access tool and communication centre;
  6. no need for any intermediary person (driver, dispatcher);
  7. no especial ability, qualification or skills required by the vehicle user – decisions only about the final point of the trip, the possibility of choosing the route, the option of doing an emergency stop; organisational aspects:
  8. sharing of private vehicles or temporary use under public/corporate ownership;
  9. a subscription as a way of maintaining permanent formal access.

Changes in the organisation of individual means of transportation

The appearance of the above conditions will bring in many profound changes in the way vehicles are used as individual means of transportation. First of all they should allow for really mass temporary use of individual vehicles thanks to much larger economic accessibility (under the rules of an access subscription, no labour cost of the driver) and thanks to the lack of need of any special skills or formal qualifications of users. The second thing is, that it will allow people to only use vehicles during a period when there is a real and specific need. It will be similar to the contemporary use of taxi cabs, but without the participation of a human driver and losing time for payment after the trip. Thanks to that, use of a vehicle would be cheaper and much more flexible: vehicles could be in permanent operation for almost all of the time during the day and week. They’ll be permanently circulating. Periods of time outside of rush hours, including night-time, would be used for technical services and cleaning.

Consequences for functionality

The idea of the changes just described leads us from individually owned cars for individual use to individual shared vehicles for short temporary use. This changes the relationship between an on-road period and a parked period, as shown in Table 1.

Table 1

Difference of proportion between on-road and parked time for traditional and self-driving cars

Source: Dallegro 2014

Traditional carsSelf-driving cars
10%On roadca. 75 to 90%
90%Parkedca. 25 to 10%

Self-driving vehicles could be distantly accessible for temporary use on demand. There will not be a question about “where to park” for the user anymore. Thanks to that and according to the organisational model, self-driving vehicles will be much more useful, cheaper and with no additional requirements for the user. They will not eliminate public mass transportation measures, but the owning of individual cars for private use will lose its rationality: functional and economic meaning. It wouldn’t be necessary to put quite a large amount of capital into machinery that is fast losing value, or to provide smart individual mobility. The periodic subscription giving the right to take part in the sharing of self-driving cars will be the answer, and also very cost-effective.

Vehicles in temporary use could be much more precisely customised to an exact, current need, e.g. the ones for only one or two persons without luggage could be very small. According to the well-known real manner in which private cars are used in cities (mostly with 1-2 people on board during rush hours), there could be a small number of larger vehicles needed, which could be used for luggage or other cargo. Due to the vehicle operators’ pursuit of cost optimisation, one could expect there to be limitations of access to vehicles during rush hours.

The user of a vehicle will be free of service and cleaning activities. It will bring a large scale saving of the user’s time in the social dimension and also some new professions, but this last trend will probably not be sufficiently strong to replace the loss of employment for drivers. It also will allow some dimensions of the technical activities associated with the service of vehicles to be avoided. Self-driving vehicles would drive to service stations by themselves during off-peak hours.

The lower costs of utilising self-driving cars will also be supported by the higher safety of such vehicles, if this is confirmed (Kublik 2015). It will bring much lower costs of insurance and accidents.

Consequences for mobility: ways of moving, user groups and lifestyle

The factors described above mean that the group of people independently using individual vehicles could be much, much wider than today. It may also mean that many groups of people (disabled people, teenagers, elderly people, persons without a driving licence) will get much greater potential access to permanent, independent, comfortable short and medium distance trips than today, without expenditure of a large amount of capital, so we can expect an increase in total traffic (Smith 2012b).

The widespread use of systems for the sharing of self-driving vehicles should lead to a rapid decrease in the number of traditional, individual cars (Włodarski 2014). We can see that even now in these European cities where a mix of smart, integrated public transportation systems and the beginnings of car sharing systems coexist. These are also compact with urban structures having a mixture of functions and the levels of motorisation are much lower than in cities without all of these factors. We can compare the economically much richer Paris and Berlin with less than 300 cars/1000 inh. in 2008 (Eurostat 2013: 210) with Warsaw with 598 cars/1000 inhabitants in 2012 (Miasta drogowe. n.d.). Of 15 European cities with a level of motorisation higher than 600 cars/1000 inh., all except one (Luxembourg) are in Italy (Eurostat 2013: 208). Cities from the Netherlands, UK and Switzerland show much lower levels. So this rate is not dependent on economic prosperity, but rather on lifestyle factors, the need for symbols of economic status, and probably also with the way that the public transportation system is organised. If means of transportation lose their emotional value, this will lead to the optimisation of all transportation systems in cities.

