Measuring New Mobility
Definitions, Indicators, Data Collection

Measuring New Mobility

Definitions, Indicators, Data Collection

The International Transport ForumThe International Transport Forum is an intergovernmental organisation with 64 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, Cambodia, 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, Türkiye, Ukraine, the United Arab Emirates,
the United Kingdom, the United States and Uzbekistan.

About the ITF Mobility Innovation Hub
ITF’s Mobility Innovation Hub helps governments to develop innovative policies for better transport in theface of uncertainty and rapid change. It scans for transport-relevant developments at the horizon of publicauthority foresight, assesses their potential impacts and highlights best practices for deploying effective
policies.

Case-Specific Policy Analysis Reports
The ITF’s Case-Specific Policy Analysis series presents topical studies on specific issues carried out by the
ITF in agreement with local institutions. Any findings, interpretations and conclusions expressed herein arethose of the authors and do not necessarily reflect the views of the International Transport Forum or theOECD. Neither the OECD, ITF nor the authors guarantee the accuracy of any data or other information
contained in this publication and accept no responsibility whatsoever for any consequence of their use.
This work is published under the responsibility of the Secretary-General of the ITF. This document, as wellas any data and map included herein, are without prejudice to the status of or sovereignty over any
territory, to the delimitation of international frontiers and boundaries and to the name of any territory,
city or area.
Cite this work as: ITF (2023), “Measuring New Mobility: Definitions, Indicators, Data Collection”,
International Transport Forum Policy Papers, No. 114, OECD Publishing, Paris.

Acknowledgements
This report was written by Nicholas Caros and Rachele Poggi, with contributions, editing and graphics
support by Philippe Crist, all of the International Transport Forum (ITF). Maria Santos Alfagame (ITF)
contributed to the initial phase of the project.
The authors thank Guineng Chen (ITF) and Elisabeth Windisch (ITF) for their comments on the various
drafts of this report and Hilary Gaboriau (ITF) for editorial support. Thanks also go to Diego Canales(Populus), Adama Cohen (University of California, Berkeley) and Tu-Tho Thai (MobilityData) for their reviewof the report, and to the participants in the ITF New Mobility survey and the two New Mobility
Performance Evaluation workshops held on 25 and 27 October 2022 for their valued input.
The project was co-ordinated by Rachele Poggi of the ITF.
The work for this report was carried out in the context of the ITF Mobility Innovation Hub, funded by the
Korean Ministry of Land, Infrastructure and Transport.

Foreword

Innovative mobility services and vehicles are quickly changing the way people travel. Collectively, they
have become known as “New Mobility”. But what characterises the phenomena to which this term isapplied? And how much do they influence the daily lives of citizens? Transport policy makers around theworld are grappling with these questions.
Good definitions are essential to understand, monitor and regulate New Mobility for the benefit of all. This
report proposes a classification system for New Mobility services and vehicles that can be universally
applied. It suggests a list of policy-relevant performance indicators that will allow comparisons in time andacross different jurisdictions or countries.
They enable a common understanding and compatible categorisations that will help public authorities andprivate-sector companies, which provide most of the New Mobility services, to work together. Reportingof compatible data can help ensure optimal service and also light regulation.
This report is the first publication by the International Transport Forum’s Mobility Innovation Hub.
The Hub was created with generous funding from Korea’s Ministry of Land, Infrastructure andTransport to help governments respond effectively to innovations in the mobility sector anddevelop innovative approaches to transport policy-making in the face of rapid change. My personalgratitude and appreciation go to Minister Won Hee-ryong for his unwavering support and his passion for
better mobility.
I am convinced that this report can take the development of New Mobility to a new level and that
future outputs of the ITF Mobility Innovation Hub will bring real improvements to the way weconceive and execute policies for better transport.
Young Tae Kim
Secretary-General
International Transport ForumTABLE OF CONTENTS

Table of contents
Abbreviations and acronyms .......................................................................................................... 7
Executive summary ........................................................................................................................ 8
What is New Mobility? ................................................................................................................. 11
Mapping New Mobility at the local, national and international levels .......................................... 11
Guidelines for classifying New Mobility ......................................................................................... 12
A taxonomy for New Mobility services .......................................................................................... 14
A taxonomy for New Mobility vehicles .......................................................................................... 19
Matching service categories and vehicle types ............................................................................. 23
Developing New Mobility performance indicators ........................................................................ 24
Why measure New Mobility performance?................................................................................... 24
Potential performance indicators by category .............................................................................. 27
Potential performance management challenges........................................................................... 38
Collecting data for New Mobility performance indicators ............................................................. 40
Approaches to reporting New Mobility data ................................................................................. 40
Data reporting: New Mobility data formats................................................................................... 42
Public-private agreements for reporting New Mobility data ......................................................... 45
References................................................................................................................................... 48

FiguresFigure 1. Mobility services spectrum ............................................................................................ 15
Figure 2. New Mobility services and the vehicle types that perform them ..................................... 23
Figure 3. Portland’s multi-modal New Mobility performance indicator.......................................... 25
Figure 4. US Federal Transit Administration’s tiered mobility performance metrics framework andpolicy objectives .......................................................................................................................... 27

TablesTable 1. A taxonomy of the most common New Mobility vehicle types ......................................... 19
Table 2. New Mobility performance indicators in five policy areas ................................................ 28
MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

5

TABLE OF CONTENTS

Table 3. Sustainability indicators for New Mobility services........................................................... 29
Table 4. Safety indicators for New Mobility services ..................................................................... 32
Table 5. Utilisation indicators for New Mobility services ............................................................... 33
Table 6. Accessibility indicators for New Mobility services ............................................................ 35
Table 7. Equity indicators for New Mobility services ..................................................................... 37

Boxes
Box 1. Privacy-preserving mechanisms for data reporting ............................................................. 42
Box 2. Mobility Data Specification application programming interfaces......................................... 45
Box 3. Data acquisition methods .................................................................................................. 46

6

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

ABBREVIATIONS AND ACRONYMS

Abbreviations and acronyms
AAM

Advanced air mobility

API

Application programming interface

CEN

European Committee for Standardisation

CO2

Carbon dioxide

DRT

Demand-responsive transit

EDA

Economically disconnected areas

GBFS

General Bikeshare Feed Specification

GPS

Global positioning system

ICCTInternational Council on Clean Transportation

ICE

Internal combustion engine

MaaS

Mobility as a Service

MDS

Mobility Data Specification

MPM

Mobility performance metrics

NeTEx

Network and Timetable Exchange

NUMO

New Urban Mobility Alliance

OSLO

Open Standards for Linked Organisations

ovkm

Operational vehicle-kilometres

pkm

Passenger-kilometre

PPM

Privacy-preserving mechanism

pvkm

Passenger-related vehicle-kilometres

SAV

Shared autonomous vehicles

SUMP

Sustainable urban mobility plan

SUVSport utility vehicle

TNC

Transportation network companies

vkm

Vehicle-kilometre

ZEV

Zero-emission vehicle

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

7

EXECUTIVE SUMMARY

Executive summary
What we did
This report proposes a comprehensive classification of “New Mobility” services and vehicles that arechanging the way people travel. It also identifies performance indicators to help set the right policies as
New Mobility evolves. It sets out a framework to systematically collect and compare New Mobility-related
data. Finally, the report examines how governments and private stakeholders can collaborate to improvetheir understanding of New Mobility and determine if and where policy interventions are needed.
Around 150 policy makers, New Mobility operators and transport professionals provided insights into thisreport through a survey for this study. A workshop with 15 experts consolidated the survey’s findings andestablished the list of New Mobility performance indicators presented in this report. The report alsoheavily draws on previous ITF work, notably Safe Micromobility (ITF, 2020a), and available literature.

What we found“New Mobility” is defined here as intraurban passenger mobility services and vehicles enabled by digitaltechnology. The spectrum of these services is widening, and the pace has accelerated since 2020 and in
the wake of the Covid 19 pandemic. New Mobility services provide value to many citizens by increasing
travellers’ options and providing affordable and convenient alternatives to traditional transport modes.
However, they are also disruptive and create new challenges, such as creating complex regulations for
public authorities and the need to balance their benefits with potentially negative impacts.
To ensure New Mobility services ultimately serve the public good, policy makers must gain the best
possible understanding of emerging trends in urban mobility. This, in turn, requires evidence based on
accurate data and meaningful indicators.
A first and essential step to obtaining relevant data is clearly defining and categorising the elements that
constitute New Mobility.
This report proposes a classification system for both New Mobility services and New Mobility vehicles. The
categories were chosen to also capture other mobility options, such as public transport or populartransport, thereby allowing comparisons and benchmarking.
New Mobility services are classified by the type of product or service delivered. The two major categories
are ride services and fleet sharing.
New Mobility vehicles are grouped into three categories: micromobility, powered light mobility, and caror van-like vehicles.
Setting policies and regulations for New Mobility in ways that support desired outcomes requires
information on performance and hence tools to measure that performance. This report presents relevant
indicators for evaluating how New Mobility contributes to policy objectives. It proposes standard

8

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

EXECUTIVE SUMMARY

calculation methodologies, data requirements and effective reporting frequencies for performanceindicators in five policy areas, namely sustainability, safety, utilisation, accessibility and equity.
Not least, it requires a well-designed data reporting framework to collect the relevant information. Mostof the data are held by the service providers. Public authorities need to find an effective way for service
providers to report it. They can either request raw data and subsequently decide which indicators tomeasure based on the data received. Alternatively, they can articulate policy objectives, define indicators
to monitor them, and then request only the necessary data from the operator. Finally, public authorities
can also task third-parties with collecting and analysing data. With all approaches, concerns about privacy
and commercial sensitivity need to be considered when framing data reporting.
New Mobility service providers operate in different cities, regions and countries and must report data to
several public authorities. Likewise, public authorities receive data from several service providers. Seeking
convergence towards a common terminology, compatible data structures and standardised syntaxes istherefore desirable for the sake of efficiency.
Different frameworks exist for data reporting by service providers to public authorities. Data reporting canbe compulsory or the condition to obtain an operating permit. Service providers may also report somedata voluntarily to obtain mutually beneficial outcomes, or offer to sell data if prevailing data governanceregulations allow this. Public authorities must build on the existing options to choose indicators and datareporting frameworks that allow them to monitor New Mobility and other incumbent services and ensurethat these support public policy outcomes.

