CPB

Corporate Partnership
Board

Streets That Fit
Re-allocating Space for
Better Cities

Corporate Partnership Board
ReportStreets That Fit
Re-allocating Space for
Better Cities

Corporate Partnership Board
ReportThe International Transport ForumThe International Transport Forum is an intergovernmental organisation with 63 member countries. It acts
as a think tank for transport policy and organises the Annual Summit of transport ministers. ITF is the only
global body that covers all transport modes. The ITF is politically autonomous and administratively
integrated with the OECD.
The ITF works for transport policies that improve peoples’ lives. Our mission is to foster a deeper
understanding of the role of transport in economic growth, environmental sustainability and social
inclusion and to raise the public profile of transport policy.
The ITF organises global dialogue for better transport. We act as a platform for discussion and prenegotiation of policy issues across all transport modes. We analyse trends, share knowledge and promoteexchange among transport decision-makers and civil society. The ITF’s Annual Summit is the world’s largest
gathering of transport ministers and the leading global platform for dialogue on transport policy.
The Members of the Forum are: Albania, Armenia, Argentina, Australia, Austria, Azerbaijan, Belarus,
Belgium, Bosnia and Herzegovina, Bulgaria, Canada, Chile, China (People’s Republic of), Colombia, Croatia,
Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, India,
Ireland, Israel, Italy, Japan, Kazakhstan, Korea, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta,
Mexico, Republic of Moldova, Mongolia, Montenegro, Morocco, the Netherlands, New Zealand,
North Macedonia, Norway, Poland, Portugal, Romania, Russian Federation, Serbia, Slovak Republic,
Slovenia, Spain, Sweden, Switzerland, Tunisia, Turkey, Ukraine, the United Arab Emirates, theUnited Kingdom, the United States, and Uzbekistan.

About the Corporate Partnership Board
The Corporate Partnership Board (CPB) is the International Transport Forum’s platform for engaging withthe private sector and enriching global transport policy discussion with a business perspective. The
members of the ITF Corporate Partnership Board are: AB InBev, Airbus, Allianz Partners, Alstom, Aramco,
Argo AI, Arrival, AutoCrypt, Bosch, CEiiA, Cruise, ExxonMobil, Iberdrola, Kakao Mobility, Michelin,
Mott MacDonald, NXP, PTV Group, RATP Group, Rolls Royce, Shell, Siemens, SPEA Engineering,
Tier Mobility, Total Energies, Toyota, Trucknet, Uber, Valeo, Voi, Volvo Cars and Volvo Group.

Disclaimer
Funding for this work has been provided by the ITF Corporate Partnership Board. This report is publishedunder the responsibility of the Secretary-General of the ITF. It has not been subject to the scrutiny of ITF
or OECD member countries, and does not necessarily reflect their official views or those of the membersof the Corporate Partnership Board.
Cite this work as: ITF (2022), “Streets That Fit: Re-allocating Space for Better Cities”, International Transport
Forum Policy Papers, No. 100, OECD Publishing, Paris.

ACKNOWLEDGEMENTS

Acknowledgements
This report was written by Philippe Crist, Luis Martinez and John P. Pritchard of the International Transport
Forum (ITF). Luis Martinez and John P. Pritchard carried out the modelling work in support of this report,with input from Orla McCarthy.
This report was informed by a workshop with members of the ITF Corporate Partnership Board, including
Sven Lengsfeld (Bosch), Wolfgang Brückler (Kapsch TrafficCom), Laurent Tridemy (Michelin), Philippe
Ventejol (RATP Group), Christian Irmisch (Siemens), Maguelonne Chandesris (KISIO-SNCF), Allison Wylie(Uber Technologies), Santosh Rao Danda (Uber Technologies) and Florence Prybyla (SNCF). Also
contributing to the workshop discussions were Carlos Felipe Pardo Velez (NUMO), Karen Vancluysen (Polis
Network), Vicente Torres Garibay (Grow), Urs Walter (Swiss Federal Department of the Environment),
Chris Bruntlett (Dutch Cycling Embassy), Marits Pieters (Municipality of Amsterdam), Danique Ton (TU
Delft), Shaleen Srivastava (Immense), Bernike Rijksen (Dat.Mobility) and Hans Huisman (Goudappel
Coffeng). Other CPB members of the project not present in Amsterdam include Laurence Wilse-Samson(Bird), Louis Pappas (Bird), Ashwini Chhabra (Bird), Manon Lee (Bosch), Gilbert Konzett (Kapsch
TrafficCom), Felipe Garcia Castello (RATP), Maximilian Eichhorn (Siemens), Anka Schild (Siemens), Pierre
Messulam (SNCF) and Paul-Henri de Laboulaye (SNCF). Philippe Crist, Luis Martinez, Katja Schechtner andSharon Masterson all participated for ITF. The authors would also like to thank Barry Colleary, Head ofTransport Modelling at the National Transport Authority of Ireland, for providing transport data from
Dublin for the modelling.
The work for this report was carried out in the context of a project initiated and funded by the International
Transport Forum's Corporate Partnership Board (CPB). CPB projects are designed to enrich policy
discussion with a business perspective. They are launched in areas where CPB member companies identify
an emerging issue in transport policy or an innovation challenge to the transport system. Led by the ITF,
the project development is carried out in a collaborative fashion in working groups consisting of CPB
member companies, external experts and ITF staff.
The project was managed by Philippe Crist. Jari Kauppila, Sharon Masterson and Mary Crass (all ITF)
reviewed a draft version of the report. Suzanne Parandian (independent) edited the report and Hilary
Gaboriau (ITF) co-ordinated publication.

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TABLE OF CONTENTS

Table of contents
Executive summary ................................................................................................................................ 6
Allocating street space: How and why?.................................................................................................. 9
Tensions inherent in road space allocation ..................................................................................... 10
How to measure space consumption .............................................................................................. 13
New frameworks to characterise street space ..................................................................................... 22
Modelling mobility and space consumption......................................................................................... 26
Scenario definitions .......................................................................................................................... 28
Modal alternatives ........................................................................................................................... 28
Street space functions and configurations ...................................................................................... 30
Assessment indicators of model outputs ........................................................................................ 33
New mobility and dynamic street space allocation: The impact on space consumption ..................... 35
Activity in the network and modal shift ........................................................................................... 37
Space consumption .......................................................................................................................... 40
Modal accessibility: What modes can be used to perform current trips? ...................................... 43
Environmental considerations ......................................................................................................... 45
Conclusions........................................................................................................................................... 50
Notes .................................................................................................................................................... 52
References ............................................................................................................................................ 53

FiguresFigure 1. A short history of street space development ........................................................................ 11
Figure 2. Different standard parking space footprints in Europe and the United States ..................... 14
Figure 3. Vehicle footprints of cars sold in Europe by model launch year, 1960-2019 ........................ 15
Figure 4. Change in dimensions of selected vehicle models from launch year to present................... 16
Figure 5. Space consumed per person per trip for different trip characteristics ................................. 21
Figure 6. The Good Street vehicle families, classified by weight and dimension ................................. 23
Figure 7. Indicative classification of various vehicles into six vehicle families ...................................... 24

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Figure 8. The Good Street archetypal urban traffic environments....................................................... 25
Figure 9. Updated model framework to account for space allocation and use.................................... 27
Figure 10. Dynamic reconfiguration of road space: Possible in-model conversion
pathways of road classifications ........................................................................................................... 32
Figure 11. Street performance compared to baseline over 24 hours .................................................. 36
Figure 12. Street performance compared to baseline in the morning peak ........................................ 37
Figure 13. Passenger-kilometres by mode ........................................................................................... 38
Figure 14. Changes in activity between baseline and full implementation scenarios by mode ........... 39
Figure 15. Modal switch (baseline vs. full implementation scenario) .................................................. 40
Figure 16. Total and share of space consumed by mode per day ........................................................ 42
Figure 17. Relative space consumption: Dynamic versus static space ................................................. 43
Figure 18. Fleet relocation dynamics in a central urban area .............................................................. 47
Figure 19. Fleet relocation dynamics in an outer urban area ............................................................... 48

TablesTable 1. Static space consumption per person by mode and configuration ........................................ 17
Table 2. Maximum vehicle and person throughput by mode for different carriageways .................... 18
Table 3. Description of modal alternatives included in the simulation exercise .................................. 29
Table 4. Street space functions and configurations ............................................................................. 31
Table 5. Space consumption by mode.................................................................................................. 41
Table 6. Potential travel isochrone indicator: Modal shift with Full implementation scenario ........... 44
Table 7. Traveller modal propensity indicator: Willingness to use different modes ............................ 44
Table 8. Traveller modal propensity indicator: Willingness to use different modes (by location) ....... 45
Table 9. Load factor by mode and use rate in full implementation scenario ....................................... 46
Table 10. Environmental impacts and travel volumes by mode........................................................... 49

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EXECUTIVE SUMMARY

Executive summary
What we did
Street space in cities is a rare resource. Much of it is currently allocated to highly space-consuming
transport modes without taking into account that demands for that space vary over time. This report looks
at how street space has typically been allocated in the past, examines the rationale for street space
allocation and describes how to measure space consumption for mobility purposes. The study also
explores by way of a simulation how new mobility services and travel modes interact when a limited,dynamic and demand-responsive re-allocation of street space is introduced in a mid-sized city. The
modelling framework put people first by framing the issue around how this space serves the populationand not on how the vehicles occupy the space.

What we foundThe most visible infrastructure in cities is transport infrastructure. It is a necessary component of wellfunctioning cities but also consumes a tremendous amount of scarce and therefore precious urban space.
The allocation of urban space is fraught with inherent tension. Space-related decisions are based on
improving the speed of cars and thus the travel times of their occupants to enable them to maximise whatis scarce to them, namely time. For communities, the impacts of speed are less clear-cut. It contributes toimproving access for those who are travelling fast but at the same time consumes a disproportionate
amount of space.
This tension is exacerbated by the fact that space allocation revolves around the question of how vehicles
use it and not on how well it serves people, despite the many potential and desired uses. Such an approach,however, favours car users over those who travel by other means or would use that space forother purposes.
There is an important distinction to be made between the use of street space versus the allocation ofstreet space. On the one hand, the actual use of street space is a representation of the demand for theuse of that space for travel. The demand and use vary by transport mode, by time of day and by urbanzone. The use of space relates to how much physical space people and vehicles actually consume as
they move.
On the other hand, the allocation of space represents the supply of street space made available for people
to move or linger and for vehicles to operate, park or otherwise idle. Currently, space allocation is largely
static over the course of the day. Such rigidity leads to street configurations that typically reflect the needsof the most space-consuming modes and uses allowed there. It invariably results in inefficient use of space.
Moving away from the static allocation of street space towards flexible approaches poses challenges.
However, the temporary, reversible allocation of street space can make sense in specific contexts andconfer considerable benefits.
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EXECUTIVE SUMMARY

The tension around street space in cities also comes from a lack of universal and comparable metrics toinform allocation choices. To serve that purpose, such metrics must incorporate both the dynamic aspectof street space consumption as vehicles move and the static consumption of public space by parked or idle
vehicles. These two components can be captured in a composite indicator: square metre hours consumed(m2.h) per traveller offers a good basis for discussing the relative space-consumption impacts of different
travel modes and trips.
Together, limited and dynamic re-allocation of street space plus additional free-floating mobility services
and other mobility options could significantly improve mobility outcomes. The simulations for this study
suggest a performance improvement of the urban transport network of approximately 12%, measured byindicators for mobility, accessibility, environment, space efficiency and safety.
Without changes to the available mobility options and patterns, limited and dynamic space re-allocation
improves network performance by 9%. Including new modes without changing to a dynamic street
configuration generates only a 3% improvement.
The introduction of a wider range of generally more space-efficient modes and the limited, demandresponsive, re-allocation of street space allocation leads to a 19% reduction in the overall space consumed
by all trips in the modelled city. These savings could create new opportunities for developing new uses for
public city space. These benefits are not evenly distributed geographically, however. They areconcentrated in the urban core, where competition for alternative uses of street space is greatest.
The combination of new mobility alternatives and limited, dynamic re-allocation of space also brings
significant environmental gains. Despite increasing the number of passenger-kilometres, overall transport
CO2 emissions fall by around 5%. Particulate matter emissions drop by 4% and could fall by 23% if theshared vehicles are electric and powered using the current electricity mix.