The much greater efficiency of use of future vehicles means that in spite of a wider circle of users, the number of existing vehicles will greatly decrease (CPB Report 2015) but the number of vehicles in operation on the streets would not change significantly: it will probably not decrease or it may even slightly increase according to the great increase in the intensity of use of each vehicle. Thanks to remote wi-fi connections between vehicles, they could be proactively informed of traffic conditions and the intentions of other cars. This will also allow for a decrease in the distances between vehicles, thanks to a much faster reaction of the vehicle steering systems than with human drivers. The traffic will even be much more intense, but calmer, more continuous, and without long stops and without often overtaking cars one at a time.

The utilisation of self-driving cars will be much more flexible, even with the possibility of easily arriving close to the entrance of a building, stopping for the moment necessary for a passenger to get out and then letting the vehicle drive off again. There will be no waste of time looking for parking space and often quite a long pedestrian walk from the parking lot to an exact destination etc. The other very important change and flexibility factor is the lack of obsession that a person has with the location where they have left their car. He/ she will be able to come by vehicle to their destination (i.e. work), then go shopping on foot without the need to go back to the place where their car was parked. It will be possible to start the return trip from a different, practical location after calling a car. This will often help to avoid very short distance “technical” displacements of cars on such occasions and to avoid the walk back to the original place where their car had been parked. This is an especially common experience in city centres. Paradoxically, it could cause an increase in pedestrian movement since people who have up to now intensively utilised traditional cars wouldn’t need to “take their car with them” anymore to avoid walking too long a distance to the vehicle.

Driverless cars, were they to become equally widespread in different regions and countries, should support traditional mass, long distance means of transportation. The possibility of accessing useful short or medium distance means of transportation in the target region will make bimodal or multimodal organisation of a journey a more obvious choice and will push many people to abandon the traditional car as the means of transport for long distance journeys. It would be much easier if a self-driving car operator were a global company or if systems of operation were compatible across different countries (recognition of users accounts and subscriptions). It may apply to holiday journeys or business trips and provide particular support for means of transportation such as train and plane.

Consequences for space consumption and urban spatial structure

Vehicles which are not fulfilling the roles of status symbols for their owners could be customised to meet the real needs they would serve. Cars for one or two persons for a short distance trip could be very small and consume a very small amount of space on the street. Vehicles designed to take luggage or for a larger number of persons could be much rarer than today.

Thanks to advanced sensor systems (radar, lidar, cameras, etc.), and also inter-car communication, vehicles will get much more information about the surrounding area than a traditional, human driver. Some of that information will be pre-emptive information. This allows inter-car distances to be shorter and as a result road traffic can be much more continuous and calm. Even if the average traffic speed is low, traffic flow will be higher and space consumption lower. There will be no need to overtake vehicles one after the other in urban areas thanks to the good continuity of traffic flow, so the width of the road (street) could be reduced under the condition of providing bands for short stops and side exclusion strips at street intersections.

Taking into account all the circumstances analysed above, we can try to describe the consequences for urban space in the following respects:

  1. consumption of public urban space;
  2. functional structure, accessibility of places/functions. We can be sure that the spatial consequences of the widespread use of self-driving cars are mostly connected to the structural ones:
  3. a decrease in the space needed for long-term parking of vehicles;
  4. new parameters for the driveways of public buildings, residential areas and private parcels;
  5. the need for the continuity of strips for temporary stops along streets;
  6. probably a further increase in the domination of the role of mobility in the urban lifestyle.

It is possible that the potential will arise to reduce the size of building parcels, due to the possibility of removing most of the parking places. It must also be obvious that one will be able to avoid building expensive underground and multilevel parking lots and garages.