What we recommend
Apply a comprehensive classification of New Mobility services and vehicles
When looking at the type of product or service delivered, New Mobility services should be organised
around two service types: fleet sharing and ride services. Public authorities should also categorise New
Mobility vehicles using three top-level categories (micromobility, powered light mobility, and car- or vanlike vehicles). If these classifications are not applicable, public authorities could create an alternative one,taking into account essential characteristics. A classification system should provide functional definitions,avoid ambiguous terms and create a logical and consistent structure for comparing and grouping different
New Mobility services according to features of interest. It should also be general enough to capture
important differences between categories without creating too many minor categories that cause theclassification system to be overly complex. It should be flexible enough to incorporate all existing New
Mobility services and unknown future New Mobility services. Finally, it should not bias analysis, policy orcompetition in favour of any particular actor, business model or technology.
Identify relevant performance indicators for New Mobility services and use them to set and monitor policy
To be effective tools to set and monitor New Mobility services, indicators should be relevant to policy
goals, tied to a specific regulatory or policy action, easily understood and communicated, and consistentacross services and modes whenever possible. They also need to be based on continuously available data
to enable the calculation of time series. Common indicators used by as many actors as possible have great
benefits and establishing them should be a priority for governments and private sector. New Mobility
operators would no longer need to create custom performance reporting programmes. Governments
could easily compare how New Mobility performs across cities. Finally, standard indicators would enableinternational comparisons and enable lessons-learned from other countries.

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

9

EXECUTIVE SUMMARY

Adopt a consistent reporting framework for data on New Mobility services
Public authorities should apply a consistent approach for data reporting to all New Mobility service
providers in a jurisdiction. Wherever possible, a unified approach should be implemented acrossjurisdictions. This will make it easier for service providers to comply with the requirements in different
cities or regions. Conversely, cities and regions will be able to use data received from different operators
more efficiently. Convergence towards compatible data structures and syntaxes should thus be a priority
for both governments and the private sector.

10

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

WHAT IS NEW MOBILITY?

What is New Mobility?
“New Mobility” can be defined as the range of emerging services and vehicles where new technologies
intersect with novel and existing business models and transform people’s ways of moving, sharing andusing transport (Cunningham and Carlsson, 2019; Slowik and Kamakaté, 2017). While some New Mobility
services have been around for more than a decade, they are still relatively new compared to other mobility
services. They are generally innovative in nature, though the same can be said for many public transport
and informal transport or “popular” transport services (Global Network for Popular Transport, 2023).
In this report, New Mobility refers to any intraurban passenger mobility services and vehicles enabled bydigital technology. The spectrum of services in New Mobility is widening, with the pace of change muchaccelerated since the end of the 2010s and in the wake of the Covid-19 pandemic. These new services
provide value to many but are equally disruptive. Therefore, policy makers must stay ahead of emerging
trends to support the public good proactively.

Mapping New Mobility at the local, national and international levels
Several research institutions, non-profits, corporations and multi-actor initiatives have sought to explore
the emerging New Mobility ecosystem in the last decade, highlighting the relevance of New Mobility tounderstanding today’s transport sector. These initiatives seek to characterise the New Mobility landscapeand, in many cases, monitor its development.
High-level New Mobility mapping exercises include SAE International’s Taxonomy of On-Demand andShared Mobility: Ground, Aviation and Marine – JA3163 (SAE, 2021), SAE International’s Taxonomy andDefinitions for Terms Related to Shared Mobility and Enabling Technologies – J3163 (SAE, 2018), theInternational Council on Clean Transportation’s (ICCT) New Mobility: Today’s Technology and Policy
Landscape (Slowik and Kamakaté, 2017), UC Berkeley Institute of Transportation Studies’ Shared Mobility
Policy Playbook (Shaheen et al., 2019), the EU Share North project’s Shared Mobility Rocks: A Planners
Guide to the Shared Mobility Galaxy (SHARE-North, 2021), which focuses on helping cities and urban
regions understand and regulate new forms of mobility services. The media platform Micromobility
Industries maps the state of the micromobility industry covering service providers, original equipment
manufacturer’s suppliers and other industry stakeholders in its Micromobility Landscape (Micromobility
Industries, n.d.).
Fluctuo, a commercial mobility data processing and analysis provider in Europe, has partnered with several
New Mobility operators and POLIS, the European network of cities and regions for transport innovation,
to produce quarterly and yearly reviews of New Mobility services. Fluctuo’s quarterly and yearly Shared
Mobility Index provides a comprehensive overview of the status of most New Mobility actors and services
in Europe (Fluctuo, 2022).
Other initiatives describe subsets of the New Mobility landscape and their state of play. These include theMeddin bikesharing world map (DeMaio et al., 2021), the demand-responsive transit (DRT) world map

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

11

WHAT IS NEW MOBILITY?

(Foljanty, 2020) and New Urban Mobility Alliance’s New Mobility Atlas (NUMO, 2022) that maps the
dockless shared micromobility landscape in cities worldwide.
The data provided by these sources highlight the importance of monitoring New Mobility. For instance,
the number of operating bikesharing services increased from 31 in 2006 to almost 2 000 in 2021 (NUMO,
2022). DRT services using algorithmic routing and pooling optimisation experienced an exponential
increase over the last eight years, from practically no operations in 2012 to almost 400 running in 2020
(Foljanty, 2020).
Some countries have taken the lead in mapping the New Mobility landscape at the national level. For
instance, the US Government has made progress in characterising New Mobility within its territory withpublications by the US Bureau of Transport Statistics, comparing bikeshare ridership across the country forsystems with docking stations (USDOT, n.d.). At a sub-national level, several US cities were pioneers incollecting data of ridesourcing data from operators. For example, in 2016, the Transportation Authority of
the city of San Francisco, California gathered data from Uber and Lyft on transportation network
companies’ (TNCs) usage to estimate the number of TNCs’ drivers operating in the city and the amount ofvehicle miles travelled (SFCTA, 2017). Since 2014, the New York City Taxi and Limousine Commission has
published data on individual taxi trips performed by yellow and green taxis and ride-hailing apps (NYC TLC,
2022). The United States has also collaborated with Canadian cities through the National Association ofCity Transportation Officials, using data collected yearly from major operators in the United States(NACTO, 2019). The North American Bikeshare and Scootershare Association also published a 2021 stateof the industry report (NABSA, 2022). In Europe, the French Ecological Transition Agency, l'Agence del'Environnement et de le Maîtrise de l'Energie, French rail company SNCF, Keolis and Mappy publishedthree editions of Observatoire des mobilités émergentes (The Observatory of Emerging Mobility) between
2014 and 2020. The latest of these reports studies New Mobility in France, Germany, the United Kingdom
and Spain (ADEME, 2020). Grant Thornton, Mobility City and Fundación IberCaja also released a paper
analysing the current mobility landscape and the medium- to long-term challenges of sustainable mobility
and the automotive sector in Spain (Grant Thornton, Mobility City and Fundación IberCaja, 2021).

Guidelines for classifying New Mobility
One of the primary aims of this report is to provide a comprehensive framework for classifying New
Mobility services and vehicles to capture the similarities and differences between types while enablingcomparisons with other, more established mobility services – principally public transport and taxis.
Whereas public transport and taxis operate under mature service delivery models and well-defined,
broadly accepted service and vehicle taxonomies, many New Mobility services do not.
New Mobility’s wide range and diversity of services, vehicles, business models and service delivery models
create a challenge for classifying them. Establishing a meaningful classification framework is also
complicated because stakeholders may wish to emphasise different features according to their interestsand objectives. Further, many New Mobility services and their underlying business models are still
relatively immature and in flux. The boundaries between certain mobility services are shifting. This is likely
to continue as technologies, business models and regulations evolve. Finally, whereas other, more mature
mobility services, like public transportation or taxis, are governed under broad principles adopted acrossmultiple jurisdictions, the rapid deployment of New Mobility services has led to more localised regulatory
frameworks adopting ad-hoc definitions and service taxonomies.
Combined, these factors complicate establishing a single and broadly accepted classification of all New
Mobility services. Sketching out the high-level contours and categories of the maturing New Mobility
12

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

WHAT IS NEW MOBILITY?

ecosystem is nonetheless vital to frame discourse around New Mobility services and allow publicauthorities to address them consistently and unambiguously in policy and regulation (SAE, 2021). This
process should be guided by sound principles.
A classification framework should provide functional definitions, avoid ambiguous terms and create alogical and consistent structure for comparing and grouping different New Mobility services according tofeatures of interest. It should be general enough to capture important differences between categories but
should not create too many sub-categories that complexify the classification system. It should be broadly
consistent with industry practice and existing literature. It should be flexible enough to incorporate all
existing New Mobility services and unknown future New Mobility services and be useful across disciplines.
Finally, it should not bias analysis, policy or competition in favour of any particular actor, business modelor technology. (SAE, 2021)
The first step in designing a classification system for New Mobility is to establish standard definitions andconcepts that are operator or business-model agnostic. For example, previous ITF work (ITF, 2017) andSAE JA 3163 (SAE, 2021) note that mobility services can be scheduled or on-demand – the former plannedby mobility operators and the latter initiated by user requests. The concept of shared mobility – “theshared use of a travel mode that provides travellers with access to a transportation mode on an as-neededbasis” (SAE, 2021) – may involve concurrent sharing (where several people share a vehicle at the sametime) or sequential sharing (where people use the same vehicle one after the other). Business models may
be business-to-consumer, business-to-business, business-to-government or person-to-person (SAE, 2021).
For most terms and concepts characterising New Mobility services, SAE JA 3163 serves as a reasonable –though not canonical – basis.
The second step in designing a classification system for New Mobility is to determine what top-levelfeatures should characterise mobility services. These classes should be sufficiently flexible to classify
existing mobility services as well as new and future services and vehicles. Entities’ in-house priorities may
shape how they approach classification. For example, consider a classification system developed by apublic agency to design safety regulations for New Mobility services. If safety is their principal concern, theconcept of risk should form the basis for classification. Vehicle mass and speed are then relevant facets ofthe concept or class of risk. In this example, it would be prudent to classify vehicles according to thosefeatures rather than the business model or the powertrain type. Such a classification system may haveseveral categories for electric bicycles but group many small rideable vehicles with similar safety risk
factors together in a single category. This approach was taken in the ITF’s report Safe Micromobility (ITF,
2020a), which proposes regulations and policies to improve the safety of micromobility systems. Other
classification systems might be oriented around other components of vehicle design, such as vehicle
dimensions or space consumption, to design infrastructure and allocate road space. On the other hand,setting data reporting requirements would necessitate a differentiation between commercially- or
publicly-operated New Mobility services and privately-owned vehicles, which does not occur in the safetyoriented classification.
New Mobility services could be classified according to any of the following features:•

vehicle type (e.g. bicycle, e-push scooter, car, van)•vehicle physical features (e.g. size, width, mass, number of axles)•

vehicle operational features (e.g. maximum speed, powertrain type, zero-emissions vehicles,level of automation)•

vehicle ownership (e.g. private or shared)

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

13

WHAT IS NEW MOBILITY?