What we recommend
Adopt meaningful indicators for how urban street space is used
No straightforward and comprehensive indicators are currently in use to assess space consumption.
Traditional capacity metrics such as speed-flow relationships or other throughput measures do not capture
the static and dynamic aspects of space consumed by travellers. Neither do the most common aggregate
measures of transport demand such as vehicle-kilometres or passenger-kilometres travelled. An indicatorthat fills this gap is the square metre hour (m2.h) occupied by a vehicle- or person-trip. This composite
indicator includes the static consumption of space, i.e. the on-trip parking and idling of a vehicle, and the
dynamic use of space, namely the exclusive occupation of street space by the vehicle as it moves.
Re-allocate street space to account for diverse uses and users
Prevailing road classifications are largely designed to manage the use of road space by cars, vans andtrucks. Opening that space to other vehicles, users and uses – even non-transport uses – may bring benefits
in many contexts. More diversity and flexibility in street space allocation requires a shift in the practice ofallocating space, however. This includes aligning the legal speed of vehicle traffic with the characteristicsof the street space. Which vehicles can use what street should be defined by the infrastructure’s
characteristics. A vehicle could access those parts of the network whose design is adapted to the vehicle’s
mass and dimensions. Such an approach would enable a broader range of safe uses of public spacedesignated for mobility uses and help establish a wider variety of street uses.

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EXECUTIVE SUMMARY

Prioritise people over vehicles when allocating street space
Street space allocation should put people first. It must focus on how that space serves the population andnot how vehicles occupy that space. For that, it is critical to consider how the mobility needs of people canbe met and assess the access provided by the transport system. Decisions about street space allocation
should, therefore, always take as their starting point the space required per person, not per vehicle.
Explore the benefits of dynamically re-allocating certain street spaces
Moving from rigid towards demand-responsive street space allocation to make it available to a wider setof users and uses can bring benefits. These accrue from a more space-efficient use of streets and the ability
for people to travel safely with a wider range of vehicles and modes. Limited and dynamic re-allocation ofstreet space also improves the resilience of the urban transport system through the ability to flexibly meetchanging demand.
Adopt Safe System principles to guide the re-allocation of street space
The re-allocation of urban street space must be carefully designed and implemented to avoid negative
impacts on traffic safety. Street space re-allocation should incorporate the principles of the Safe System:that humans will make mistakes, that injuries result if the human body is exposed to known levels of kinetic
energy, and that all actors in a traffic system share responsibility for road safety of all, not only that ofmotor vehicle occupants. In a Safe System, all elements of the road traffic system combine to prevent acrash or at least prevent serious injury. Speed management and traffic separation, where necessary, are
two pillars of a Safe System. Integrating Safe System thinking into decisions on street-space allocation may
limit the range of potential reconfigurations achievable.

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ALLOCATING STREET SPACE: HOW AND WHY?

Allocating street space: How and why?
The most visible and omnipresent infrastructure in cities is related to transport – and road transport inparticular. Urban motorways, boulevards, streets, parking spaces, off-road or multistorey car parks,intersections and interchanges all constitute the necessary foundation for the automotive age. However,they also consume a tremendous amount of otherwise rare (and therefore precious) urban space.
Accessibility is broadly defined as the extent to which the land use and transport systems enable individuals
to reach activities or destinations (Geurs and van Wee, 2004). The spatial dimension of accessibility islinked to the interplay between its three constitutive components: available activities/opportunities, theirlocation, and travel to them. The latter helps link the previous two – but so too does proximity. Increasingproximity (and thus density) or increasing the speed of travel increases the number of opportunities andactivities that individuals can satisfy for a given period of time (Crozet, 2020).
From the perspective of travellers, increased speed is an acceptable substitute for physical proximity since
it delivers the same result in terms of linking people to destinations. For individual travellers, time is a
scarce resource. As such, speed delivers valuable time savings in getting from A to B, time which can beused for other things. Aggregated together, cumulative time savings represent one indicator of collective
welfare and has served as the basis for measuring the impacts of transport investments and other policies(Crozet, 2020). This indicator has dominated project and policy appraisal but has also been criticised forpoorly capturing certain important policy considerations and leading to unjust outcomes (Martens andCiommo, 2017).
For communities, the impacts of speed are slightly different since travel time savings contribute to overall
agglomeration benefits but consume a valuable and scarce public resource in communities – space.
Consequently, there is an inherent tension between the time savings that individuals wish to maximise by
their mobility behaviour and the efficient allocation and management of public space in urban areas. Thistension is further exacerbated by two factors: that urban space, even space predominantly allocated tomobility, has multiple potential and desired uses, and secondly, that its allocation has largely concerned
how vehicles use that space versus how well that space serves people. These factors, although different,
favour the rights of those who use cars versus those who do not. The tension around urban mobility space
allocation is further complicated by the lack of a universal and comparable set of metrics that can help
inform choices on how this space should be allocated.
The discussion around the demand for and use of street space versus the allocation of street space should
not conflate the two concepts. In the first instance, the actual use or consumption of street space is a
representation of the demand for the mobility-related use of that space. This use/demand aspect isvariable by mode, time of day and zone, and represents how much physical space is actually consumed as
people and vehicles move.
The allocation of space represents the supply of urban road space made available for people and vehicles
to move and operate. The allocation of space is largely static over the course of the day and invariable overtime. It represents an investment in hard infrastructure (roadways, junctions, on- and off-street parking)

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and is typically dimensioned for peak demand (for roads) and to allow for generous storage of personalvehicles in residential areas and in commercial or work destinations (for a discussion of parking supply see
ITF, 2021a).

Tensions inherent in road space allocation
Despite periods of relative stability, road and urban public space allocation is often contested – especiallyin response to broader societal changes, local demands and external events. The global Covid-19 pandemic
is one example of such an external stressor. Existing trends seeking to give more space to active mobility
in many cities have questioned the basis for, and practice of, allocating most road space solely to motor
vehicle traffic and storage. The tension around road space allocation is typically strongest where that spaceis rarest – in core urban areas. But it also emerges elsewhere when existing road space allocation
practices exclude other desired uses of that space. The tensions inherent in road space allocation canbroadly be categorised into four areas: liveability, capacity, spatiality, and network. Liveability relates towhy and under what assumptions street space is allocated. Capacity relates to how and by whom road
space is allocated. Network relates to where the space is being allocated. Spatiality relates to what isbeing allocated.
Tensions around liveability

At the heart of many of the tensions around the allocation of road space are the assumptions about who
should use that space and for what purpose.
Current practice regarding the allocation and supply of urban road space implicitly assumes that this spaceshould be reserved exclusively for the movement and storage of vehicles. This stance stems from theactors and institutions that have over time defined how, by whom and to whom road space should beallocated. At its core is the shift from a broader iterative negotiation of how that space may be used, to a
traffic engineering-led technical specification of how that space should be used by vehicles (Jones, 2014).
Such a vehicle-oriented technical specification makes sense for major thoroughfares and urban motorways
where mixing uses would prove dangerous and inefficient. However, for residential and many commercial
streets, such exclusive allocation of road space is contested as there are other valuable (and valued byresidents and shopkeepers) uses to which that space may be put, including children’s play, socialising,
shopping and dining. These alternative uses gained attention during the Covid-19 pandemic, with thecontestability of urban space sharpened in many areas around the world.
Assumptions underpinning road space allocation have emerged largely in response to the large-scale
deployment of motor vehicles whose speed and mass made them unsafe for mixed uses of that space andwhose economic contribution to urban areas was prioritised over contributions stemming from other usesof that space. In addition, the technical characteristics of these vehicles – especially their speed – led tothem occupying an increasing share of that scarce space. Thus road space allocation has prioritised motor
vehicles under the understanding that this represents the best use of that limited space while at the sametime, the amount of space allocated to motor vehicles has increased due to their technical characteristics.
This development represents a break with historic trends in street space allocation (Figure 1).
A second assumption is that the main metric for evaluating the allocation and supply of urban road spaceshould be the space required by vehicles to operate safely. This is understandable since under-sized roads
would lead to more inter-vehicle conflicts and increased congestion for a given level and mix of vehiculartraffic. Nonetheless, the allocation of road space using vehicle space consumption requirements as its basis

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is highly problematic in urban areas where space is rare. It conflates social welfare outcomes with the
movement of vehicles and not with the movement of people. By doing so, it excludes (or minimises) the
spatial needs of people who are travelling using more space-efficient modes and favours the least spatiallyefficient modes – especially single-occupancy car use. It leads to a biased and inequitable allocation of
space to the fastest and least space-efficient modes, contributing to overconsumption of scarce urban
space and reducing overall welfare outcomes. A better metric to guide the allocation of space from astrategic perspective would relate to the amount of space required to move people, and not the vehicles
in which they travel.
Figure 1. A short history of street space development

Carriageway width= people

Pre-1880

Pedestrian-dominated,narrow, windy roads,
multiple uses of streetspace

1880-1920s

Pedestrian-dominated,roads widened, paved,
street grids introduced

1920s-Today

Future?

Motor vehicle-dominated, Simultaneous
motorway infrastructure, accomodation of multiple
pedestrians/cyclists/other users, light modes given
uses pushed out of thespace, some streets
streetbecome narrower

Source: Based on Ward (2018).

Alternative assumptions around uses of road space and the prioritisation of space allocation have emergedin different, largely local contexts. These assumptions give more consideration to alternative uses of road
space – even beyond vehicular movement and storage – and users of road space. One of the clearest
points of tension around discussions on road space allocation is the conflict between how local authorities
and citizens may wish to revisit road space allocation and the planning and engineering requirements that
underpin current practice (Jones, 2014; Halpern et al., 2020).
Tensions around capacity

Linked to discussions on why and to whom road space should be allocated is the question of who makes
those determinations and on what basis. As noted above, road space allocation decisions are largely
framed by traffic engineering considerations. Additionally, where economic appraisal is required orincorporated, transport economists provide guidance. This should not be surprising as once theassumption has been made that road space should be prioritised to the movement and storage of vehicles,

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it makes sense to involve traffic engineers. Given that road space is a scarce resource, it is also logical to
involve economists and economic appraisal to help determine the most efficient and welfare-improving
allocation of that resource. Nonetheless, because both traffic engineers and economists may work on thesame set of assumptions outlined above, the outcome of their involvement may well ignore other valued
uses of urban road space or exclude other, more space-efficient, uses of that space.
A second issue to consider is how road space provision and allocation decisions are made. This issue islinked to how transport planning and provision is carried out and has, over time, shifted from ad-hoc
decision making to a more formal, plan-based and model-supported decision framework. This frameworkhas generally focused on planning and providing for motorised vehicle demand (both private and public
transport-based) under the twin assumptions of the continuation of past growth trends into the future
and a stability in the way in which exogenous factors impact transport demand. The main task for publicauthorities in that context was to predict future demand growth and then to accommodate that growthby re-allocating existing road space or building new infrastructure. This “predict and provide” approach
has served well in periods characterised by stability in demand growth and the relative impact ofexogenous factors – especially during the large-scale uptake of motorised vehicles in the last century.
However, this approach has become increasingly problematic (ITF, 2021b). In many instances, future
demand forecasts have failed to materialise. Where these forecasts have served as the basis for providing
infrastructure, this has led to an over-allocation of space to accommodate projected growth which,paradoxically, then served to generate some of the very growth in demand it was meant to accommodate(ITF, 2021b). Thus, demand forecasts have become somewhat self-fulfilling, at least in their trajectories, if
not in their levels. In addition, such “predict and provide” approaches favour existing uses of road space
and are less well-suited to other users or for new, unanticipated but potentially valuable uses. Anotherissue is how well such aggregate projections are able to detect or accommodate more local uses of urban
space and guide allocation decisions on a city or neighbourhood scale (ITF, 2021b).
For these reasons, a renewed interest has emerged in the decision-making processes that recognise
agency on the part of public authorities and are based on a strategic vision for future urban mobility. These“decide and provide” approaches make explicit a vision of what the future allocation of public space shouldlook like and how that vision should guide decisions on allocation (ITF, 2021b).
Tensions around networks