Impact on the dominant model of housing

It is interesting to consider how the future organisation of transportation systems can be expected to influence suburbanisation and urban sprawl? This phenomenon is closely connected to mobility over medium distances and to date this is closely associated with the accessibility of the private car (1950-1960’s USA, 1970-1980’s Western Europe, 1990-2000’s Eastern Europe). If the sharing of self-driving cars makes short and medium distance mobility far more accessible for more groups of people, it means that it can increase the suburbanisation process and as a consequence, the phenomena of urban sprawl. We can expect that it is almost as certain to bring a mass dimension to the search for building plots further and further out in the open countryside and an increasing spread of the individual, one family house model. Although, under conditions of a lack of a need for parking lots and garages, increasing costs of contemporary housing within the inner-city, parallel to a decreasingly onerous presence of self-driving-cars in city centres (lower speed, better traffic flow continuity and calm, less area occupied than by traditional cars), we can expect an increasing attractiveness of multifamily housing within intensive urban structures. So, the new model of transportation may not radically change the relationship between models of housing. It probably will depend on the model of family structure and other demographic factors. Economic status will probably play a less important role than today (Tab. 2).

Table 2

Benefits and risks of new solutions for urban development

Source: own study

Area of impactBenefitsRisks
1.spatialground consumption decreasingurban sprawl increasing
2.socialincreasing availability of mobility, weak inclusion of social groups in individual mobilitydanger of further social atomisation
3.economicreduction of individual mobility costsemployment reduction, widespread economic consequences of urban sprawl
4.environmentalpossible reduction of the quantity of vehiclesenvironmental consequences of urban sprawl widespread

Fields of research required according to future spatial consequences of self-driving cars

Considering the above analysis of factors connected to the development of the idea of self-driving cars, we are able to describe fields of research looking at the spatial consequences of the widespread adoption of this new model of mobility:

  1. new types of transportation system infrastructure and the resulting spatial requirements: new types of means of transportation and their forms of organisation mean that there are new requirements for the accompanying infrastructure. One will need to classify its types and to define new rules of location and the space consumption implications of each type;
  2. the functional structure of cities, new rules of spatial arrangement of both public and commercial facilities over urban areas: the discussion of the accessibility of facilities versus mobility in cities can be observed in contemporary life. From an urban and sustainable development point of view, accessibility should be the prime consideration but a far easier accessibility of means of individual transportation will probably increase the contemporary tendency for mobility considerations to dominate;
  3. the future scale of spatial expansion of man-made structures: new means of transportation, like all previous ones, will change the dynamic of spatial processes, especially such phenomena as suburbanisation and the spatial structures associated with it: urban sprawl. It needs intensive studies on these phenomena as well as on tools to moderate them;
  4. new rules and indicators of the shaping of the urban environment: self-driving cars could also change the rules for urban design: building parcels, neighbouring and public spaces, especially related to the possibility of reducing the number of parking spaces, garages, parking lots etc. It can also bring about a new structuring of space in the city street. It will need to match them to the transportation system with a share of selfdrive cars;
  5. another sphere is related to questions on whether self-driving car sharing systems could be useful over areas of dispersed investment – urban sprawl. Would the availability of shared self-driving cars on demand be useful in areas with a low density of urban structures and without a clear spatial structure? What are the margins of waiting time for a vehicle that has been requested? Would traditional cars be kept in such areas?


We still don’t know when the fully accessible, commercial use of self-driving cars will commence. Additionally we don’t know to what extent the sentimental aspect of owning a traditional car could slow down he development of the idea of vehicles for temporary use. The stimulus for promoting the widespread use of this idea will certainly be an economic factor. Many more people will utilise self-driving cars than use traditional ones now. In particular this will apply to those groups that have up to now been excluded. Traffic levels could probably be similar or even larger than present with a share of traditional cars. Consumption of space would be much lower thanks both to resigning from most parking places and the calming of traffic and making traffic flow faster and more continuous. Building parcels and roadways could be smaller and pavements could be wider. New elements of streets could be temporary stop belts. These will be necessary to create new standards of roadway solutions, including a new dimension for roadways, strips along roads for temporary stops, and maybe also vehicle charging zones if widespread systems are adopted to permit inductive charging while driving. These are the main fields of research on the new structure of urban areas in the future and their solutions. The list is probably still not complete and there will be a need of wide-ranging research for a moderate optimisation of land use to the conditions of mobility.

The fundamental question concerning the consequence of widespread use of new means of transportation is: how it will influence the quality of life? How will the widespread use of self-driving cars influence aspects of sustainable development? We will begin to know the answers to these questions during the coming decades.


The research was carried out within the framework of project No. S/WA/1/2017 funded by the Polish Ministry of Science and Higher Education.


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