•

service delivery features (e.g. services provided, rental duration, dock- or parking-based versus
free-floating, e-hailed or pre-reserved)•

business model features (e.g. ride-based, subscription-based, membership-based).

The third step in designing a classification system for New Mobility is to determine how to derive ataxonomy reflecting the organising principles for the classification. This step may involve setting boundary
thresholds for features displaying continuous properties (such as speed or mass). These thresholds should
represent a meaningful difference between categories rather than an arbitrary cut-off. For example, thespeed thresholds for a micromobility classification system could be the average speed of other road users.
This would produce categories for vehicles that are at least as fast as a pedestrian, then vehicles that are
faster than a pedestrian but slower than a cyclist, and so forth, to create regulations around road space
access. Similarly, when the features of interest are discrete or qualitative (e.g. business model, vehicletype), categories should be chosen to create groups that are as large as possible while still capturing thedifferences between categories relevant to the user.
Finally, the classification system must have a hierarchy. In an enumerative classification system, thefeatures should generally be ordered by importance to the user. For example, consider a classification
system with two features: vehicle type and ownership. A transport planner may be most concerned about
vehicle type and would therefore prioritise ordering the taxonomy by vehicle type and then differentiating
between shared and private vehicles. On the other hand, a regulatory agency may be most interested inownership first and would reverse the hierarchy's order.

A taxonomy for New Mobility services
This report aligns with SAE JA 3163 (SAE, 2021) and is organised around two service types: ride services
and fleet-sharing, each divided into several sub-service types. It addresses the high-level features of theexisting and emerging New Mobility landscapes. This breakdown reflects the type of product or service
delivered (e.g. the organising “service concept”), such as a single ride in a platform-owned vehicle or ashort-term vehicle rental. These service types and their sub-categories are sufficiently high-level to capture
meaningful differences between all mobility services (including public transport and popular or informaltransport) while still usefully characterising New Mobility services. This breakdown reflects that different
mobility operators (e.g. public transport, popular transport, taxis and ridesourcing) offer the same product(e.g. a ride) in different ways. It also outlines where potential regulatory weaknesses may lie and, thus,
where regulatory frameworks must be adapted to specific types of services (e.g. taxis versus ridesourcing
or carsharing versus car rental).
Categorising by type of service creates a classification system that is technology- and vehicle-agnostic,
allowing future New Mobility services to be included and compared against those that exist today.
Nonetheless, changes in business practices among incumbent operators may warrant revisiting how public
and popular transport are categorised. For example, taxis are highly regulated ride service types but, facedwith competition from ridesourcing services, many have started offering e-hail ridesourcing. Likewise,
popular transport providers may join digital dispatch platforms to increase revenues. There is also
uncertainty over the long-term economic viability of certain service types; many New Mobility services
have struggled to attain profitability across all markets where they operate. It may be that some New
Mobility services may only be viable under very different market conditions (e.g. operating under public
subsidy). If that were the case, their classification might shift.

14

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

WHAT IS NEW MOBILITY?

Service delivery model

Figure 1. Mobility services spectrum

Service type

Vehicle type

New
Mobility
Services

Long-term lease

Source: adapted from SAE (2021).

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

15

WHAT IS NEW MOBILITY?

Figure 1 provides a snapshot of the current mobility service spectrum. It differentiates ride services, fleet
sharing and various sub-configurations of each. It highlights consequential categories of New Mobility
services and characterises the different roles of platforms, transport companies, contractors, and users ineach service delivery model.
An emerging mobility service that is not included in the spectrum below is Advanced Air Mobility (AAM).
AAM encompasses but goes beyond Urban Air Mobility and is “a broad concept focusing on emerging
aviation markets and use cases for urban, suburban, and rural operations” (SAE, 2021). It includes
passenger, logistics, and humanitarian use cases, including piloted or automated “air taxis”. AAM can
include different categories of New Mobility services (ride services and fleet sharing) and may operate inways that are analogous to public transport (Cohen, Shaheen and Farrar, 2021). However, AAM services
adopt a wholly different regulatory model than ground transport services. For this reason, they are
typically addressed separately from the latter and are not covered in this report.
Ride services

SAE (2021) refers to ride services as “services that provide the traveller with access to rides where thetraveller is the passenger and not the operator of the vehicle”. It makes the distinction between for-hireride services (e.g. those operated on a commercial basis) and not-for-hire ride services (e.g. those operated
on a non-commercial basis between drivers and travellers with similar points of departure or destinations).
Ride services broadly encompass many incumbent services (e.g. public transport, popular or informaltransport and taxis) as well as New Mobility services. This section discusses only ride services that fall into
the category of New Mobility services as outlined at the outset of this report: intraurban passenger
mobility services and vehicles enabled by digital technology.
Ridesourcing

Ridesourcing is a ride service that provides a single pre-arranged or on-demand ride in a vehicle operated
by an employee or contractor of the ridesourcing platform or in an autonomous vehicle owned or deployed
by a platform. The vehicle operator acts as a chauffeur for passengers. The vehicle may be owned by theplatform or the operator or leased from a third party. Vehicle operators (e.g. drivers) may themselves work
for a contractor who contracts with the platform. Ridesourcing comprises three sub-categories:•

ride-hailing operators of cars, vans or powered two-wheelers•

shared autonomous vehicles (SAVs)•

taxis (for e-hailed taxi services).

Ride-hailing operators (sometimes referred to as TNCs) are currently the largest category of ridesourcing
services by trip volume, having proliferated since their introduction in the early- to mid-2010s. This
category includes the most well-known New Mobility service providers with hundreds of thousands ofcustomers and large corporate valuations, such as Uber, DiDi, Lyft, Careem and Grab. Ride-hailing
operators offer both private and shared rides, primarily in passenger cars. However, many providers alsooffer to chauffeur passengers on motorcycles (e.g. Gojek), electric three-wheelers (e.g. Bzzt) and othersmall passenger vehicles. Ridesourcing fares and price-setting mechanisms differ in some significant ways
from similar services. Taxi fares are set according to established publicly available rules. Ridesourcing fares
and prices are set according to proprietary algorithms that balance the supply of drivers and rider demand.
This means that ridesourcing prices may adjust dynamically to attract drivers during high-demand periods.
With the exception of dynamic fare-setting, many taxis are adopting app-based e-hail service models.
Accordingly, some taxi service delivery models can be considered as part of “New Mobility”.

16

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

WHAT IS NEW MOBILITY?

Shared automated vehicles (SAVs) are a potential ridesourcing service type similar in practice to a ridehailing service but with the important distinction that the vehicles are operated without drivers in certain
designated areas. Several local authorities have recently granted licences to SAV operators to conduct pilot
projects under strict geographic limitations while the technology remains in a developmental stage. These
operators include Waymo and Cruise in the United States and Baidu and Pony.ai in the People’s Republicof China. More broadly, there has been much speculation about the developmental timeline and impactof SAVs on the ridesourcing market and mobility systems. The hypothesis held by operators is that, by
eliminating the labour cost associated with the vehicle operator and improving system efficiency, SAVs
could offer low-cost ridesourcing compared to current services, thus attracting a considerable market
share (Stocker and Shaheen, 2017). There remain serious questions regarding the technological feasibility
and public acceptance of fully automated vehicle operation, especially in urban areas (ITF, 2018).
Microtransit

Microtransit (also known as demand-responsive transport or on-demand transport) is a type of ondemand multi-passenger ride-sourcing service typically transporting passengers in small buses or vansalong flexible routes or at flexible times. Trips can be scheduled in advance or requested when needed.
The micro-transit service may provide direct origin-to-destination service or pick-ups and drop-offs at
designated locations. Microtransit services adapted for people with specific mobility needs have greatly
improved travel options for those otherwise dependent on less flexible paratransit services. Microtransit
often complements traditional fixed-route public transport services by providing access to and fromstations. It also operates in areas with poor public transport service, a feature that gained importancefollowing the Covid-19 pandemic as public transport agencies faced drops in ridership and revenues.
Prominent microtransit operators include Swvl and Via. White-label software providers such as Bridj andPadam Mobility offer microtransit digital platform solutions. There are also emerging platforms such as
Jetty, which introduce digital seat reservation and vehicle-dispatching tools for popular transport services,
creating a microtransit-like technology-enabled service (Flores Dewey, 2019).
Ridepooling

Ridepooling (also known as ride-splitting) is a service that provides an open seat for a single trip in a
privately-owned vehicle operated by another user of the platform. Unlike ridesourcing, the passenger anddriver share similar or complementary destinations, and the vehicle operator is neither an employee nor
a contractor for the platform. The vehicle is owned by the user of the platform. When using a private
ridepooling platform, the passenger pays a fee per ride, which is then split between the driver and theplatform. In some cases, private ridepooling platforms are subsidised by public agencies, corporations or
institutions to reduce emissions and road congestion in their communities. Ridepooling serves a relatively
small market compared to ridesourcing. Ridepooling services can be categorised by vehicle type:
carpooling and vanpooling platforms, and motorcycle-pooling platforms.
Carpooling platforms connect passengers with drivers in typical passenger vehicles such as cars, trucks orvans. There are many private platforms, often operating in a limited geographic area. These include
BlaBlaCarDaily, Karos and CityGo (France), RideAmigos (North America), TwoGo (Germany) and Moovit(Israel). There are also intercity carpooling platforms, which fall outside this report’s scope.
Motorcycle-pooling platforms like QuickRide and RedBus’ rPool service have also emerged recently in
India. Through a mobile application, motorcycle-pooling platforms connect a motorcycle operator with asingle passenger who sits on the back of the motorcycle.

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

17

WHAT IS NEW MOBILITY?