Decisions regarding road space allocation differ according to where in the network they are required andon what types of roads and public spaces they should be implemented. In urban contexts, publicauthorities face pressure to do more with existing (or less) space. How they solve this challenge is guided
by systems of hierarchical road classifications. Even when considering the allocation of road space
principally for vehicular use, it became clear early on that not all roads serve or should serve the samefunction. This has led to the development of road hierarchies that stipulate what kind of functions anduses should characterise different types of roads. The conventional road classification hierarchy organises
the road network based on the functional characteristics of each road class, ranked by a set of normative
factors relating to safety, speed and traffic volume. Different road hierarchies have emerged but theylargely differentiate road function between through use (e.g. motorways and arterials) and local accessfunctions (e.g. local and collector roads) (Levinson and Zhu, 2012; Paraphantakul, 2014).
Road hierarchies deliver several benefits from the perspective of managing motor traffic flows and access(Levinson and Zhu, 2012). By channelling the largest flows onto larger facilities, they allow economies ofscale to be achieved in the construction of limited-access, regionally significant facilities. The hierarchy
also avoids potentially dangerous conflicts among different road users and road uses. Designating and
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designing lower hierarchy links as local networks helps preserve neighbourhoods from the negative
impacts of high volumes of through-traffic. Nonetheless, especially for roads in dense urban areas, thefocus on functional hierarchies is problematic for several reasons.
From the moment they were originally conceived in the 1930s to the present, road classification
hierarchies view road space as movement space, moderating circulation on some roads and concentrating
it on others. They do not envisage non-movement uses of the space, rarely address non-motor-vehicle useof the space and almost never account for dynamic and time-variable allocation of road space. Roads andtheir uses are fixed and invariable and are, for all intents and purposes, set in stone. This does not reflect
the historical reality of road space allocation and usage — which has changed over time and according todifferent technologies and prevailing uses — and prevents potential future uses of those spaces notconsidered in current engineering and traffic planning guidance.
Tensions around infrastructure

Inherent tensions emerge when discussing the re-allocation of built assets such as roads and other formsof mobility spaces. Roads and other forms of public space are infrastructure. They are tangible assets
comprised of surface and sub-surface materials. They have set widths, turning radii, junction treatments,
traffic signs and signals, on-road markings, curbs and other delimitations marking movement space andaccess or transaction space. They are designed for specific uses and to ensure specific outcomes (e.g.
network function and safety). Decisions to re-allocate road space must necessarily contend with thephysicality of the space. They must also consider expectations regarding the use of that space by those towhom it has been allocated. For this reason, discussions around the allocation of space, and especially there-allocation of space are fraught with controversy and involve technical challenges. This has notprevented road space from being reimagined and re-allocated in specific instances or at a national scalein certain countries, such as the Netherlands (Oldenziel, et al, 2016; Dekker, 2021). However, in general,
space re-allocation is a contested process that must overcome the inertia embedded in current practices(Mattioli et al., 2020). For these reasons, efforts to redistribute urban road space have largely focused on
specific interventions or at a street level but rarely at the scale of the entire urban road network. This
makes it difficult to gauge what the impacts of such a redistribution might be if it were deployed in a
coherent, connected and cohesive manner at the urban scale.

How to measure space consumption
How much space does a person use when travelling? The answer to that question – differentiated bymode, travel distance and speed – can provide a good basis for discussions around the efficient allocationof urban road space. Though the tension between the consumption and allocation of space was noted
early in the deployment of cars in cities, relatively little effort has been made to develop and adopt suitable
metrics to measure the former compared to the latter. This is not for lack of a conceptual basis formeasuring the consumption of urban space by vehicles – the framework for which is described below.
An indicator for measuring space consumption: Square metre per hour

One of the difficulties in answering the space consumption question is that the amount of space consumed
by a person travelling has both static and dynamic elements. It is linked to the vehicle’s spatial footprint,
the number of people travelling in or on the vehicle, the amount of time the vehicle is stored during around or multi-legged trip, the trip distance and the vehicle’s travel speed. These components are notcaptured in traditional capacity metrics such as speed-flow relationships or other throughput measures.
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ALLOCATING STREET SPACE: HOW AND WHY?

Similarly, they are not captured in the most common aggregate measures of transport demand such as
vehicle-kilometres or passenger-kilometres travelled. Furthermore, they require a composite indicatorthat captures both the static (parking/vehicle storage) and dynamic (amount of space exclusively occupied
while in movement) elements of space consumption. In 1973, Louis Marchand, a senior engineer at Paris’s
public transport operator, the RATP, developed an indicator to address this gap: the square metre houroccupied by a vehicle or person-trip (m2.h) (Héran and Ravalet, 2008). This indicator has two components:
the static consumption of space related to the on-trip storage of the vehicle and the dynamic consumptionof space related to the exclusive occupation of road space by the vehicle during the trip.
Static space consumption: Parking and vehicle storage space

Trips using private vehicles involve parking the vehicle at one or several locations. This temporary storageof the vehicle requires space that may be located on the street or in off-street or multistorey car parks.
The space required is proportional to the vehicle footprint, but for practical purposes, the amount of spaceconsumed can be considered as the footprint of a standard parking space. The m2.h for parking and vehicle
storage is thus simply the amount of space consumed multiplied by the amount of time the space is
occupied. This is an invariable linear relationship, although the size of a standard parking space is variable
across different geographical contexts and has increased over time as vehicles have increased in size.
Vehicles are stored whenever they are not used – most typically at or near the owner’s residence. This
storage also consumes space but is not related to the vehicle trip, per se, but rather to the storage spacenecessary to own a vehicle. For this reason, the rest of this report only focuses on trip-based parking spaceconsumption.
Figure 2. Different standard parking space footprints in Europe and the United States

Source: Dimensions (n.d.), www.dimensions.com.

There is no single international parking space dimension. In many cases, the size of on- and off-street
parking spaces are set by traffic engineering or other bodies based on average vehicle sizes, which serve
as guidance rather than a requirement. Sometimes the guidance is set at the national level and at othertimes by regional or local authorities. In practice, this means that these dimensions are variable across
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different cities, regions and countries with sometimes significant discrepancies. The most important ofthese discrepancies emerges in North America, where average car footprints are larger than most otherregions, resulting in larger average parking space dimensions (Figure 2). The dominant share of sports
utility vehicles (SUVs) and pick-up trucks in the North American fleet, alongside a car fleet that is alreadylarger on average than other world regions, explains the shift towards larger parking space dimensions.
Figure 3. Vehicle footprints of cars sold in Europe by model launch year, 1960-201913

Car footprint(width* length,
Car footprint(W*L,m2) m2 )

12

11

10

9

8

7

6

5

4

1960

1970

1980

1990

2000

2010

2020

Source: Based on car dimension data from Teoalida (n.d.).

Over time, the growth in average vehicle size has also placed upward pressure on parking size dimensions.
Car footprint dimensions for new models sold in Europe (by launch year) have increased by approximately
18% from the 1960s to the 2010s (Figure 3). This increase has been even larger in North America due tothe rapid penetration of SUVs in the market (US EPA, 2019). Launch year dimension statistics hide growthwithin each car model over its production lifetime. Here too, dimensions have increased over time,reflecting the generalised trend in the growth of vehicle footprints (Figure 4). Parking space dimensions
include additional space for accessing the vehicle and manoeuvring in and out of the parking space. The
growth in vehicle size creates pressure to increase the storage space allocated to vehicles, just as
discussions are underway in many cities to re-allocate that space to other uses.

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ALLOCATING STREET SPACE: HOW AND WHY?

Figure 4. Change in dimensions of selected vehicle models from launch year to present

Toyota Corolla

Volkswagen Golf

BMW 3 series

1974

1974

1975

3.95 m2019

4.63 m (+17%)

3.71 m2019

4.26 m (+14%)

4.37 m2019

4.71 m (+7%)

Source: Based on data and drawings from The Blueprints (n.d.).

The footprint of on-street parking varies according to its configuration. Parking parallel to the curb
consumes the least space but other on-street parking arrangements (e.g. angled parking) consumes more
space. Off-street parking consumes even more space as the auxiliary space required to access andmanoeuvre in standard or multistorey car parks must also be included. The differences are significant andshould be accounted for in space consumption calculations. In a comprehensive study looking at spaceconsumption in the Ile-de-France region of France, Frédéric Héran compiled a set of “standard” European
parking space use values based on an empirical review (Table 1). The values in this table serve as a goodstarting point for calculating the static space required to store various vehicles. In other regions, andparticularly in North America, they should be adjusted to reflect prevailing dimensions. The values areadjusted by typical load factors to give per-person static space consumption values.
The values for the static consumption of space by buses is based on observed values in bus depots in theIle-de-France region. This space is analogous to the space required for home parking by vehicles inresidential areas and as such, it is not clear whether this space should be considered trip-based parking
since buses do not park on-trip. Buses do, however, temporarily occupy space at bus stops —approximately 24 linear metres for a 12-metre bus accounting for manoeuvring space (Héran and Ravalet,2008). Because of its short-term nature, the exclusive use of space by buses can be subsumed into the
dynamic aspect of the m2.h indicator and is related to the bus’s average commercial speed. In practice,because of the need to keep stops accessible to buses, space at bus stops is often exclusively allocated tobuses and is thus not available for other uses. So, while the bus’s consumption of space is limited, theallocation of space to buses is more extensive.

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Table 1. Static space consumption per person by mode and configuration
Mode and configuration

Parking
Ancillary andspace permanoeuvring
vehicle (m2) space (m2)Space
Average load
Static spaceconsumption factor per vehicle consumption
per vehicle(persons/vehicle) per person
(m2)
(m2)

Car
(on-street, parallel)

10

10

1.3

7.7

Car
(on-street, angled)

12

12

1.3

9.2

Car (car park)

10

15

25

1.3

19.2

Car (multistorey car park)

15

10

25

1.3

19.2

Motorised two-wheeler (on-street)

1.5

1.5

1.05

1.4

Motorised two-wheeler (car park)

1.5

2.5

1.05

2.4

Bicycle (stand)

0.8

0.8

1

0.8

Bicycle (two-level rack)

0.4

0.2

0.6

1

0.6

Bus (12-metre)*

42

28

70

17

4.1

Bus (12-metre, peak period)*

42

28

70

50

1.4

1.0

(pedestrian)(0.25)* For an 18-metre articulated bus, multiply by 1.5
Source: Héran (2012).

Dynamic space consumption: Movement space

The exclusive occupation of space by vehicles in movement is a function of vehicle size, inter-vehicle
spacing, the amount of lateral space consumed in normal operation and the amount of time the space is
occupied – itself a function of vehicle speed. Therefore, a large car travelling fast requires more space andexclusively occupies a greater linear and lateral spatial footprint in traffic. However, because it travels
through that space at greater speed, it occupies that space for less time. A bicycle, on the other hand,takes little space, has a smaller spatial footprint as it moves but occupies that space longer due to its lower
speed. Speed consumes space as it increases the vehicle’s dynamic spatial footprint, but speed also meansthat vehicles occupy that space for less time. However, of the two effects, the speed effect is the greatest– on balance, the faster a vehicle travels, the more space it consumes.
The relationship between space consumption and throughput is an inverse one. For any given width ofcarriageway, the more space consumed by a vehicle, the less throughput is achieved. Table 2 shows
maximum vehicle and person throughput values for different modes and carriageway widths. For public
transport services, it accounts for standard service-related headways and peak hour load factors.

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ALLOCATING STREET SPACE: HOW AND WHY?

Table 2. Maximum vehicle and person throughput by mode for different carriageways
Mode

Vehiclesper houranddirection

Inter-vehicle
headway for
public transport(minutes)

AverageHourlyWidth ofload factor throughput carriageway(persons per of peoplevehicle)per direction

Walking

Hourlythroughput per
metre ofcarriageway
width

9 000

3

3 000

1

5 000

3

1 667

Bicycle9 000

Bus (12-metre in traffic)

20

3.0

70

1 400

3

467

Bus (18-metre,
dedicated lane)

25

2.4

100

2 500

3.5

714

Tramway

30

2.0

300

9 000

3

3 000

Subway

40

1.5

800

32 0003.5

9 143

Commuter rail

20

3.0

1 000

20 0004

5 000

High-speed commuter
rail

30

2.0

2 000

60 0004

15 000Car (motorway)

2 400

1.25

3 000

3.5

857

Car (urban street)

900

1.25

1 125

3

375

Source: Héran and Ravalet (2011).