Fleet sharing

Fleet sharing refers to services that provide temporary access to vehicles owned by a platform. The
customer operates the vehicle themselves for one or more trips and then returns or parks the vehicle.
Fleet sharing includes shared micromobility vehicles. There are four significant sub-categories of fleetsharing services: bicycle and e-bike-sharing (including cargo bikes); e-scooter-sharing; moped and emoped-sharing; and carsharing.
Bicycle and e-bike sharing

Bicycle and e-bike-sharing platforms were among the earliest New Mobility services to be established, withsmart-card-based systems arriving in Europe in the 1990s. There are now over 1 000 cities worldwide withat least one bikeshare platform. Bikesharing systems provide short-term rental of a bike or e-bike.
There are two main categories of bikeshare systems: docked and dockless. Docked bikeshare systems have
fixed stations where bikes must be picked up and dropped off and are typically operated directly by local
governments or private operators under a contract defining required service levels. They are generally
partially or fully subsidised. Some docked systems offer only pedal cycles, while others also offer e-bikes.
Docked systems typically offer a flat rate for a single trip within a certain time limit or a subscription modelthat allows subscribers to take a limited or unlimited number of trips, also under a certain time limit.
Dockless bikeshare systems have free-floating geo-localised bikes and e-bikes. These bikes can be dropped
off at any suitable location (e.g. a public bike rack or designated bicycle parking space) and are typicallyoperated by private companies. Hybrid models also exist where bikeshare operates either as a dockless ordocked system depending on the type of bicycle (e.g. e-bike versus pedal bicycle) or zone (e.g. central or
peripheral). Dockless systems are more likely to be priced on a time or distance basis rather than a flat rate
per use. Leading dockless bikesharing platform operators include Tier, Dott and Lime. Over time, docklessbikeshare systems have offered more and more e-bikes, especially in Europe and North America.
E-scooter sharing

E-scooter platforms are a more recent type of New Mobility service that have experienced tremendous
growth over the past decade and are expected to continue to grow. These platforms allow for short-term
rent of an electric scooter, usually for a time or distance-based fee. Unlike bikesharing platforms, escooter-sharing platforms are almost exclusively offered as dockless systems with designated drop-off
areas. In some instances, however, e-scooter platforms operate with docks or designated, geo-fenced
parking bays. E-scooter-sharing platforms are less likely to receive public sector subsidies. Many of thesame operators provide both dockless bikesharing and e-scooter-sharing services, including Lime, Dott andTier, while others like Voi offer e-scooters exclusively.
Car and moped sharing

Car- and moped-sharing platforms grant customers access to a motorised passenger road vehicle for atime-based fee. Vehicles are typically located on a mobile application via GPS. They may be dropped off at
any suitable or designated parking location. Carsharing vehicles operate either on a round-trip model (theymust return to where they were picked up) or on a one-way model (they can be dropped off at anotherdesignated or freely chosen location within a designated geographic perimeter). As they operate on the
roadway in mixed traffic, proof of a valid driver’s license is required to hire a vehicle for which a licence isnecessary. Moped-sharing is similar to e-scooter-sharing and bikesharing in that it is typically priced by theminute and often used for one-way trips.
Many carsharing platforms offer vehicles for rent by the hour or day. Carsharing co-operatives emerged in
the 1940s in Europe and transformed into commercial operators in the 1990s (e.g. Mobility Carsharing in
18

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

WHAT IS NEW MOBILITY?

Switzerland in 1997). Some carsharing services are still operated under a peer-to-peer model (wherein car
owners rent out their vehicles when they are not using them), now enabled by a digital platform (e.g.
Cozywheels). North American carsharing emerged primarily as a commercial venture in the 2000s (e.g.
ZipCar in 2000). SHARE NOW and Zipcar (owned by Avis) are the market leaders in Europe and North
America, respectively. Incumbent vehicle rental market actors (e.g. Europcar) are starting to offer shortterm carsharing services to compete with new carsharing platforms. Popular moped-sharing operators
include Cityscoot, Revel and Yego.
Long-term vehicle rentals, often used for inter-urban and holiday travel, do not fall within the definition of
New Mobility for this report. Traditional car rental services offered by companies like Avis and Enterpriseand newer peer-to-peer carsharing platforms like Turo fall into this category, as do day- or week-longbicycle rentals. In the future, however, long-term vehicle-sharing subscriptions may become moreprevalent for daily intraurban travel as car owners give up their cars for this service as a means ofminimising car maintenance and the risk of theft. Bike companies have tried the same method. Swapfiets,
a Paris-based commercial bike leasing company, and Veligo, a publicly-owned electric bicycle and cargo
bike leasing scheme in the Ile de France region, offer long-term bicycle leases via subscription. The fee
includes the rental, cost of maintenance and theft insurance. The hope is that users will purchase e-bikes
after trying them.

A taxonomy for New Mobility vehicles
Table 1 provides definitions of existing and emerging New Mobility vehicles. It provides an overview of thediversity of form factors and serves as a guide for terms used in the remainder of this report. Several broadtypes of vehicles were identified and grouped into three top-level categories:•

micromobility•

powered light mobility•

car or van-like vehicles.

The definitions for micromobility and powered light mobility vehicles in Table 1 are largely based on thereport “Safe Micromobility” (ITF, 2020a) and SAE Recommended Practice “Taxonomy of Powered
Micromobility Vehicles” (SAE, 2019). Table 1 defines the most common vehicle types for each category.
Table 1. A taxonomy of the most common New Mobility vehicle types
Micromobility
BicyclesBicycle (bike, cycle)
A road vehicle that has two or more wheels and is generally propelled by themuscular energy of the persons on that vehicle, in particular by means of a pedal
system, lever or handle (e.g. bicycles, tricycles, quadricycles and carriages).

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

19

WHAT IS NEW MOBILITY?

Cargo bikeA bicycle designed as a load- or passenger-carrying vehicle – e.g. cargo-bike – in twoor three-wheel configurations.

Pedicab (cycle rickshaw)
A rickshaw-like pedal-powered vehicle able to carry two or more passengers.

Electric bicycles (e-bike)An electrically assisted or electrically propelled bicycle-like vehicle. Regulatory
distinctions regarding different classes of e-bikes are typically based on speed andwhether the rider must be pedalling for the electric motor to engage.
Pedelec (slow e-bike, Class 1 pedal assist bike)
A type of pedal-assisted bicycle where the electric assistance cuts off when thevehicle reaches approximately 25 km/h (exact limit depends on local regulations). A
pedalec only provides assistance when the user is pedalling.
Speed-pedelec (fast e-bike, Class 3 e-bike, speed pedal-assisted e-bike)
A type of pedal-assisted bicycle where the electric assistance cuts off when thevehicle reaches approximately 45 km/h (exact limit depends on local regulations). A
speed-pedelec only provides assistance when the user is pedalling.
E-pedicab (e-cycle rickshaw)
A rickshaw-like electric-assisted pedal-powered vehicle able to carry two or morepassengers.

Scooters
Standing scooter (kick-scooter, push scooter)A human-powered street vehicle with a handlebar, deck and wheels propelled by arider pushing off the ground. Models exist with two, three or four wheels. Standing
scooters are distinguished from skateboards by the presence of a central control
column and a set of handlebars.
E-scooter (standing electric scooter, powered standing scooter, seated electricscooter, powered seated scooter)A stand-up or seated scooter propelled by an electric motor, irrespective of the user
kicking.
Mobility scooter
An electrically powered vehicle specifically designed for people with restrictedmobility, typically those who are elderly or disabled. The term scooter is used inreference to the flat vehicle frame and the foot platform.

20

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

WHAT IS NEW MOBILITY?

Other Rideables
A small, single-rider vehicle designed to be ridden by one person.
Skateboard
A rideable board with four wheels on two axles, propelled by the user kicking against
the ground.

Electric skateboard (powered non-self-balancing board, electric board, e-board,e-skateboard)
A skateboard powered by an electric motor controlled by a hand-held throttle.
Hoverboard (powered self-balancing board, self-balancing board, electric personal
assistive mobility device—when it has a centre column and handlebar)
A self-balancing micro-vehicle consisting of two motorised wheels connected to apair of articulated pads on which the rider stands. The rider controls the speed byleaning forwards or backwards, and the direction of travel by twisting the pads.
One-wheel
A self-balancing electric personal transporter on which the user stands and places
feet perpendicular to the direction of travel on front and back platforms.
Electric unicycle (EUC, yuke, uni)
A self-balancing electric personal transporter with a single wheel. The rider controls
the speed by leaning forwards or backwards and steers by twisting the unit using
their feet. Some dual-wheel models exist, but the principle remains that of a singleaxle device used with feet in the direction of travel.
Electric skates (e-skates, powered skates)
A pair of skates with electric batteries and motors, controlled by the user leaning
forwards or backwards or using a remote control.

Powered light mobility
Throttled e-bike (“twist and go” e-bike, scooter-style electric bike, low-speed
throttle-assisted electric bicycle, throttle on-demand bike)A light bicycle-like two- or three-wheel vehicle able to operate with no pedal action
solely on the impetus of a throttle-controlled electric motor. Maximum speed andelectric motor power ratings vary according to applicable regulations. Throttled
e-bikes able to travel above 25 km/h (exact limit depends on local regulations) areoften regulated as mopeds.

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

21

WHAT IS NEW MOBILITY?

Moped
A powered street vehicle with two or three wheels and a seat, sometimes equippedwith pedals. When powered by an internal combustion engine, its capacity is
typically limited to 50 cc. Maximum vehicle speed depends on national regulations
but is typically limited to 45 km/h. Number plates are imposed in some countries andon some classes of mopeds.

E-mopedA moped powered by an electric motor.

Motorcycle (motorbike)
A powered street vehicle with two or three wheels and a seat, designed to reach
speeds greater than 45 km/h.

E-motorcycle
A motorcycle powered by an electric power motor.

Rickshaw (auto rickshaw, tuk-tuk)
A small motor vehicle designed for transporting passengers or goods, typically withthree wheels and a covered passenger compartment.

E-rickshaw (e-auto rickshaw, e-tuk-tuk)
A rickshaw powered by an electric motor rather than a combustion engine.

Car and van-like vehicles
Internal combustion engine (ICE) car or van (private car)
A common consumer motor vehicle used for private passenger transportation, taxi
services and ridesourcing. Includes variations such as pick-up trucks, minivans,sedans and sport utility vehicles (SUVs) that are primarily used for privatetransportation of passengers. ICE cars can be powered by internal combustion
engines operating on fossil fuels, biogas or hydrogen.
Hybrid car or van (hybrid electric car)A passenger car powered by an internal combustion engine and one or more electricmotors for greater fuel efficiency.

22

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

WHAT IS NEW MOBILITY?

Battery-electric car or van (electric car)A passenger car powered exclusively by an electric motor. Zero-emission vehicles(ZEVs), electric cars that runs completely on electric power, are included in thiscategory, as are micro-ZEVs, an emerging type of inexpensive, compact electric car.
Automated car or van (autonomous car, self-driving car, driverless car)A type of passenger car-like vehicle that uses vehicular automation combining
sensors, actuators and advanced types of algorithmic decision-making to operate in agiven environment safely with little or no human input (SAE Level 4 or 5).
Minibus (jitney, microbus, taxi-bus)
A motor vehicle designed to carry a moderate number of passengers, typicallybetween 12 and 30 people, used by private and public transport services.
Source: ITF (2020a), SAE (2019).

Matching service categories and vehicle types
The New Mobility service categories outlined in Figure 1 can be specific to a single vehicle type or offer
many different types of vehicles. Similarly, a specific vehicle type could be available across many different
services. Figure 2 connects New Mobility services with the most common vehicle types used to perform
those services. This is intended to be an illustrative representation only, not a comprehensive inventory.
There may be service-vehicle connections now or in the future that are not indicated in this figure.
Figure 2. New Mobility services and the vehicle types that perform them

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

23

DEVELOPING NEW MOBILITY PERFORMANCE INDICATORS

Developing New Mobility performance indicators
Developing policies and enacting regulations to ensure a safe, clean, efficient and equitable transportsystem is a critical component of government mandates to improve quality of life. Policy makers’ ability toproactively plan for and shape a rapidly changing transport system, rather than reacting to new
developments, will largely determine the environmental and societal outcomes of New Mobility modes
and how they interact with other essential transport services. This section aims to help policy makers, local
authorities and national governments choose effective New Mobility performance indicators to establish
or improve their New Mobility policies and regulatory programmes.

Why measure New Mobility performance?
When the public sector adopts a vision-led “decide and provide” approach (wherein transport
infrastructure and services are supplied to meet agreed-upon policy outcomes), New Mobility services canimprove the quality of life for urban residents and visitors. However, when policy makers take a morereactive approach towards new business models and emerging services, they often fail to deploy or alignregulations with desired outcomes. When regulators do not adopt coherent frameworks to regulate
mobility services and vehicles, policies can result in more traffic congestion, higher levels of air pollution,
tensions over the use of public space and reduced quality of life. For example, several large cities took areactive approach to the disruptive emergence of ride-hailing services and micromobility platforms.
Instead of carefully evaluating the benefits and externalities of these New Mobility services, cities reacted
by banning operations altogether or making it difficult for them to function. Doing so led to a backlashfrom citizens (ITF, 2016).
The first step to building an effective New Mobility policy and regulatory programme is to assess New
Mobility services in light of existing goals for the transport system. Examples may be those established ina sustainable urban mobility plan - SUMP (Eltis, 2019) or other urban mobility “vision” document. These
plans or processes develop guiding visions for the transport system as a whole. If plans do not exist, theyshould be developed. If they do exist, they may need to be adapted to account for New Mobility services.
For example, in 2021, the EU-based CIVITAS initiative published Safe Use of Micromobility Devices in Urban
Areas (Figg, 2021), guidance on integrating micromobility services into sustainable urban mobility plans.
The second step to building an effective New Mobility policy and regulatory programme is to identify
specific outcomes to which New Mobility services can contribute or which outcomes may be most at riskin the absence of adequate regulation.
The third step is to determine how to measure progress in reaching the established desired outcomes or
mitigating the identified risks.
Choosing effective performance indicators is critical for measuring progress. However, the ever-increasingdata generated by New Mobility modes makes this a challenging task (ITF, 2022). On the one hand,
performance measurement may seem daunting in light of the technical expertise and resources required
to analyse large amounts of data from multiple New Mobility operators. Authorities may be discouraged
24

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

DEVELOPING NEW MOBILITY PERFORMANCE INDICATORS

from developing a robust performance management programme. On the other hand, the abundance ofdata might tempt some regulators to create unnecessarily complex and burdensome performancemeasurement programmes that go far beyond what is needed to serve the public interest. Finally, there isalways the risk that the regulators may focus on services and activities producing digital data and neglect
other “analogue” activities simply because gathering and processing data from the latter may be more
complex than from the former.
A set of guidelines for selecting New Mobility performance indicators is needed. Effective New Mobility
performance indicators should be:1) feasibly calculable on a repeated basis2) relevant to policy goals3) tied to a specific regulatory or policy action4) easily understood and communicated5) consistent across services and modes whenever possible.

Figure 3. Portland’s multi-modal New Mobility performance indicator

Source: PBOT (2020).

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

25

DEVELOPING NEW MOBILITY PERFORMANCE INDICATORS

Many promising mobility performance indicators are currently in practice. For example, like many cities,
Chicago, Illinois, in the United States, seeks to ensure that transport policies contribute to more equitable
access. To that end, the city “identified geographies not currently well connected to regional economic
progress (CMAP, 2019).” It refers to those areas as “economically disconnected areas” (EDA). The city uses
two indicators to monitor equity: one calculates the percentage of ride-hailing trips that begin and end in
an EDA and the other measures the length of trips serving an EDA. These indicators inform policy andinvestment decisions in line with desired equitability outcomes (CMAP, 2019). The government ofCanberra, Australia, uses traffic violation notices and hospital admissions as proxy measures for the safetyof its e-scooter pilot to ensure it is in line with over-arching traffic safety objectives (TCCS, 2021). Finally,
the city of Portland, Oregon, in the United States, published its New Mobility Snapshot in 2020,
incorporating car-based modes (e.g. ridesourcing, carsharing) and micromobility (bikesharing, e-scooter
sharing) into a single performance evaluation programme with indicators for usage, sustainability andequity (PBOT, 2020). Figure 3 illustrates Portland’s multi-modal performance indicator, i.e. the average
trip duration by mode.
There is an infinite number of potential indicators for any given targeted policy outcome. Authorities mustselect the right indicators for their New Mobility performance management programme to be effective.
They must also choose detailed calculation methodologies and measurement frequencies. Ideally, theindicators will be consistent across operators to enable a fair comparison of transport outcomes by New
Mobility mode. However, choosing indicators that apply to a wide range of vehicles and business models
can be difficult. Many governments take different approaches for the same indicator or area of policy
interest. This creates inefficiencies and compliance challenges for operators operating in multiple
jurisdictions. Moreover, different methodologies in different jurisdictions make it difficult to aggregate orcompare the performance of New Mobility at the national or international level.
In an attempt to address this governance alignment challenge, the United States Federal Transit
Administration released guidance on developing mobility performance metrics. The FTA (2020) report“Mobility Performance Metrics (MPM) for Integrated Mobility and Beyond” outlines a tiered frameworkfor developing, categorising and selecting meaningful indicators and metrics, starting with those that are
most important to individuals travelling (see Figure 4). It covers both new and incumbent mobility services
and aligns relevant indicators across local, regional and national governance levels.
At the heart of the Federal Transit Administration’s report, mobility performance metric framework aretraveller-centric indicators that capture how well mobility services meet the needs of individual travellers.
Three tiers of indicators measure mobility systems at different levels. Tier 1 indicators capture how wellmobility services and the mobility system perform as they meet travellers’ needs. These system-centricmeasures relate to system performance and efficiency, including local geographic accessibility
performance. Tier 2 indicators capture the regional-scale secondary impacts of mobility services and theoverall mobility system. They relate to regional economic, environmental, safety and social equity
outcomes. Finally, Tier 3 indicators aggregate elements of previous tier indicators at the national scale.
Tier 3 indicators provide a framework for developing and deploying national policies in line with theoutcomes characterised and captured in the previous three levels. (FTA, 2020)
This framework helps align indicator development and tracking through all relevant governance levels. In
Europe, the framework could be further extended to cover supra-national governance levels in areas
where the European Union has an identified role.

26

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

DEVELOPING NEW MOBILITY PERFORMANCE INDICATORS

Figure 4. US Federal Transit Administration’s tiered mobility performance
metrics framework and policy objectives

Core indicators:
Impacts toindividual travelers

Source: adapted from (FTA, 2020).

Many New Mobility stakeholders would benefit from a set of common indicators. Operators would nolonger need to create custom performance reporting programmes for each new service area. Regional andnational governments could easily compare performance across all urban areas within their jurisdictions.
Larger, well-resourced cities could easily share expertise and best practices with smaller cities if thosecities used common performance indicators. Finally, standardised New Mobility indicators would enable
compiling national and international databases, allowing the impact of New Mobility on global urban
passenger transport to be better understood.

Potential performance indicators by category
Table 2 presents the list of potential policy-relevant indicators for the performance evaluation of New
Mobility services to serve the public interest. This section proposes standard calculation methodologies,
data needs and reporting frequencies for several performance indicators in five policy areas: sustainability,
safety, utilisation, accessibility and equity. It associates each indicator with one or more policy actions anddiscusses the benefits and drawbacks of each indicator.
Table 2 does not address all possible indicators. Rather, it establishes initial starting points for
implementing effective New Mobility metrics tied to specific policy actions. Indicators such as these canserve as the foundation for a robust performance management programme. Other indicators may also be
relevant to address context- or actor-specific issues.
Crucially, these indicators help guide the development of New Mobility services and a means to monitor
them efficiently. Nonetheless, many of the proposed indicators impose burdens on actors incentivised or
required to report them to public authorities.