Strictly from a throughput perspective, the potential for public transport and active mobility is orders ofmagnitude greater than that of cars – these modes simply can convey more people per unit of space
available. However, they are unable to serve all parts of the urban region (for rail-based modes), nor canthey offer the same travel speeds (for active and bus-based modes) – though at peak hours, this differential
decreases significantly in urban centres. Further, the arrival of new modes such as electric kick scootersand electric bicycles have increased achievable speeds and ranges for light mobility modes.
Throughput metrics only indirectly reflect dynamic space consumption. Marchand’s early work at the RATP
sought to capture this dimension more explicitly and accurately with the notion of the dynamic surface,which captures the exclusive occupation of space by a vehicle as it travels.
The dynamic surface is calculated by multiplying the sum of the length of the vehicle (in metres) and theinter-vehicle spacing (in metres) by the average lane width per direction of travel (also in metres). Because
vehicle size plays a minimal role in inter-vehicle distancing (this distance is a factor of vehicle mass andspeed) and because lateral width necessary for safe operation (allowing for natural weaving and sufficient
separation from other vehicles) is also a factor of vehicle speed, there is little difference in the dynamic
surface of small versus large vehicles operating at the same speed and what difference there is relates
more to the mass of the vehicle rather than their footprint (Héran and Ravalet, 2011).
Inter-vehicle spacing is a function of reaction time and stopping time – both, in turn, are a function of
speed. Reaction time for a normal driver is approximately one second – this must be multiplied by the
speed to obtain the reaction distance component of the dynamic surface. Stopping time is the amount oftime it takes from the moment the vehicle starts braking to the moment it stops moving. This time must
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also be multiplied by speed (and adjusted for the rate of deceleration for the vehicle’s mass andcoefficients for surface friction and gravity) to obtain the stopping distance. However, (Héran and Ravalet,2011) note that inter-vehicle distances are, in practice, much shorter than the theoretical function would
predict and have decreased over time (with, for example, the introduction of ABS). For this reason, hesuggests basing the inter-vehicle distance component of the dynamic surface equation on observed values
on crowded urban roads (e.g. the Paris ring road). This results in the following function to calculate theinter-vehicle distance component of the dynamic surface (Héran and Ravalet, 2011):
IVD= V+ 0.01371 V2
Where:
IVD=Inter-vehicle distance in metres, and
V=Speed in metres per second
The lateral component of the dynamic surface calculation is comprised of the width of the vehicle, anadditional width necessary for the safe operation of the vehicle in traffic (due to natural weaving andlateral separation from other vehicles) and an additional adjustment for the space required to operate thevehicle on a roadway (junctions, shoulders, etc.). The average space consumed per lane of travel can be
simplified based on observed values (again from the Paris region) such as speed and the number ofjunctions. Thus the lateral component of the dynamic surface is represented in the following formula(Héran and Ravalet, 2011):
ALSL= 2.2 + 0.0052 V2
Where:
ALSL= Average Lateral Space consumption per lane in metres, and
V= Speed, in metres per second
The combination of the inter-vehicle and lateral components gives the dynamic surface function:
DS = (L + IVD) * ALSL
Where:DS = Dynamic Surface
IVD = Inter-vehicle distance in metres
V = Speed, in metres per second
The dynamic surface increases with the square of speed which explains the preponderant weight of speed
in the overall calculation of space consumption. However, as noted, speed also means that as a vehicle
travels faster, it occupies that space for less time. Thus Marchand’s specification for the m2.h indicator also
accounts for travel speeds and trip lengths. The resulting function for calculating m2.h/km is (Héran andRavalet, 2011):
DSC = DS * Tk
Where:DSC = Dynamic space consumption per vehicle per kilometre travelled in m2.h
DS = Dynamic Surface
Tk = Time it takes to travel one kilometre
Since Tk = 1/(3.6 V) and given that V is expressed in metres per second, the dynamic space consumption
formula can be simplified as (Héran and Ravalet, 2011):
DSC = SD / (3.6 V)

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ALLOCATING STREET SPACE: HOW AND WHY?

The above formula reflects a theoretical basis for approximating the consumption of space by mode. In
practice, it must be adjusted to reflect differences between modes. Héran and Ravalet (2011) note thaturban traffic speeds are highly variable and cars and other motorised vehicles may travel at anywhere
between 15 km/h (and slower) to 50 km/h (or faster) during the same trip. To account for this variability,
he recommends adjusting the dynamic space consumption resulting from average speeds upwards by
50%. The same adjustment should be applied to motorised two-wheelers who have limited opportunities
to bypass other vehicles in dense urban traffic. Pedestrians, cyclists and e-push scooters experience much
more even travel speeds – especially where dedicated infrastructure is available (pavements and lightmobility lanes).
Combining the dynamic and static space consumption functions outlined above and applying different
specific trip characteristics and speeds provides insight (and data) on the actual space consumed bydifferent transport modes per trip and, when trip distances and numbers are available, in aggregate.
Figure 5 shows the space consumed per person for two different trips. The first is comparable to a
shopping trip with a 6 km round-trip distance and a two-hour dwell time. The second is comparable to atypical work trip with a 10 km round-trip distance and an eight-hour dwell time.
What these examples (and others based on the functions outlined here) show is that the consumption of
space varies by mode but that the most space-intensive by far is the car. It also shows the spatial efficacyof public transport in comparison to all other modes (at sufficient load factors) and underscores the impactthat speed plays since the 5 km/h difference in speeds between motorised two-wheelers and cars is
sufficient to raise the dynamic space consumption of the former over the latter.
Figure 5 shows the impact that parking plays on space consumption, since as dwell time increases, so too
does the space exclusively occupied by vehicles. Finally, load factors play an important role in influencing
space consumption per person. Increasing the load factor of cars to three people per vehicle would almost
equalise the dynamic consumption of space between cars and bicycles. Likewise, decreasing the loadfactor of buses to ten people per bus (not unusual in off-peak hours) would triple its dynamic spatial
footprint (although it would still consume less than half the space of a car occupant at typical load factors).
The m2.h indicator serves to better differentiate modes by their actual consumption of space. As noted
earlier, in urban areas, especially in core and other locally dense areas, this space is rare and so it makes
sense to prioritise the most space-efficient modes and uses. However, the optimisation of the use of space,although important, is only one factor to consider when allocating space.
From a societal perspective, it makes sense to give space to active modes as these provide significant
health benefits on top of their mobility benefits (ITF, 2013). From a physical accessibility perspective, it
makes sense to give space for those who may require a car or another vehicle to get around due to theirmobility impairments. Finally, as noted earlier, individuals value the use of the car not only because it
allows them to access numerous destinations relatively quickly, but also because they derive other amenity
values such as carrying loads, giving others a ride and enjoying a private space in which to travel. In many
circumstances, some of these amenities could also be delivered by other modes (cargo bikes can carry
many typical carloads; cycling side-by-side on safe infrastructure provides an analogous level of privacy as
travelling within a car; travelling by public transport allows people to read or interact with internet-based
services and social media) but these practices have rarely been facilitated by the provision of car-based
infrastructure.

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Figure 5. Space consumed per person per trip for different trip characteristics

The relatively recent arrival of different new mobility services, including shared micromobility, carsharing,ridesourcing and on-demand van services, all display slightly different spatial footprints. These weremodelled in the work described later, but the main differences relate to the way in which the static
footprint is defined. On a per-trip basis, shared cars, shared bicycles and shared e-push scooters have thesame static spatial footprint as their non-shared analogues (and shared e-push scooters have similar
dynamic spatial footprints as bicycles). However, if a “trip” for shared vehicles is based on each vehicle’s
daily service cycle, then the relative share of the static component decreases in line with how long thevehicle is stopped between uses (and almost disappears for shared rides and ridesourcing) while thedynamic component of the spatial footprint increases. Seen this way, each trip can be considered one
segment of a daily composite trip chain.

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NEW FRAMEWORKS TO CHARACTERISE STREET SPACE

New frameworks to characterise street space
Most frameworks used to characterise street spaces and their functions emerged from the need to
manage motor vehicle flows and avoid conflicts with other uses. While it is natural that these frameworks
prioritised motor vehicle flows, as they were designed to do, these road classification hierarchies do notallow for new possibilities around the use and allocation of road space.
As a response, alternative street hierarchies and methods for organising urban road space have emerged.
These include frameworks such as London’s Street Families that differentiate the use of streets according
to their movement or link functions as opposed to their place-making functions. The former relates to how
people, goods and services get from one place to another by using a range of different travel modes. The
latter relates to the use of streets for living and unlocking or accessing opportunities in areas adjacent to
the movement space and to ensure essential urban functions (TfL, 2013). This approach allows for more
flexibility in how street space could be used but does not directly address issues relating to the specificallocation of street space to competing uses. For instance, streets may be designated as principally having
a movement or link function and thus favour motor vehicle use, however, allocating space to separated
light mobility infrastructure may also extend the link function to bicycles and other forms of micromobility.
Likewise, designating a street as having primarily a place function does not preclude it also serving as a link
for bicycles and other forms of micromobility (Aldred, 2014).
Other frameworks such as the superblock model (Ajuntament de Barcelona, 2016; Mueller et al., 2018)base urban space allocation and function on the network permeability for certain modes (primarily
walking, cycling and micromobility) and segregation for faster, motorised modes. The superblock model iscomprised of a higher speed link-oriented network for cars and public transport and a local, accessrestricted and speed-managed network that services active modes and residential access by motorised
vehicles. This approach is also reflected in the Street Design Principles developed by Sidewalk Labs (Ng andDoyle-Wiese, 2019).
The modelling work undertaken for this report was informed by another framework, commissioned by theRoyal Dutch Motoring Club (ANWB). This framework – the “Good Street” framework (Immers et al., 2020)– seeks to “develop a generically applicable design methodology with which urban public space (including
traffic infrastructure) can be reconfigured, while simultaneously considering quality of life, safety andaccessibility demands and requirements” (Immers et al., 2016; 2020).
This framework is based on two fundamental principles:
1. The legal speed of vehicular traffic is linked to the design and characteristics of the infrastructureand not, as currently is often the case, to vehicle type. Vehicle access is thus based on
infrastructure design and characteristics.
2. The mass and dimensions of vehicles will determine on what parts of the network vehicles are
permitted or not.
Taken together, these two principles ensure that differences in speed between vehicles are minimised bydesign (the first principle) as are inter-vehicle differences in mass and size (the second principle). Reducing
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differences in speed, mass and size are at the core of the Safe System 1 approach for road safety (ITF, 2016a;
2016b) and are a highly effective strategy to improve road safety. They also contribute to ensuring that awider variety of street functions and uses can emerge.
Figure 6. The Good Street vehicle families, classified by weight and dimension

Mass (kg)Maximum (dynamic) width (m)<35 kg<1.00 mI

<1.50 mII<350 kg<3 500 kg

IIIIV>3 500 kg

pedestrians
rideables<2.00 mlight motorised

car-like>2.00 mV

GuidedVItruck-liketram-like

Source: Adapted from Immers et al. (2016; 2020).

At the core of the framework is the classification of vehicles into six different vehicle families, as shown in
Figure 6 (Immers et al., 2016; 2020). These families were adapted for this study to reflect different formsof micromobility and, in particular, integrate the four classes of micromobility vehicles established in the
ITF’s work on Safe Micromobility (ITF, 2020b). The vehicle families are as follows:•

Vehicle family 1: Pedestrians. This family acknowledges the place of all persons walking, both asusers of public space and as a standalone mode of transport, or as a way of accessing other modesof transport.

•

Vehicle family 2: All “cycle-like” modes of transport that are lighter than 35 kg and no wider than
1.5 m. These can be considered as “rideables”. This family includes bicycles, e-bikes, e-pushscooters and other like vehicles. It covers ITF Micromobility types A and D (ITF, 2020b).

•

Vehicle family 3: Small yet heavier vehicles, including ITF Micromobility type C and D (ITF, 2020b).
This comprises heavier cargo bikes, small urban goods distribution vehicles, motorised
wheelchairs, rickshaws and heavier mopeds and other motorised two or three-wheelers.

•

Vehicle family 4: All car-like vehicles, including vans and SUVs.

•

Vehicle family 5: All truck-like delivery, service or work vehicles, including buses.

•

Vehicle family 6: On-street guided vehicles such as trams and streetcars.