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

27

DEVELOPING NEW MOBILITY PERFORMANCE INDICATORS

By their very nature, the indicators target a small subset of all mobility services and actors. At this stage intheir development, and possibly for some time into the foreseeable future, New Mobility services
represent a small share of overall trips, trip volumes and related impacts. For example, the marginalcontribution of vehicle-kilometres travelled by ridesourcing may trigger higher congestion levels. Still, theoverwhelming volume of personal vehicle and freight distribution travel is the main contributor to urban
congestion. Likewise, poorly parked micromobility vehicles may be seen to contribute to urban clutter, but
the main source of illegal or anarchic occupation of urban public space remains private cars and urbandelivery vehicles (ITF, 2021a).
The imposition of reporting requirements linked to this or other indicator sets should account for theabove. At the very least, indicators chosen to monitor the contribution of New Mobility services to specificpolicy outcomes should mirror analogous indicators tracking how other mobility services or travel
contributes to those same outcomes. Ideally, generic indicators should be developed and deployed tomonitor all mobility services and trips. There are instances where developing and deploying indicators
specific to New Mobility serves policy goals – especially as these services are new and quickly developing.
When this is the case, public authorities should clearly and transparently communicate why the indicators
are only applied to New Mobility services.
Table 2. New Mobility performance indicators in five policy areas
Policy area

Indicator
1.1 Vehicle-kilometres and passenger-kilometres travelled

Sustainability

1.2 Average vehicle lifespan1.3 Alternative mode replaced and trip generation effects
1.4 Operational CO2 emissions
2.1 Injury rate

Safety2.2 Crash rate
2.3 Share of passenger-kilometres travelled on low-stress routes
3.1 Vehicle utilisation rate

Utilisation

3.2 Trip distance (or trip duration for round-trip services)
3.3 Total users
4.1 Access latency

Accessibility

4.2 Number of trips starting or ending near essential services and opportunities
4.3 Vehicles or trips available by area (spatially aggregated)
4.4 Trip purpose
5.1 Vehicle and trip availability in targeted service areas

Equity5.2 Number of trips starting or ending in targeted service areas
5.3 Vehicle and trip availability for users with physical disabilities

Sustainability indicators

Sustainability is a challenge for transport policy today. There remains a considerable gap between thedesire of public authorities to monitor the sustainability impacts of New Mobility and their ability to do so28

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

DEVELOPING NEW MOBILITY PERFORMANCE INDICATORS(ITF, 2021a). A significant challenge is determining the appropriate methodology and reasonableassumptions for computing environmental impacts, including CO2 emissions.
Table 3 provides details on the sustainability performance measures from Table 2. Three of the four
indicators included in this section are intended to be directly measurable (e.g. vehicle-kilometres travelled)
in order to limit ambiguity and complexity while offering policy-relevant information.
CO2 emissions are such an essential component of sustainability that Indicator 1.4 is dedicated solely tomonitoring operational CO2 emissions. A full account of environmental impacts should consider emissions
and impacts over the whole lifecycle of the service, including upstream energy and end-of-life disposal
impacts. ITF developed a framework and methodology for measuring New Mobility services’ lifecycle
energy and CO2 impacts. They are presented in the report Good to Go? Assessing the Environmental
Performance of New Mobility (ITF, 2020b). Some of the indicators below help feed into lifecycle
assessments of energy and CO2 (e.g. lifespan of vehicles and vehicle-kilometres travelled). Others shouldreflect the guidance outlined in the same report (e.g. operational CO2 emissions).
Table 3. Sustainability indicators for New Mobility services
Indicator 1.1: Vehicle-kilometres and passenger-kilometres travelled
Policy mandate

Monitor and/or limit urban congestion.

Policy action

Offer incentives or regulations to achieve an average vkm/pkm threshold. Note that individual
thresholds should be set by mode. This indicator could also be used to assess the potential for
distance-based fees.

Considerations

This indicator can be used to compare marginal contributions to congestion across all New Mobility
modes. However, it cannot serve as a metric for overall congestion since most vkm do not involve
New Mobility services.

Reporting frequency

Monthly or annually

Data needs

1.

Passenger-related vehicle-kilometres (pvkm) for vehicles travelling with passengers, whether
the vehicles are deployed in a fleet or operated by contractors. For ridesourcing vehicles, thismeasurement should commence when a trip is booked, not when the passenger has boarded,to account for passenger-related vehicle travel.

2.

Operational vehicle-kilometres (ovkm) covering indirect vkm related to support vehicles
involved in maintenance, rebalancing, charging and other routine operational tasks.

3.

Total number of passengers or riders.

4.

Total number of vehicles (split by passenger and operational vehicles).

Calculation method

Total vehicle-kilometres (vkm): sum 1 and 2 above by vehicle class. Report overall vkm separately
for pvkm and ovkm.
Average vehicle occupancy rate: divide 3 by 4 (passenger-carrying vehicles) above.
Passenger-kilometres travelled (pkm): multiply pvkm by the average vehicle occupancy rate.
Average pvkm per pkm: divide the total pvkm by the number of pkm travelled.
Average overall vkm per pkm: sum 1 and 2 above and divide by pkm.

Examples in practice

San Francisco, California, in the United States, uses vkm as a performance indicator for ride-hailingcompanies (SFCTA, 2017), while São Paolo charges ride-hailing companies a dynamic fee based on
mileage (Joshi et al., 2019).

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

29

DEVELOPING NEW MOBILITY PERFORMANCE INDICATORS

Indicator 1.2: Average vehicle lifespan
Policy mandate

Monitor and/or limit the total environmental impact of New Mobility, including waste generation.

Policy action

Offer incentives or regulations to achieve an average lifespan threshold. Note that individual
thresholds should be set by mode.

Considerations

While this indicator is based directly on chassis replacement, the increasingly modular nature ofmany vehicles used for micromobility complicates this indicator’s use for these services. Some
convergence over what constitutes a “vehicle” in lifespan calculations may be necessary. Given theincreasing importance of electric modes, battery lifespans could also be monitored. Considerations
for sustainable recycling programmes should be included in any lifespan-based policy action.

Reporting frequency

Annually

Data needs

1.

Start and end of service dates for each vehicle chassis when vehicles are retired.

2.

Total pkm for each vehicle chassis retired.

Calculation method

Average lifespan: difference between start and end of service dates for each vehicle chassis retiredin the period (in months) averaged over the total number of chassis retired in the period.
Average pkm per month of lifespan: for each chassis retired in the period, divide the pkm thechassis served by the months of lifespan, then average over all chassis retired in the period.

Examples in practice

Voi, an e-scooter platform operator, has made vehicle lifespan information available to the public(Holm Møller and Simlett, 2020).

Indicator 1.3: Alternative mode replaced and trip generation effects
Policy mandate

Promote an efficient and sustainable transport system.

Policy action

Introduce new regulations or invest in alternative modes if New Mobility is found to be replacing
alternative modes with fewer social externalities.

Considerations

This indicator can compare modal shifts induced by all New Mobility modes, including private usersof New Mobility vehicles (depending on the survey design). Traditional surveys are often expensive
to conduct. However, application-based New Mobility services could propose the survey throughthe platform’s interface.

Reporting frequency

Annually or at regular multi-year intervals

Data needs

1.

Calculation method

Alternative mode replaced: count the number of trips that would have been taken by eachalternative mode, with responses weighted to provide a representative sample of New Mobility
users. Alternative modes in the questionnaire should include public transport (if available), walking,driving, cycling and New Mobility modes (with an option to indicate that the trip is an induced tripthat would not have occurred if the New Mobility mode had not been available). This assessment
should ideally capture full trip chains and not just the dominant mode used.

Examples in practiceMany cities, including Wellington, New Zealand (vehicle-sharing) and Boston, Massachusetts,
United States (ride-hailing), have surveyed New Mobility users about alternative mode replacement(Wellington City Council, 2020; MAPC, 2018).

30

Survey responses from a representative and statistically significant sample of New Mobility
users.

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

DEVELOPING NEW MOBILITY PERFORMANCE INDICATORS

Indicator 1.4: Operational CO2 emissions
Policy mandate

Monitor and/or limit CO2 emissions from transport.

Policy action

Offer incentives or regulations to achieve an average CO2/pkm threshold. Individual thresholds
should be set by mode.
Offer incentives to increase the share of vkm that uses more environmentally friendly vehicles.

Considerations

This indicator can compare sustainability across all New Mobility modes. It relies on the averagerate of CO2 emissions per vkm, which may be difficult to verify or validate. In many countries,
automotive original equipment manufacturers are required to report CO2 and energy performance.
Where this is the case, such figures should be used. Where such requirements do not exist, theyshould be considered for new vehicle types (e.g. electric scooters and bikes) in line with otherenergy and CO2 reporting requirements for consumer goods. In the absence of official reporting
requirements, third-party verification should be considered.

Reporting frequency

Monthly or annually

Data needs

1.

Passenger-related vkm (pvmk) (for vehicles travelling with passengers deployed in a fleet or
operated by contractors) and operational vkm (ovkm) (from support vehicles involved inmaintenance, rebalancing, charging and other routine operational tasks) for each vehicle
model.

2.

Average CO2 emissions per kilometre by vehicle model.

3.

Total passenger-kilometres (to calculate the average emissions per pkm).

Calculation method

Total lifecycle CO2 emissions: multiply the pvkm and ovkm by the average lifecycle CO2 emissions
per kilometre for each vehicle type in the operator’s fleet.
Average CO2 emissions per pkm: divide the total lifecycle CO2 emissions calculated above by the
total pkm.

Examples in practice

ITF has developed a comprehensive lifecycle assessment methodology for capturing New Mobility
services' CO2 and energy intensities (ITF, 2020b). This can serve as the basis for other lifecycle
assessments and has been used to develop certain operator-based lifecycle assessments of theirservices (Lime and Tier). Some operators, including Gojek and Uber (ridesourcing), as well as Tier,Voi and Lime (vehicle-sharing), already make CO2/pkm available to the public in many markets(WEF, 2022).

Safety indicators

The safety impacts of New Mobility services are often a primary public concern when a New Mobility
service is launched (ITF, 2020a). Safety performance is not evenly measured across modes. Generally, a
relatively large amount of quality data is available for motorised modes like cars, vans, trucks and powered
two-wheelers. However, there are far less available data for bicycles, scooters and other forms of
micromobility. Road safety crashes for the latter are largely under-reported – especially for non-fatal
crashes (ITF, 2020a).
The following three indicators estimate the public safety impacts of New Mobility by measuring injuries
and road crashes, as well as users’ perceptions of safety. Robust data reporting relationships with otherstakeholders (e.g. law enforcement, hospitals and other health services) are critical for indicators 2.1 and2.2. This set of safety indicators assumes that New Mobility operators already have establishedmechanisms to deter unsafe behaviour by the vehicle operator, such as speeding, parking in restricted
areas and impaired or double-riding (for e-scooters).
There is ongoing work on micromobility safety to use fine-grained acceleration data to detect swerving
and rapid deceleration, which provides direct evidence of unsafe behaviour or potential injuries (ITF,

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

31

DEVELOPING NEW MOBILITY PERFORMANCE INDICATORS

2020a). Currently, crash-detecting sensors are rarely installed on vehicles to record crashes and they are
not typically installed on private vehicles. Crash-detecting sensors generate large amounts of data that canbe challenging to process. For this reason, the report does not include an indicator related to such sensors.
Nevertheless, this area of research appears promising for future policy applications such as identifying
safety hazards.
The perception of safety (and security) is an important factor to consider when establishing safety- or
security-enhancing policies. This is not captured in the indicators below but should be explored later sincesafety- and security-enhancing policies may fail or underperform expectations if the result is perceived asless safe or secure than the current situation (ITF, 2013).
Table 4. Safety indicators for New Mobility services
Indicator 2.1: Injury rate
Policy mandate

Ensure a safe transport system for all travellers.

Policy action

Address the danger posed by non-New Mobility service cars and trucks. Invest in targeted traffic
safety improvements in risky areas. Introduce new regulations for unsafe New Mobility vehicles or
enforcement mechanisms for unsafe behaviour. Improve communication of critical safety
information for all drivers and New Mobility service users.

Considerations

This indicator can be used to compare the impact on safety across all New Mobility modes. Notethat true injury rates for walking, cycling and scootering are often vastly underreported. In addition,
it can be difficult to collect reports of minor injuries. Regulators should consider multiple injury data
sources, such as injuries reported to the service operator, hospital or clinic admissions, andrequests for emergency services.

Reporting frequency

Monthly or annually

Data needs

1.

Total number of injuries (fatal and serious, as measured on a standardised scale, e.g. theMaximum Abbreviated Injury Scale or MAIS in Europe) resulting from the use of a New
Mobility vehicle, and the location at which the injury occurred.

2.

Number of people involved in a crash with a new mobility vehicle by road user category.

3.

Total passenger-kilometres (pkm) per New Mobility service model.

Calculation method

Injuries per pkm: total number of injuries reported (fatal and serious) divided by the total pkm.
Distribution of injuries by the road user category of the crash opponent: number of injuries
reported (fatal and serious) by the road user category, the crash opponent, including self-crashes,
divided by the total number.

Examples in practice

The government of Canberra, Australia, uses traffic violation notices and hospital admissions as
proxy measures for the safety of its e-scooter pilot (TCCS, 2021).

Indicator 2.2: Crash rate
Policy mandate

Ensure a safe and convenient transport system for all travellers.

Policy action

Introduce or modify regulations of key crash opponent vehicle class. Introduce new regulations orenforcement for New Mobility vehicles with high crash rates. Improve communication of regulatory
and compliance information for users.

Considerations

This indicator can be used to compare the impact on safety across all New Mobility modes. Crash
rates should be collected from law enforcement agencies or health authorities.

Reporting frequency

Monthly or annually

Data needs

1.

Total number of traffic crashes involving the use of a New Mobility vehicle.

2.

Total pkm per New Mobility service model.

32

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

DEVELOPING NEW MOBILITY PERFORMANCE INDICATORS

Calculation method

Crashes per pkm: total number of crashes reported divided by the total pkm.

Examples in practice

The government of Canberra, Australia, uses traffic violation notices and hospital admissions as
proxy measures for the safety of its e-scooter pilot (TCCS, 2021).

Indicator 2.3: Share of pkm travelled on low-stress routes
Policy mandate

Ensure a safe and convenient transport system for all travellers.

Policy action

Implement ambitious speed management policies. Implement targeted urban vehicle accessregulations. Make targeted investments in new separated infrastructure for light and micromobility
modes. Improve directional indications for low-stress routes.

Considerations

This is a relatively complex indicator to compute as “stress” is perceived by a heterogeneous userbase and is context-specific. For some, stress may only be related to the traffic environment, but forothers, the perception of stress may also be linked to acts of violence, including gender violence.
Perceived stress may also change over time as the travelling population becomes more skilled andcomfortable with new travel behaviours.
From a technical perspective, stress indicators are not necessarily straightforward to calculate sincevehicle trajectories must be snapped to the road network.
Stress indicators provide helpful insight into the traveller experience and enable targeted policy
interventions, including infrastructure deployment.

Reporting frequency

Monthly or annually

Data needs

1.

New Mobility traffic volumes by road segment and direction.

2.

Network map identifying low-stress road segments for New Mobility users.

Calculation method

Perceived stress methodology: low-stress routes can be captured by several measurements. Directsurveys can capture peoples’ perceptions of stress. Proxy indicators could also be used, such as thedegree of separation between micromobility users and other road users, overall traffic volume perstreet segment, average vehicular travel speeds, etc.
Share of pkm travelled on low-stress routes: multiply directional road segment volumes by thelength of the road segment for all low-stress road segments. Divide the result by the product ofroad segment volumes and the road segment length for all road segments.

Examples in practiceMany cities set targets for increasing the use of low-stress routes, including Boulder, Colorado,United States (City of Boulder, 2019). The City of Calgary, Canada, uses GPS trajectories to plan
dedicated infrastructure for micromobility (Kanygin, 2019).

Utilisation indicators

Understanding how and where people travel around a city is critical to maintaining an efficient transportsystem. The following indicators provide evidence for policy decisions around appropriate fleet sizes for
New Mobility services and identify opportunities to encourage mode shifts towards sustainable travelmodes. These indicators are measured directly and straightforwardly to calculate.
Table 5. Utilisation indicators for New Mobility services
Indicator 3.1: Vehicle utilisation rate
Policy mandate

Maintain an appropriate fleet size to avoid cluttering public space while meeting transport needs.

Policy action

Allow increases or enforce decreases in fleet size based on utilisation thresholds.

Considerations

This indicator can be used to compare utilisation across all New Mobility modes. Weekdays andweekends could be determined separately. Seasonal effects are quite pronounced in some regions;

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

33

DEVELOPING NEW MOBILITY PERFORMANCE INDICATORS

therefore, policy thresholds for the fleet size set by public authorities should take into accountspecific policy targets.
Caution should be exercised in tracking utilisation rates in the context of spatial requirements forfleet deployment. Where there are requirements to service a broad area, utilisation rates will
decrease on average due to fewer trips in low-density or less central locations. Likewise, publicauthorities may want to incentivise fleet availability in targeted areas that may naturally have low
utilisation rates. Low utilisation rates in those contexts may not necessarily reflect specific targeted
public policy outcomes (e.g. access to vehicles for a targeted population versus efficient use ofvehicles).
Reporting frequency

Monthly

Data needs

1.

Total number of trips.

2.

Active fleet size.

Calculation method

Average vehicle utilisation rate: divide the total number of trips by the total number of active
vehicles in the reporting period and the number of days in that period. Take the average over the
fleet.

Examples in practiceMany cities, including Santa Monica, California and St. Louis, Missouri in the United States, haveadopted dynamic fleet size limits based on utilisation rate (ITF, 2021a).

Indicator 3.2: Trip distance or travel duration
Policy mandate

Promote an efficient and sustainable transport system.

Policy action

Trip distance distribution and the share of short trips can be used to identify opportunities forincentivising mode shift to sustainable and active modes. Average travel duration statistics help
gauge fleet size needs (e.g. for carshare or cargo bikeshare) as vehicles that operate on a round-trip
basis are used for longer periods and are not available at a parking stand or dock.

Considerations

This indicator can be used to compare travel behaviour across all New Mobility modes.

Reporting frequency

Monthly

Data needs

1.

Distance travelled for each trip.

2.

Travel duration for each trip, disaggregated by service model (point-to-point versus roundtrip).

Calculation method

Trip distance and travel duration distribution: calculate the average trip distance and travel
duration, and relevant quantiles of the distribution.
Share of short trips: count the number of trips under an established distance threshold (e.g. 4 km).

Examples in practice

Chicago, Illinois, in the United States, uses trip distance to inform policy making about the utilisationof New Mobility (CMAP, 2019).

Indicator 3.3: Total users
Policy mandate

Promote an efficient and sustainable transport system.

Policy action

Understanding the popularity of New Mobility services over time can inform fleet size limits andfuture mobility services planning.

Considerations

This indicator can be used to compare utilisation across all New Mobility modes.

Reporting frequency

Monthly

Data needs

1.

Calculation method

Total users: take the sum of the unique active users.

Examples in practice

The city of Seoul, Korea incorporates the monthly users’ indicator into their bikesharing
performance analysis (Cho, Seo and Kim, 2021).

34

Number of unique active users with at least one trip in the period.

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

DEVELOPING NEW MOBILITY PERFORMANCE INDICATORS

Accessibility indicators

The concept of accessibility is increasingly used to evaluate transport systems and inform decision-making(ITF, 2022). Accessibility is not to be confused with mobility. Accessibility indicators measure the numberof opportunities that people can access in a given time or distance, whereas mobility focuses on travel
speeds and distances, not the reason behind the travel. The indicators presented in this section are related
to trip purposes and destinations, as well as the availability and convenience of New Mobility services forpursuing those trips. These indicators provide policy makers with deeper insights into how effectively New
Mobility services allow residents to pursue essential activities like work and grocery shopping anddiscretionary activities like leisure activities.
Table 6. Accessibility indicators for New Mobility services
Indicator 4.1: Access latency
Policy mandate

Promote an efficient and sustainable transport system.

Policy action

Plan the deployment of additional New Mobility services or regulate rebalancing, charging andmaintenance activities to ensure convenient and timely access for all potential users.

Considerations

The term “latency” is used for this indicator to represent access time across all New Mobility
modes. This indicator enables comparison across all New Mobility modes, including car-based andmicromobility modes.

Reporting frequency

Monthly or annually

Data needs

1.

Average waiting time between request and pick-up (i.e. latency) for ridesourcing andridesharing services each hour (aggregated to predefined geographic zones).

2.

Average time to access the nearest available vehicle (i.e. latency) for vehicle-sharing services
each hour (aggregated to pre-defined geographic zones).

3.

Percentage of unfulfilled trips (number of route or vehicle searches divided by the number oftrips undertaken).

Calculation method

Average access latency: take the average time to access a New Mobility service at key policyrelevant time periods, such as the morning peak, mid-day, afternoon peak and evening.

Examples in practice

Chicago, Illinois, in the United States, used access time as an indicator to evaluate a new e-scooter
vehicle-sharing service (City of Chicago, 2021) and passenger wait time to evaluate ride-hailing(CMAP, 2019).

Indicator 4.2: Number of trips starting or ending near essential services and opportunities
Policy mandate

Ensure broad access to opportunities across the region.