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NEW FRAMEWORKS TO CHARACTERISE STREET SPACE

This set of vehicle families provides a simple basis to classify any number of existing or new vehicles
according to mass and size (Figure 7).
Figure 7. Indicative classification of various vehicles into six vehicle families

Ipedestrians

IIrideablesIIIlight motorisedIVcar-likeV

truck-likeVI

tram-likeAs with other approaches, the Good Street framework seeks to address the balance between place-specificattributes of networks (i.e. the identity of the city, identity and function of the neighbourhood, alternative
uses of urban space, desired land use characteristics and density) and traffic-related functions (i.e. accessto activities, trip-making, desired transport network structure and decisions to mix or separate users). Thisapproach recognises that people both live and use spaces just as they use these spaces to travel.
To help achieve this balance, the Good Street framework proposes four archetypal traffic environmentscharacterised by the maximum legal operating speed for vehicles in each environment and primarily
designed for a normative vehicle family (Figure 8). In this framework, lighter and smaller vehicles than thenormative design family always have access. Vehicles larger and heavier than the normative family may be
admitted within each environment but only as guests and under certain conditions – the first of which isthat they respect the design speed and predominant traffic behaviour.

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Figure 8. The Good Street archetypal urban traffic environments,
corresponding normative vehicle family and access rules
Urban traffic
environment.

1

2

3

4

Maximum permissible
speed

Normative vehicle
family for design

Lighter vehicles (always
permitted)

Heavier vehicles(excluded or guest)

10 km/h

I

20 km/hII

I(III, IV, V, VI)

30 km/h

III

I, II

(IV, V, VI)

50 km/hIV

I, II, III(V, VI)(II, III, IV, V, VI)

pedestrians

rideables

light motorised

car-like

Source: Adapted from Immers et al. (2016; 2020).

The Good Street framework addresses multiple uses of the same street space by defining when and underwhat conditions vehicle families may operate in the same space or “domain”, and when they should be
separated. Generally, vehicle families that are no more than one category lighter than the normative
design family may be allowed (but not required) to share that domain or road space. Vehicle families thatare two or more categories lighter and smaller than the normative design family must be accommodated
in other domains either by separation in the same road space or by rerouting to other domains.
These principles and the Good Street framework served to inform the modelling work undertaken in thesecond part of this report.

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MODELLING MOBILITY AND SPACE CONSUMPTION

Modelling mobility and space consumption
To assess alternative space allocation options, the ITF developed a detailed simulation and optimisation
approach that could simultaneously test dynamic management of street space allocation, and thedeployment of new private ownership and shared mobility solutions.
The modelling for this study has two new features compared to previous ITF urban modelling work. The
first is that it incorporates a wider range of mobility options and services than previous work and addresses
these at a more detailed level within the urban core. The second is that it allows for limited, dynamic,
demand-responsive re-allocation of street space according to street type. A pre-existing shared simulation
model for the Greater Dublin Area (ITF, 2018a) was used as the basis for testing the measures and policies
discussed in this report. However, rather than depend on observed data, a plausible characterisation ofshared or free-floating transport supply, as well as detailed parking availability, was generated
synthetically. For this reason, the results of this report should not be used to assess the results or impacts
specifically in the case of Dublin.
The pre-existing simulation was adapted by adding multiple free-floating modes (including shared cars andmicromobility, i.e. e-kick scooters and free-floating shared bicycles), pedestrians, and private bicycles.
These modes are incorporated either as a principal travel mode or an access or egress mode (i.e. first andlast kilometre) for public transport. Furthermore, a new and more detailed routable street network was
created within the core urban area covered in the model to more finely capture detailed non-motorised
vehicle flows. This network was then consolidated with the pre-existing network outside the city core.
More details about the simulation architecture of the shared component and optimisation services
approach can be found in ITF (2018a). The present report focuses on capturing the impacts of adding more,largely free-floating, shared mobility services that are directly operated by customers than were
investigated in ITF (2018a). It also seeks to capture the effects of more dynamically managing street space
allocation and use. The simulation does this by running an optimisation process every 30 minutes. This
enables configuring streets that are theoretically adjusted to the observed or predicted demand.
The optimisation objective function minimises space consumption (both in its static and dynamic
components). The objective function includes: travel time by vehicle type (weighted by the flow andcorrected by a factor of two for pedestrians and one point five for bicycles so as to not overly represent
cars that may present higher travel time savings by circulating at higher speeds), parking capacity usage(i.e. estimated parking search time and unused spare capacity of reserved parking spaces) and traffic safety(i.e. number of potential conflicts between vehicle types and their difference in flee flow speeds). For this,all components of the function are converted into time.
Figure 9 presents the adjusted model framework. It has three key components: a lexicographic travel
demand framework, an infrastructure optimisation framework and a transport operation andmanagement framework. The model simulates how users make mode-choice decisions given available
options, the modal attributes that are expected given the operational layout and the expected travel time
given the street specifications in terms of capacity and speed for each vehicle type.

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MODELLING MOBILITY AND SPACE CONSUMPTION

The model is conceived as a sorting utility-based algorithm derived from the Outlook 2021 Global Urban
Model (ITF, 2021b) and constraints are set to trigger potential modal switches (i.e. the conditions underwhich one mode can be replaced by another). Specific modal availability and/or suitability depends on thetime of day and trip purpose. In practice, all travellers and vehicles operating in the city simultaneously
send and receive information to a centralised dispatch algorithm that optimises vehicle usage and plans
for subsequent periods. Finally, every 30 minutes an optimisation algorithm runs based on the registereddynamic and static (i.e. parking) demand on each street segment. This algorithm enables changes in thecapacity reserved for each vehicle type (moving or standing) to be captured, as well as the allowed freeflow speed given the combination of flows expected in the next 30 minutes.
The optimisation objective function minimises space consumption (both in its static and dynamic
components). The objective function includes: travel time by vehicle type (weighted by the flow andcorrected by a factor of two for pedestrians and one point five for bicycles so as to not overly represent
cars that may present higher travel time savings by circulating at higher speeds), parking capacity usage(i.e. estimated parking search time and unused spare capacity of reserved parking spaces) and traffic safety(i.e. number of potential conflicts between vehicle types and their difference in flee flow speeds). For this,all components of the function are converted into time.
Figure 9. Updated model framework to account for space allocation and use

The optimisation of each street segment accounts for 19 different road typologies. It uses a “greedy
optimisation” approach, where each link is optimised individually and co-ordinated with the neighbouring
segments. Each road type has a predefined set of compatible conversion options. Table 4 and Figure 10
present the different street typologies used, their nomenclature, and the conversion compatibilities
among them. There are different flow capacities and free-flow speeds for each vehicle type (depending onwhether the infrastructure is shared or not), and parking capacity is allocated for every linear 10-metre
street segment. The values are separated for motorised-based modes and vehicles (car/bus/motorcycle),pedestrians, and bicycle-based modes. In each street configuration solution, the infrastructure devoted toeach of these groups can be shared – which requires lower speeds (especially for the fastest modes) – orsegregated. In the following section, each transport mode considered is attributed to one of these groups.
Each street has an initial typology that is revised every 30 minutes during the simulation.

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More details about the simulation architecture of the shared component and optimisation services
approach can be found in Martinez and Viegas (2017) and ITF (2018b). The current report will focus on
discussing the modelling approach and the results of adding more shared mobility services that are directly
operated by customers and are usually free-floating, and the dynamic management of the street network
configuration.

Scenario definitions
Four different space allocation scenarios are assessed. They include variations in terms of the network(static or dynamic), the demand (static or dynamic) and the mode choice (existing or including new sharedmodes). This approach allows for the impacts of these different components to be disentangled.
The scenarios are defined as follows:
1. Baseline scenario: This serves as the departure point. The network remains static (i.e. without
temporal configuration and street space allocation changes), as does the demand. No additionalmodes are included in the supply.
2. Full implementation scenario: Incorporates a dynamic network, dynamic demand, and theimplementation of new transport alternatives (e.g. free-floating micromobility, shared modes). It
is a combination of scenarios 3 and 4.
3. Dynamic network scenario: Incorporates a dynamic network, but the transport supply and mobility
alternatives remain unchanged from the baseline.
4. New mobility paradigm scenario: Incorporates new modal alternatives, including a dynamic
demand where users can switch between modes, depending on the current supply at any
moment in time. However, the road network remains unchanged and static.

Modal alternatives
As noted earlier, the simulation covers 19 different travel modes. These were categorised according toseveral variables and are outlined in Table 3 below. To help define the rules for re-allocating street accessand use in the model, these modes were mapped to the Good Street vehicle families described earlier(Figure 6). The modes include active, electric and internal combustion (IC) alternatives. In addition towalking, a range of low-capacity (i.e. cars, motorbikes) and micromobility (i.e. bikes, e-bikes, e-scooters)
alternatives are included in their private and shared variants. Mass public transport (i.e. bus, car + public
transport [CPT], light rail, heavy rail) is also considered, as well as on-demand services of different capacities(i.e. taxis, on-demand buses designated here as TaxiBus, shared vehicle options, feeders). The 19 modes
were finally divided into five classes for modelling purposes:

28•

private non-motorised and micromobility•

shared non-motorised and micromobility•

private motorised transport•public transport•shared transport.

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The model is implemented for the whole of the urban region, but free-floating shared micromobility
modes are included only within the core, where the resolution of the street network for this purpose wasalso increased.
Table 3. Description of modal alternatives included in the simulation exercise
Mode inmodel

Description

Active or
motorised?

Private selfowned or
shared?

Good Street
vehicleSharing type family*

Modelling
analysis group

Walking

Walking as access or fullmode from origin todestination

ActivePrivateI

Private nonmotorised +micromobility

Owned
bicyclesPrivate non-electric bicycle as Active
access or full mode fromorigin to destination

PrivateII

Private nonmotorised +micromobility

Owned
e-bicycles

Private electric bicycle as fullmode from origin todestination

PrivateII

Private nonmotorised +micromobility

Owned
e-scooters

Private electric scooter as full Motorisedmode from origin todestination

PrivateII

Private nonmotorised +micromobility

SharedbicyclesFree-floating shared-bicycle
service

ActiveSharedFree-floatingor stationbased

II

Shared nonmotorised +micromobility

Sharede-bicycles

Free-floating electric sharedbicycle service

ActiveSharedFree-floatingor stationbased

II

Shared nonmotorised +micromobility

Sharede-scooters

Free-floating electric scooter
sharing service

Motorised

SharedFree-floatingor stationbased

II

Shared nonmotorised +micromobility

CPT

Car + public transport (e.g.
park and ride)

Motorised

Private/sharedIV/VI

Privatemotorisedtransport

Car (driver)Car driver from origin todestination

Motorised

PrivateIV

Privatemotorisedtransport

Car
passenger

Car passenger from origin todestination

Motorised

PrivateIV

Privatemotorisedtransport

Motorcycle

Motorcycle from origin todestination

Motorised

PrivateIII

Privatemotorisedtransport

Active

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Mode inmodel

Description

Active or
motorised?

Private selfowned or
shared?

Good Street
vehicleSharing type family*

Modelling
analysis group
PublicTransport

Bus

Bus as single-use or part ofmultimodal trip

Motorised

SharedMass Public
Transport

Rail

Rail as single-use or part ofmultimodal trip

Motorised

SharedMass Public
Transport

LRT

Light rail as single-use or part Motorisedof multimodal trip

SharedMass Public
Transport

VI

PublicTransport

Taxi

Taxi passenger from origin to Motorised
destination

SharedOn-demandIV

Sharedtransport

Carsharing

Free-floating carsharing
service

Motorised

SharedFree-floatingor stationbasedIV

Sharedtransport

Ridesourcing App-based ridesourcing
service with professional
driverMotorised

SharedOn-demandIV

Sharedtransport

Taxi-bus

App-based on-demand bus(route and schedule) fromorigin to destination

Motorised

SharedOn-demand

IV/V

Sharedtransport

FeederApp-based on-demand bus
Motorised(route and schedule) toaccess heavy public transport
station (rail or LRT) directly todestination

SharedOn-demand

IV/V/VI

Sharedtransport

Sharedmotorbike

Free-floating motorcycle
sharing service

SharedFree-floatingor stationbased

III

Sharedtransport

MotorisedV

PublicTransport* See Figure 6 for an explanation of the vehicle families.

Street space functions and configurations
The simulation model developed for this report adjusts the allocation of street space to expected demand
every 30 minutes. To enable this, different road classification categories were defined based on acharacterisation of the Dublin road network (OpenStreetMap, 2021). Rules relating to how and under whatcircumstances these road classes could switch were also built into the model. The linkage between roadclasses (and their use) and vehicle classes was roughly based on the street space classifications developed
for the Good Street framework as seen in Figure 6 (Immers et al., 2016; 2020).

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Table 4. Street space functions and configurations
Road
category

Pedestrian

Cycling

Speed(km/h)

Capacity Speed(person/h) (km/h)

Steps

3

500

Pedestrian

5

3 000

12

Cycleway

3

100

Footway

5

Path

Car
Capacity
Speed(vehicle/h) (km/h)

ParkingTraffic
segregation (%)

Capacity(vehicle/10 m)(vehicle/h)
0

100%

500

0

0%

27

2 000

0

0%

4 000

12

500

2

0%

5

2 000

27

800

20

400

1

10%

Track

5

2 000

27

500

20

250

0

0%

Service

5

1 000

12

500

30

600

3

25%

Residential

5

1 000

12

500

30

600

4

50%

Roads

5

1 000

12

500

30

600

3

75%

Tertiary
entry

5

1 000

12

500

30

600

2

50%

Unclassified

5

750

12

500

40

600

1

25%

Secondary
entry

5

1 000

12

500

40

800

2

80%

Tertiary

5

750

12

500

40

800

4

50%

Secondary

5

750

12

500

50

1 400

2

80%

Primary

5

500

12

200

50

1 800

0

100%

8

1

50

1 400

0

100%

50

2 200

0

100%

80

3 000

0

100%

90

4 400

0

100%

Trunk entry
Motorway
entry
Trunk
Motorway

8

1

Characteristic road performance values for the different road categories (i.e. capacity, speed and traffic
segregation or reserved right of way) are defined and used as attributes in the optimisation exercise.
Additionally, for each road category, the car-equivalent parking capacity was defined for every 10 metresof road length (e.g. a bus occupies three times more space than a standard car). The values for these

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categories can be found in Table 4. It is important to note that this table represents the speed and capacity
performance per mode if the street configuration is changed in the model. Thus, there are some vehicles
that can never occupy some types of street space in the model – e.g. a car will never operate on steps anda pedestrian or bicycle will never be present on a motorway. The table does indicate how infrastructure
built for one use may handle different vehicles under a temporarily changed configuration. For example, inthe model, a pedestrian zone that is temporarily redesignated to accept bicycle use could see up to
500 bicycles operating per hour at a maximum speed of 12 km/h. Similarly, a tertiary road that is
temporarily redesignated as a secondary road in the model will not see more than 800 vehicles per hour
operating at a maximum speed of 40 km/h (as opposed to a “native” secondary road that can handle
1 400 vehicles per hour at a maximum speed of 50 km/h).
These categories also act as constraints in the model in terms of the geometrical changes that can be
introduced to alter the original road space configuration. Each road category has a predefined set of otherpossible categories to which it can be converted based on the original geometry (Figure 10). For example,
a motorway cannot become a pedestrian area in the model.

30

40

50

Motorway

Trunk road

Primary road

Secondary road

Tertiary road

20

Residential street

Cycleway
Path

10

Road

Pedestrian
Footway

Figure 10. Dynamic reconfiguration of road space:
Possible in-model conversion pathways of road classifications

80+

Permissible top speed range
Note: the grey arrows represent one-way or uni-directional change of road typology, whereas the black arrows
represent two-way or bidirectional potential road typology changes. Grouped road typologies imply
interchangeability among them. Arrows to/from groups indicate potential changes of road typology to any roadtype in the group.

It is important to stress that the potential street-use conversion pathways discussed in this report aretheoretical in nature and serve to help understand what potential efficiencies could be achieved with more
dynamic adjustments of physical street space. In reality, such adjustments would have to be carefully
designed and implemented to avoid any negative safety outcome. This may limit the range of potential
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reconfigurations that this report investigates, even under the constraints imposed in the model. At aminimum, such dynamic re-allocation of space would require a very different road management paradigm
than exists today.

Assessment indicators of model outputs
To assess the results of the simulation exercise, two main types of indicators are used: space consumption
measures and accessibility-like measures. These are described in more detail below.
Space consumption indicators

The mobility patterns (pertaining to the number of trips, modes and their duration) resulting from thedifferent simulations were used to calculate space consumption based on the approach and equations
from (Héran and Ravalet, 2011) described earlier.
The space consumption measure incorporates four main components. The first two comprise (1) the static
space consumed when vehicles are not in use (e.g. parking) and (2) the dynamic space consumed by
vehicles while travelling. Additionally, when considering public transport or shared modes, the space usedby travellers (3) while waiting for the vehicles to arrive and (4) while travelling to and from public transport,
are also included. The space consumption indicator assesses two key aspects: (1) the total stock of spaceconsumed while travelling, and (2) the space efficiency per traveller and by passenger-km.
Accessibility-like indicators

Two different types of accessibility-like indicators were developed for the current study. The first is acumulative travel isochrone and the second is an indirect measure of spatial accessibility that accounts forthe propensity for each traveller to use a travel mode based on the accessibility benefits it confers (i.e.
availability, travel time/range and affordability).
Both indicators explore the accessibility provided by different transport modes based on mobility patterns
observed in the simulation exercises. Unlike more commonly used accessibility measures, they do notconsider the potential ability to reach specific opportunities (based on the temporal or distance-based
proximity, and network and transport services performance).
The decision to define the indicators in this way stems from the fact that the simulation framework usedin this study measures the effects of the dynamic activity of users, who have a predefined location andtime for the activities they want to perform throughout the day. A different activity-based and land-use
integrated transport (LUTI) model would be required for the more common approach to accessibility.
The first indicator is a cumulative travel isochrone. This is an aggregate indicator for the entire region whichmeasures the share of all the realised travel that could have been performed by each mode within
30 minutes. The indicator estimates the percentage of recorded trips that could have been made by aparticular mode. This calculation accounts for the availability of the mode at any given time and how thatmode may perform at that time given the current operational and network characteristics. It considers theorigin and destination locations, as well as the time of the day the trip is expected to take place. This allows
the model to assess the potential of each mode to be used as an option for the intended travel. The
performance of each modal option in terms of travel times and cost is also estimated to allow for a richer
comparison between the different modes. Additionally, an analysis by departure area is performed toanalyse the geographical distribution of the resulting access indicator.

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The second set of indicators explores the potential willingness of travellers to use each mode for theirtravel based on the attributes of distance and expected travel time. It is a variation of the travel decayfunctions, which are used in potential accessibility measures. To estimate the willingness to travel by aparticular mode, the statistical distribution of choices from the synthetic mobility dataset was used as areference for the potential interest. The result is a distance and time probability density function that
reduces the probability of interest in using a particular mode as the distance and/or travel duration
increases. For modes not normally used for very short trips, it also reduces the probability of using aparticular mode as travel time decreases.
Two variations of this indicator are calculated. The first considers only the ability and willingness to travel
between a particular origin and destination at a particular time of the day and using a particular mode,
given the spatial and temporal attributes of that trip choice. The second incorporates an additional travel
cost component that considers the cost of accessing the mode (if applicable), the cost of travel, and thecost of ownership (if applicable). More details on the cost component can be found in the following
section.
The mobility costs for each mode or potential mode are computed for each trip. The cost calculations
include the following components:•

Ownership cost is calculated when travellers use vehicles they have purchased or leased. It
includes the cost of vehicle acquisition, parking, maintenance and insurance. The parking cost
includes parking permits or the cost of home garages.

•

Vehicle acquisition costs were computed and derived from new vehicle sales, vehicle age andvehicle residual values. Parking costs were estimated based on a review of the current costsof parking permits and the acquisition of new off-street parking spaces in different parts ofthe modelled urban area. Insurance and maintenance costs were added as additional
percentages to the acquisition costs. These costs are annualised and then converted into an
average commuting day (220 days in the model).

•Cost of using alternatives was calculated for each mode on a per-trip, per-hour or perkilometre basis. These costs include vehicle energy consumption (petrol or electricity),
mobility service subscriptions, tickets, distance or time-based fees, minimum fares andparking costs (on- and off-street during the activity duration). Each mode has a specific farestructure and parking requirement.

These costs are attributed to the main mode of travel and to the access and egress segments of the trip,
if applicable (e.g. shared bicycle to reach a metro station).
This final set of indicators also provides an estimate of potential demand and thus the market each service
provider may tap into given current operating characteristics. Like the isochrone indicator, it can be
estimated as an aggregate indicator for the entire region or disaggregated by departing geographicallocation, showing the suitability of each mode to satisfy the local demand in each city area.

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New mobility and dynamic street space allocation:
The impact on space consumption
The modelling undertaken for this report has resulted in the following iterative outputs:•

The network-wide impacts of including new modes, with limited dynamic re-allocation of the
network.

•

The modal impacts of these changes and space consumption (i.e. total stock of space consumed
while travelling and space efficiency per traveller and by passenger-km travelled)•

Analysis of modal potential to better understand the likelihood that specific modes could serve
current mobility needs.

•

Environmental impacts of the applied scenarios, including the environmental performance of themodes in terms of emissions, and the environmental implications of the operational performanceof shared micromobility alternatives, including rebalancing impacts.

Overall, limited and dynamic re-allocation of street space and adding new free-floating and other mobility
service options (full implementation scenario) could potentially improve network performance byapproximately 12% (a weighted measure as described above that combines mobility, accessibility,
environment, space efficiency and safety). Enacting variable and demand-responsive street
reconfiguration alone, without changes to baseline modal options and mobility patterns (dynamic network
scenario) improves network performance by 9% and finally, the inclusion of new modes without theaccompanying dynamic changes to network (new mobility paradigm scenario) improves network
performance by 3%.
As highlighted in
Figure 11 and Figure 12, the temporal dynamics of these improvements vary throughout the day. The
results show that the reduction in car use and parking requirements are almost entirely absorbed by theneed for more infrastructure diversity and greater space for pedestrians, bicycles and scooters.
Furthermore, from a safety perspective, having more vehicles circulating at very different speeds
decreases the safety component of the performance function.
What emerges from the comparison of the various scenarios is that limited dynamic re-allocation of street
space has the potential to improve network performance, especially in conjunction with the introductionor accelerated uptake of non-individual car mobility options (including owned and shared bicycles andother forms of micromobility, shared rides and carsharing). Conversely, introducing these mobility options
without re-allocating street space (dynamically or statically) does not significantly improve network
performance in the scenarios modelled.

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However, the introduction of limited dynamic re-allocation of street space would also come withchallenges. First among these are how to address the mix of different road users safely and how to ensurehigh levels of predictability in the face of the dynamic re-allocation of space.
The former concern reinforces the need for speed management approaches consistent with the Safe
System approach (ITF, 2008) or, alternatively, where this is not possible, with hard separation which limits
the opportunity to re-assign the street space dynamically.
The second concern reinforces the need to explore new road use paradigms that could deliver elevatedand safe levels of predictability under changing road use conditions. Here too, default low and safe speeds
could go a long way to managing this dynamic use of space. Providing more efficient parking solutions
during low demand periods when vehicles are mostly idle is also an important consideration.

Figure 11. Street performance compared to baseline over 24 hours

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Figure 12. Street performance compared to baseline in the morning peak

Activity in the network and modal shift
The uptake of non-individual car-based modes and limited, demand-responsive, re-allocation of street
space has a significant impact on passenger-kilometres per mode (see Figure 14 and Figure 15). The useof shared vehicles and taxis (green in Figure 14 and Figure 15) more than doubles, with a 278% increase,and bicycle kilometres (light blue in Figure 14 and Figure 15) also see a significant increase of 114%.
In contrast, there is a slight decrease of 4% in pedestrian travel (grey in Figure 13 and Figure 15), although
the spatial distribution of this decrease is very uneven. In the centre, some pedestrian access trips to public
transport and short to medium-length travel by foot are replaced by micromobility or other forms ofshared travel. Walking increases in the suburbs with more people leaving their car at home and walking toaccess-shared modes. Travel by private car (dark blue in Figure 13, and Figure 15) decreases the most(-22% passenger-kilometres). Furthermore, as a result of these induced changes in mobility, there is a 9%
decrease in on-street parking capacity requirements.

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Figure 13. Passenger-kilometres by mode(A) Baseline scenario

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Figure 14. Changes in activity between baseline and full implementation scenarios by mode
Private cars and motorcycles

Shared vehicles and taxis

͟BicyclesScooters… Full implementation
Baseline

Pedestrians

Note: The Y-axis (passenger-kilometres) varies between sub-figures.

Figure 15 shows the changes in mode choice observed between the baseline and full implementation
scenarios. The simulation indicates that car drivers and passengers generally select other motorised
transport modes, in particular shared motorised travel, if they switch mode choice between the baseline
and full implementation scenarios.
In line with other recent studies on e-bike substitution effects (e.g. de Haas et al., 2021) the largest shareof new users of electric micromobility alternatives are those that would otherwise use non-electric
micromobility alternatives in the baseline scenario. This underscores the embeddedness of current travel
choices in a system of provision that largely incentivises single-use automobile travel (Mattioli et al., 2020).
Reversing this or deviating from that trend will require creating comfortable and compelling enough travel
experiences and incentives, including infrastructure provision, to induce larger numbers of private car
users to other, less impactful, modes.

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Figure 15. Modal switch (baseline vs. full implementation scenario)

Space consumption
All trips in the baseline scenario consumed 512 km2 for a modelled day. Implementing the limited anddemand-responsive re-allocation of street space and broadening the number of new mobility options fortravellers reduced the overall consumption of space to 429 km2 – a 19% reduction (see Figure 17). This isa very significant reduction considering that only 27% of car occupants switched to alternative modes(mostly to shared, motorised modes).
The private car consumes more space per day than any other mode as measured by total square kilometres
under the baseline scenario (see Figure 16). On average, private motorised transport has the highest spaceconsumption per trip (189.66 m2/trip) and per kilometre travelled (24.35 m2/pkm). These values areseveral orders of magnitudes larger than any other reported transport mode (see Table 5).

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Table 5. Space consumption by mode
Mode

Baseline scenario

Full implementation scenario

Dynamic
space

Staticspace

Spaceconsumed(m2/trip)Space
consumed(m2/pkm)Dynamic
space

Staticspace

Spaceconsumed(m2/trip)Space
consumed(m2/pkm)

Walking

100%

0%

0.41

0.37

100%

-

0.43

0.37

Cycle

14%

86%

14.73

4.35

14%

86%

14.63

4.28

Owned e-bikes

-

-

-

-

15%

85%

14.3

3.8

Owned e-scooters

-

-

-

-

25%

75%

7.72

2.05

Owned micromobility

78%

9%

1.81

0.73

81%

19%

3.3

1.11

Shared bike

-

-

-

-

77%

23%

1.62

0.84

Shared e-bike

-

-

-

-

83%

17%

2.36

0.72

Shared e-scooter

-

-

-

-

91%

9%

0.95

0.62

Shared micromobility

-

-

-

-

83%

17%

1.8

0.74

Bus

55%

45%

1.31

0.36

56%

44%

1.38

0.37

Rail

72%

28%

2.2

0.26

72%

28%

2.33

0.28

LRT

57%

42%

1.31

0.35

58%

42%

1.38

0.37

Public transport

61%

39%

1.62

0.32

61%

38%

1.7

0.34

Car driver

4%

96%

229.66

29.51

4%

96%

232.4629.74

Motorcycle

25%

75%

31.88

3.98

25%

74%

32.52

4.01

Car + public transport

35%

65%

25.4

3.02

50%

49%

14.2

1.65

Car passenger

100%

-

0.31

0.62

100%

-

0.31

0.62

Private motorised
transport

20%

80%

187.36

24.17

20%

80%

189.66

24.35

Carsharing

-

-

-

-

14%

86%

91.07

9.37

Ridesharing

-

-

-

-

72%

28%

33.63

5.67

Taxi bus

-

-

-

-

42%

58%

1.53

0.66

Feeder

-

-

-

-

29%

71%

1.56

1.03

Shared motorbike

-

-

-

-

87%

13%

5.38

1.13

Shared transport

-

-

0.22

0.04

53%

47%

20

3.26

The ratio between dynamic and static space usage for all considered modes can be found in Figure 17.
Importantly, 80% of the space consumed by private vehicles relates to parking. As a result, sharedmotorised transport performs much better, and has a much better ratio between dynamic and static space
usage consumption (53:47). Specifically, space consumption by shared motorised modes per trip and per
kilometre travelled is only 11% and 13% respectively of that of individual motorised transport.

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Figure 16. Total and share of space consumed by mode per day

Baseline scenario

Full implementation scenario

Total space consumed for all trips per day (km2)

512

429

-19% from baseline

Relative space consumed by mode

Private vehicle

42

Shared mobility+ Taxi

Pedestrian andowned micromobility

Shared micromobility

Public Transport

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Of note is that the space consumed by shared motorised transport remains significantly higher than thatof public transport and shared and private micromobility in terms of total space, space consumed per trip,
and space consumed per passenger-kilometre (see Figure 16 and Table 5). Among these three betterperforming alternatives, public transport performs particularly well on a per-kilometre basis because
public transport trips are more likely to be longer when compared to trips using micromobility alternatives.
Figure 17. Relative space consumption: Dynamic versus static space
Private vehicle (baseline)
Private vehicle (full implementation)
Shared mobility + Taxi (baseline)
Shared mobility + Taxi (full implementation)
Pedestrian + Owned micromobility (baseline)
Pedestrian + Owned micromobility (full implementation)
Public transport (baseline)
Public transport (full implementation)
Shared micromobility (full implementation)
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Dynamic Use

Static Use

Modal accessibility: What modes can be used to perform current
trips?
Indicator 1: Potential travel isochrone

As described in the methods section, this indicator is an aggregate isochrone for the entire region whichmeasures the share of all the realised travel that could have been performed with each mode in a 30minute period.
As summarised in Table 6, shared transport modes (which are not included in the baseline scenario) aretheoretically able to realise the largest share of trips (69%) in the full implementation scenario. This isfollowed by private motorised transport, which is still a viable option for 59% of the trips in this scenario.
However, this represents a 6% decrease from baseline scenario levels. On the other hand, the number oftrips that could be performed using non-motorised and micromobility alternatives increases by a thirdfrom 27% to 36%. Finally, as expected, there is no change in the trips which can be made using public
transport. This is because no increases in service or coverage were modelled.

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Table 6. Potential travel isochrone indicator:
Modal shift with Full implementation scenario
Group

Baseline scenario

Full implementation scenario

Urban core

Rest ofurban area

Overallurban
regionUrban core

Rest ofurban area

Overallurban
regionNon-motorised + micromobility

42%

26%

27%

46%

36%

41%

Private motorised transport

68%

58%

65%

63%

54%

59%

Public transport

13%

4%

8%

13%

4%

8%

Shared transport

-

-

-

75%

61%

69%

Indicator 2: Traveller modal choice propensity

Unlike the first indicator, the second set of accessibility indicators consider not only the characteristics ofthe mode itself, but the perception of the trip makers and their willingness to travel a particular distanceor time using a particular mode. This is measured according to a basic configuration relating only to themodal and trip characteristics (e.g. availability, speed, distance) and in a full configuration in which costs
are factored. Table 7 summarises these two versions of the traveller modal propensity indictor for boththe baseline and full implementation scenarios.
In contrast to the potential travel isochrones (Indicator 1) where it was found that 69% of current trips
could be potentially replaced by shared transport (in the full implementation scenario), the propensity to
use these modes for these trips is not nearly high enough. When considering only the modal andtrip characteristics in the basic traveller modal propensity indicator, the share of replaceable trips byshared modes is reduced to 46%. When the cost is also considered, this share is reduced to only 15% of
the total trips.
Non-motorised and micromobility trips are also affected. Using Indicator 1, 41% of trips could be
potentially made using non-motorised and micromobility modes in the full implementation scenario.
However the propensity of users to make these trips using these modes is such that it becomes a viable
alternative for only 18% of the total trips when cost is not considered, and 22% of trips once cost isaccounted for.
Table 7. Traveller modal propensity indicator: Willingness to use different modes
Baseline scenario

Full implementation scenario

Mode

Basic travel
propensity
indicator

Full travel propensity
index

Basic travel
propensity
indicator

Full travel
propensity index

Non-motorised + micromobility

13%

23%

18%

22%

Private motorised transport

48%

11%

46%

11%

Public transport

12%

9%

11%

7%

Shared transport

-

-

46%

15%

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NEW MOBILITY AND DYNAMIC STREET SPACE ALLOCATION: THE IMPACT ON SPACE CONSUMPTIONIn terms of cost, under the full implementation scenario, among the micromobility alternatives, sharedbikes (EUR 0.05 per km) and shared e-bikes (EUR 0.11 per km) provide the most value for the user, at evenlower costs than the per kilometre cost for an owned bicycle (EUR 0.18 per km). This reveals the benefitsof shared assets in reducing ownership costs, which in individual transport vehicles with little to no energy(e.g. fuel) costs, are the largest cost component.
At the other extreme, this report finds that the cost of shared e-scooters is particularly high. It is morethan twice the cost per km (EUR 0.86 per km) than any other micromobility alternative (owned or shared).
This cost brings it closer to the costs of motorised alternatives such as ridesharing (EUR 0.92 per km)
private motorcycles (EUR 0.95 per km) and carsharing (EUR 1.13 per km).
Additionally, as shown in Table 8, impacts vary between the centre and the periphery. In the urban core,
as expected, more residents are willing to travel by non-car modes as compared to inhabitants of the restof the wider urban area. This is a result of more travel options being available in the core as well as thedensity of potential travel destinations resulting in shorter overall trips that are amenable to non-car
modes. Even in the wider urban area, factoring in the cost of ownership and use of cars decreases thepropensity for travellers to choose this mode even if it could meet their travel needs. Furthermore,examining the differences between the baseline and full implementation scenarios shows that sharedtransport (which includes a variety of different services including taxi buses and ridesourcing) can be an
attractive and cost-effective option for many users in both the core and extended urban area, although
the attractiveness of these modes remains highest in the urban core.
Table 8. Traveller modal propensity indicator: Willingness to use different modes (by location)
Basic travel propensity
indicator

Full travel propensity index

Urban
core

Rest of urban
area

Urban core

Rest of urban area

Private non-motorised + micromobility

0.15

0.09

0.25

0.16

Private motorised transport

0.66

0.34

0.19

0.07

Public transport

0.23

0.06

0.17

0.04

Shared transport

-

-

-

-

Private non-motorised + micromobility

0.26

0.16

0.29

0.16

Private motorised transport

0.64

0.32

0.18

0.07

Public transport

0.21

0.05

0.14

0.03

Shared transport

0.63

0.32

0.19

0.11

Group
Baseline scenario

Full implementation scenario

Environmental considerations
Changes in mode choice and travel activity will have a knock-on effect on environmental impacts – which
are important to consider when evaluating different scenarios. These impacts are linked to the operational
performance of new shared modes including the positioning and relocation dynamics of sharedmicromobility modes which are a non-negligible source of generated travel (ITF, 2020a).
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NEW MOBILITY AND DYNAMIC STREET SPACE ALLOCATION: THE IMPACT ON SPACE CONSUMPTION

Operational performance of shared mobility and micromobility

Table 9 presents an overview of the operational performance of shared mobility and micromobility modes
included in the simulations. Shared taxis have a very high share of empty kilometres (30.4%). This is incontrast to carsharing (10.9%) and taxi-buses (8.3%) which show a very different profile. It should be noted,
however, that the in-model specification of taxi-bus services is expressly designed to minimise low
efficiency (from a load factor and empty travel perspective) operation. When certain efficiency thresholds
cannot be met, the model re-allocates taxi-bus trips to shared taxis. This reassignment of taxi-bus trips is
captured in the percentage of reassigned trips (54.6% for taxi-bus trips while shared taxis do not lose any
trips) and in the load factors (where the taxi bus has the highest load factor among all shared alternatives
at 9.14).
Additionally, the simulation tracks when a traveller’s first choice for a travel mode cannot be fulfilled due
to lack of availability or other constraints and re-assigns these trips to other modes. – e.g. ‘frustrated’
modal choices. For example, in the simulation, only 30 out of 100 travellers who wanted to ride a sharedelectric bike were able to do so. Sixty-eight of the remaining travellers had to use a conventional sharedbike and two used another mode completely.
Table 9. Load factor by mode and use rate in full implementation scenario
Mode

Fleet/1 000
inhabitants

Trips/vehicle
day

Km/vehicle
day

Load factor% share of
empty km

Reassigned or
frustrated
trips

Carsharing

4.04

7.73

73.41

0.92

10.9%

1.9%

Shared taxi

16.22

24.75

203.52

1.16

30.4%

0.0%

Taxi-bus

3.07

4.79

240.16

9.14

8.3%

54.6%*

Shared bicycles

31.64

8.14

9.67

0.89

12.4%

13.5%

Sharedscooters

17.06

4.99

10.00

0.86

16.3%

4.6%

Sharedmotorcycles

4.04

1.44

6.75

0.75

47.1%

1.6%

*Trips switched to taxi to preserve higher load factors for the taxi-bus category.

Fleet relocation and rebalancing dynamics

The operation of shared micromobility modes requires fleet rebalancing to ensure vehicles are where they
are most needed and will be most frequently used. Fleet rebalancing creates additional environmental(and monetary) costs. To this end, the movements per hour for each “station” (i.e. area of the city asmodes are free-floating) were analysed in the simulations with 72% of rebalancing movements happening
after the morning peak hours and before the afternoon peak, compensating for commuting flows to andfrom the suburbs to the city centre.
Bikesharing has the greatest number of trips per hour per station (area) of 4.7 while e-push scooter sharing
averaged 3.5 moves per hour per station (area). This performance is more than two times better thancarsharing (1.4) and motorbike sharing (0.1). The number of trips observed has a natural rebalancing effecton the demand, avoiding artificial vehicle repositioning.

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The temporal dynamics of rebalancing operations are illustrated by taking a closer look at a representative
station (area) in the centre of the city (Figure 18). While there is a natural rebalancing throughout most of
the day, there are significant changes between 08:00 and 08:30 and again between 17:30 and 18:00 for
bikesharing, implying the need for operational rebalancing. Shared e-push scooters follow a similar pattern
morning and evening in the urban core. Stations in the periphery, on the other hand, display opposite
patterns to those experienced in the core (Figure 19). There is significant movement earlier in the morning(06:00), highlighting an earlier commuting wave not present in the centre.
Figure 18. Fleet relocation dynamics in a central urban area

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NEW MOBILITY AND DYNAMIC STREET SPACE ALLOCATION: THE IMPACT ON SPACE CONSUMPTION

Figure 19. Fleet relocation dynamics in an outer urban area

Environmental performance

The full implementation scenario delivers modest but significant improvements in terms of lifecycle CO2,
NOx, SO4 and fine particulate emissions, despite increases in overall vehicle and passenger-kilometrestravelled (+4% and +7%, respectively). Tank-to-wheel and well-to-tank CO2 emissions are reduced by 6%
and 5%, while particulate matter emissions drop by 4%.
Given the fact that the increases in travelled kilometres, as shown in Table 10, are entirely due to the newly
implemented shared transport and micromobility alternatives, the full implementation scenario wasrecalculated with one additional condition: the electrification of the shared vehicle stocks (i.e. electric cars
for ridesharing and taxi-buses). The full electrification of these fleets leads to further significant emissions
reductions – up to -23% each for all measured pollutants. This highlights the importance of not only
updating and improving the alternatives available for the users but also strengthening the electrificationof all shared mobility vehicle stocks.

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Table 10. Environmental impacts and travel volumes by mode

electric ridesharing andtaxi-bus

Full implementation scenario+
Full implementation scenario

Baseline scenario

Scenario

Variable

Transport mode
Publictransport

Privatetransport

Sharedtransport

Micromobility

Total

Variationfrombaseline

CO2 TTW

250.6

2280.70.0

0.0

2531.3-CO2 WTT

144.7

497.0

0.0

0.0

641.6

-

NOx

0.7

4.1

0.0

0.0

4.8

-

SO4

0.0

0.0

0.0

0.0

0.0

-

PM25

0.0

0.1

0.0

0.0

0.1

-

PKM

6.5

17.7

0.0

0.0

24.2

-

VKM

0.3

14.8

0.0

0.0

15.2

-

CO2 TTW

200.6

1754.5428.4

1.6

2385.1-6%

CO2 WTT

124.6

382.3

93.3

6.9

607.1

-5%

NOx

0.6

3.2

0.9

0.0

4.7

-4%

SO4

0.0

0.0

0.0

0.0

0.0

-1%

PM25

0.0

0.1

0.0

0.0

0.1

-4%

PKM

5.4

13.6

5.7

1.1

25.8

7%

VKM

0.3

11.4

2.9

1.1

15.7

4%

CO2 TTW

200.6

1754.50.0

0.0

1955.1-23%

CO2 WTT

124.6

382.3

163.3

5.2

675.5

5%

NOx

0.6

3.2

0.0

0.0

3.7

-23%

SO4

0.0

0.0

0.0

0.0

0.0

-23%

PM25

0.0

0.1

0.0

0.0

0.1

-23%

PKM

5.4

13.6

5.7

1.1

25.8

7%

VKM

0.3

11.4

2.9

1.1

15.7

4%

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CONCLUSIONS

Conclusions
This report assesses street space allocation, accounting for new mobility alternatives. It explores street
configurations, space allocation and the potential of dynamically managing the use of this space to deliver
more just outcomes. It considers modal shifts, environmental impacts, mobility and accessibility impacts,
and safety considerations. It builds upon previous work in earlier CPB reports on shared mobility and the
ITF case-specific study for the region of Dublin, Ireland (ITF, 2018a).
Street space is a scarce commodity in dense urban settings and serves a broad range of purposes. Its
allocation is inherently contentious and has significant impacts on the population. The allocation of thiscontested space constrains potential accessibility and limits how citizens can effectively move.
Traditionally, the design of public spaces has too often favoured highly space-consuming transport modes,
such as private automobiles, even though the space consumed by a driver in a car by kilometre is several
orders of magnitude larger than by any other mode.
This tension around space allocation and usage is further reinforced by the rise of new motorised sharedmodes and private and shared micromobility alternatives. To address this, the methodology was expanded
to include a wider range of emerging urban mobility modes which required more detailed modelling. These
modes include other private mobility options for door-to-door travel, and shared vehicles, either docked
or free-floating, used for door-to-door travel or as part of a longer trip chain in combination with public
transport. Furthermore, the urban infrastructure reserved for transport use, which is key to ensuring wellfunctioning cities, is evaluated and optimised.
Attention must be paid to the distinction between the use of street space versus the allocation of street
space to better understand the tension inherent to space allocation. The actual use or consumption ofstreet space is a representation of the demand for the mobility-related use of that space. This use/demandfeature is variable by mode, time of day and zone. It equates to the physical space that is actuallyconsumed as people and vehicles move. The allocation of space is a representation of the supply of urban
road space made available for people and vehicles to move, operate and park.
The model focuses on guaranteeing equitable, accessible and efficient mobility for all users. It puts people
first, framing the issue around how this space serves the population, and not on how vehicles occupy the
space. To this end, it is important to consider how the mobility needs of the population can be met, andthe potential accessibility provided by the system. This is very much related to the option values concept
which highlights that people value not just the actual use they make of the transport system (mobility) but
the availability of alternatives. Three key considerations are included: the trips and trip purposes that canbe effectively served by different modes, the space needed to deliver that mobility and the environmental
impacts of moving using a particular mode.
Omitting or improperly accounting for space in transport appraisals can have significant negative
outcomes. Space allocation and consumption is rarely considered when discussing transport, despite being
so central to the amount of accessibility provided by a system. There is an underlying trade-off between
the dynamic consumption of space and vehicle speed. Vehicles moving at greater speeds increase theaccessibility provided by a mode but come with an associated increase in space usage. However, focusing
only on vehicle throughput is flawed because it only addresses the dynamic component of mobility spaceuse, while ignoring the static space consumed as vehicles idle or park. This has resulted in the outsizedconcession of space to automobile mobility, at the expense of all other road users, often without
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CONCLUSIONS

considering the significant amount of public space needed for these vehicles to stand idle for more than
90% of the time or the impacts to the safety or wellbeing of citizens.
To ensure more just outcomes, appropriate space consumption indicators need to be incorporated into
policy and appraisal assessments. These metrics need to incorporate the temporal component of spaceconsumption. They should include the dynamic consumption of street space as vehicles move and thestatic consumption of public space when vehicles are parked or idle. These two components can be
captured in a composite indicator – m2.h – which, when calculated per traveller, gives a good basis onwhich to discuss the relative space-consumption impacts of different travel modes and trips.
Street space allocation tends to be static and quasi-permanent despite the temporal variation in the spatial
requirements of different modes. By better fitting infrastructure to the needs of the different modes atdifferent points of the day, it is possible to better address the needs of the population. The simulation
results indicate that there are added benefits to having dynamic and demand-responsive re-allocation ofstreet space. Importantly, this re-allocation can help compensate for the additional dynamic spaceconsumption needs of slower vehicles, preserving the levels of efficiency and enhancing the accessibility
provided by these modes while promoting better equity and environmental performance.
Dynamic re-allocation of space alone would be associated with a 9% improvement in network performance
but would be further enhanced by the inclusion of new mobility alternatives resulting in an improvementof approximately 12%. More specifically, the introduction of more space-efficient modes and the demandresponsive re-allocation of space leads to a 19% reduction in the space consumed to perform allmodelled trips.
These benefits are not evenly distributed. They concentrate in the urban core where a greater number ofmodes are viable alternatives for travel. In the urban core, 46% of the simulated trips could be performedby active and micromobility alternatives and 75% by shared modes if taking account of theoretical doorto-door travel time alone. In the rest of the urban area, these values are 36% and 61% respectively. Whenconsidering the potential propensity to travel by a particular mode, and in particular accounting for the
associated costs, the potential willingness to travel using different modes also shows significant differences
between the core and the periphery. All modes are much more attractive in the urban core than in therest of the urban area. However, in the urban core, the population is more likely to travel by shared modes
and non-motorised micromobility alternatives than by private car, and public transport also remains a
viable option.
The combination of new mobility alternatives and limited dynamic re-allocation of space is also associatedwith significant environmental gains. Despite increasing the number of passenger-kilometres, tank-towheel and well-to-tank CO2 emissions are reduced by 6% and 5% respectively, while particulate matteremissions drop by 4%. These improvements could be even more significant, reaching 23% if the sharedvehicle stocks were electrified.
Finally, the allocation of space should always consider the safety of all. Default low speeds for all modes
would allow the system to meet the mobility needs of the population more effectively, increasing
accessibility while providing a diverse set of safe and effective transport alternatives. More modes
travelling at different speeds and sharing the same space increases the likelihood of conflicts and crashes.
Dynamically changing the use of the space makes the need for safety even more salient. By relying on safesystem principles for any dynamic re-allocation of space and minimising the differences in speeds at
which vehicles sharing the same space can travel, the transport system can become safer and moreaccessible to all.

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NOTES

Notes
1 A Safe System recognises that humans will make mistakes, and that the human body has a limit to which it can absorb crash forces without
suffering injury. It posits that safety is a shared responsibility of all actors in a traffic system, not only that of a road user. Thus, all elements of theroad traffic system should come together in an integrate safety chain in which the elements will combine to prevent a crash, or at least prevent
serious injury, even if one or more elements fail (ITF, 2016b).

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STREETS THAT FIT: RE-ALLOCATING SPACE FOR BETTER CITIES © OECD/ITF 2022

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Streets That Fit
Re-allocating Space for Better Cities

International Transport Forum
2 rue André Pascal
F-75775 Paris Cedex 16+33 (0)1 73 31 25 00
contact@itf-oecd.org
www.itf-oecd.org

Published: 02/2022 | Photo credit: Gwenn Dubourthoumieu

Street space in cities is a rare resource. Much of it is currently
allocated to highly space-consuming transport modes without taking
into account that demands for that space vary over time. This report
looks at how street space has typically been allocated in the past,
examines the rationale for street space allocation and describes how
to measure space consumption for mobility purposes. The study also
explores by way of a simulation how new mobility services and travelmodes interact when a limited, dynamic and demand-responsive
re-allocation of street space is introduced in a mid-sized city.