Policy action

Plan the deployment of additional New Mobility services or introduce new infrastructure, such as
mobility hubs, to facilitate the use of New Mobility to pursue essential activities. Using public
transport hubs as key destinations may offer insight into whether New Mobility modes complement
or compete with public transport.

Considerations

This indicator can be used to compare travel behaviour across all New Mobility modes. Geographic
zones should be large enough to mitigate privacy concerns (e.g. larger than a single address orbuilding) but not so large as to prevent detailed policy analysis.
This indicator only provides a first-order estimate, as true trip origins and destinations cannot be
determined for most New Mobility modes.

Reporting frequency

Monthly or annually

Data needs

1.

Location where trip starts (aggregated to pre-defined geographic zones).

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

35

DEVELOPING NEW MOBILITY PERFORMANCE INDICATORS

2.

Location where trip ends (aggregated to pre-defined geographic zones).

3.

Locations of essential services and opportunities.

Calculation method

Number of trips starting or ending near essential services and opportunities: sum the number oftrips beginning and ending in each zone. Identify zones that contain or are within a distancethreshold of each key destination. Suggested key destinations include public transport hubs,
healthcare facilities, job centres and grocery stores.

Examples in practiceMontreal, Canada used the percentage of trips starting or ending at a public transport station toevaluate an e-scooter pilot in 2019 (Lau, 2020).

Indicator 4.3: Vehicle or trip availability by area (spatially aggregated)
Policy mandate

Ensure broad access to opportunities across the region.

Policy action

Plan the deployment of additional New Mobility services or regulate rebalancing, charging andmaintenance activities to ensure that New Mobility is consistently available in designated areas.

Considerations

This indicator can be used to compare availability across all New Mobility modes. Geographic zones
should be large enough to mitigate privacy concerns (e.g. larger than a single address or building)but not so large as to prevent detailed policy analysis.
Whether a vehicle is in “good working condition” may be subjective, and not all issues are
immediately known to the operator, so this indicator may slightly overestimate true availability.

Reporting frequency

Monthly

Data needs

1.

Calculation method

Average number of vehicles or trips available: take the average number of vehicles or capacity
available in each zone across the hours of the day and then across the days of the month.

Examples in practice

The US city of San Francisco, California uses carsharing availability by geographic area to evaluate an
early pilot programme and inform future policy decisions (SFMTA, 2017).

Number of available vehicles in good working condition or available capacity each hour,aggregated to pre-defined geographic zones.

Indicator 4.4: Trip purpose
Policy mandate

Ensure broad access to opportunities across the region.

Policy action

Identify opportunities to make essential trips more convenient through improved infrastructure,
increased availability of New Mobility modes, or even land-use reform.

Considerations

This indicator can be used to compare the trip purpose distribution of all New Mobility modes,
including private users of New Mobility vehicles (depending on the survey design). Traditional
surveys are often expensive to conduct but could potentially be administered through a New
Mobility platform’s application interface for those services delivered via an application.

Reporting frequency

Annually or at regular multi-year intervals

Data needs

1.

Calculation method

Trip purpose: count the total number of trips for each purpose, with responses weighted to provide
a representative sample of New Mobility users.

Examples in practice

The US city Los Angeles, California uses survey questions about trip purposes to understand theimpacts of bikesharing platforms on overall accessibility (SCAG, 2019).

36

Survey responses from a representative and statistically significant sample of New Mobility
users. Suggested trip purpose options include grocery shopping, family care, education, work,
leisure, socialising and healthcare.

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

DEVELOPING NEW MOBILITY PERFORMANCE INDICATORS

Equity indicators

New Mobility can be a major asset in meeting transport equity objectives, but only if performance ismeasured and analysed carefully. New Mobility services can connect areas with limited public transport
access to the rest of the city. New Mobility can also offer convenient door-to-door services for people withphysical disabilities, given the right policies and regulations.
The indicators below represent physical measures of equitability (access, number of trips, availability ofcertain types of services). Two of the indicators focus on measuring New Mobility performance in“targeted service areas”, which should be defined by local governments based on their equity goals. These
areas might include neighbourhoods with limited public transport service, lower-income communities, or
environmental justice zones, where more than 20% of the population lives below the federal poverty line
or more than 30% identifies as a non-white minority. Indicator 5.1 relates to vehicle or trip availability intargeted service areas, which New Mobility operators can directly influence. Indicator 5.2 relates to trips
starting or ending in targeted service areas that are indirectly influenced by New Mobility operators but
are more relevant to policy makers with a mandate to improve mobility equity.
Economic aspects of equitability are also important to consider – and first among these are affordability
indicators. These are not included below but should be addressed in further work on the topic. Suchaffordability indicators should be normalised to costs-per-kilometre (accounting for some proxy tomeasure surge pricing for those modes using that technique). They could also be presented in a way that
captures the cost of these services compared to other transport modes or (where underlying passenger
kilometre travel data is available) as a percentage of average household transport or total expenditures.
Table 7. Equity indicators for New Mobility services
Indicator 5.1: Vehicle or trip availability in targeted service areas
Policy mandate

Share the benefits of New Mobility with all residents.

Policy action

Plan the deployment of additional New Mobility services or regulate rebalancing, charging andmaintenance activities to ensure that New Mobility modes provide equitable access to services.

Considerations

This indicator can be used to compare availability across all New Mobility modes. Targeted service
areas should be defined by local governments based on equity goals.

Reporting frequency

Monthly or annually

Data needs

1.

Number of vehicles available (and in good working condition) or trips available each hour,aggregated to pre-defined geographic zones.

2.

Geographic boundaries representing targeted service areas.

Calculation method

Average vehicle or trip availability in targeted service areas: take the average number of vehicles ortrips available in each zone across hours of the day and then across days of the month. Determine
the average and range of vehicle or trip availability for geographic areas in targeted service areas.
Vehicle or trip availability in targeted service areas relative to broader service area: compare theresults above against results for the full-service area.

Examples in practice

The US city Seattle, Washington requires micromobility operators to allocate 10% of their fleet todesignated equity focused-areas (Beekman, 2020).

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

37

DEVELOPING NEW MOBILITY PERFORMANCE INDICATORS

Indicator 5.2: Number of trips starting or ending in targeted service areas
Policy mandate

Share the benefits of New Mobility with all residents.

Policy action

Incentivise higher vehicle and trip densities in targeted service areas to promote utilisation. Adapt
regulations to support the latter. Launch community engagement initiatives to boost awareness.
Explore subsidies to make New Mobility affordable to lower-income households.

Considerations

This indicator can be used to compare availability across all New Mobility modes. Targeted service
areas should be defined by local governments based on equity goals.
Geographic zones should be large enough to mitigate privacy concerns (e.g. larger than a single
address or building) but not so large as to prevent detailed policy analysis.

Reporting frequency

Monthly or annually

Data needs

1.

Location where trip starts (aggregated to pre-defined geographic zones).

2.

Location where trip ends (aggregated to pre-defined geographic zones).

3.

Geographic boundaries representing targeted service areas.

Calculation method

Number of trips starting or ending in targeted service areas: sum the number of trips beginning andending in each zone that is within a community of concern.
Number of trips starting or ending in targeted service areas relative to broader service area:
compare the results above against results for the full-service area.

Examples in practice

Los Angeles, California, in the United States, measures the percentage of micromobility rides
originating in disadvantaged communities (LADOT, 2020).

Indicator 5.3: Vehicle and trip availability for users with physical disabilities
Policy mandate

Share the benefits of New Mobility with all residents.

Policy action

Incentivise or regulate increasing shares of accessible vehicles in New Mobility fleets.

Considerations

These indicators are specifically intended to enable comparison across all New Mobility modes,
including car-based modes and micromobility modes. The physical disabilities of concern should be
defined by local governments based on equity goals.

Reporting frequency

Monthly or annually

Data needs

1.

Total number of vehicles available for vehicle sharing or ridesourcing during the analysis
period.

2.

Description of vehicle types and their level of accessibility for different physical disabilities.

3.

Latency between accessible vehicle request and trip start.

Calculation method

Accessible vehicle and trip availability: take the number of accessible vehicles available for eachphysical disability of concern and divide it by the total fleet size.
Waiting time indicator for accessible services: average of all accessible vehicle latency durations
compared to all vehicle access latencies.

Examples in practice

The US state of California requires ride-hailing companies to make wheelchair-accessible vehicles
available within a specified response time for each region (Venkataram and D’Agostino, 2022).

Potential performance management challenges
Establishing a New Mobility performance management programme or adapting an existing one for a newmode or service requires significant effort and collaboration. Regulators may face legislative, technical anddata reporting challenges.
38

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

DEVELOPING NEW MOBILITY PERFORMANCE INDICATORSThe first challenge lies in determining the correct legislative authority. Public authorities at the local levelmay not have the legal authority to grant or withhold operating licenses for New Mobility services or torequire data reporting as a condition for receiving a license. Local governments should work with regionalor national counterparts to ensure they have a role in shaping and executing the legal framework forregulating New Mobility. Guidelines for developing the regulatory framework for New Mobility services
can be found in the ITF’s Regulating App-based Mobility Services report (ITF, 2019).
Allocating the necessary resources to maintain a comprehensive New Mobility performance management
programme is another major challenge for all levels of government. Proactive regulation and policy making
for New Mobility services require a complementary but distinct set of competencies for most transport
authorities. Trained staff and software tools are needed to prepare the performance indicators andgenerate actionable policy insights. Governments may find that third-party consultants or service
operators themselves could complement public expertise when setting up a performance management
programme. However, even if tasks are outsourced to third parties or service operators, public authorities
must have sufficiently knowledgeable staff who understand and can manage those relationships andevaluate the quality of the inputs. At a minimum, public authorities should require all staff responsible foroverseeing New Mobility services to have the necessary digital and data skills. A recent OECD concept
note, Competition and Regulation in the Provision of Local Transportation Services (OECD, 2022), provides
several examples of innovation by public authorities in New Mobility regulation.
Finally, a well-designed and efficient data reporting framework is essential for New Mobility performance
management. Performance indicators cannot be elaborated or used for policy development without
access to the underlying data and the ability to verify data if needed. The next section discusses datareporting for New Mobility in detail.

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

39

COLLECTING DATA FOR NEW MOBILITY PERFORMANCE INDICATORS

Collecting data for New Mobility
performance indicators
The New Mobility performance indicators presented in the previous section require the appropriate data.
Acquiring those data can be challenging for public authorities. Until now, national statistical offices andpublic authorities have used traditional data collection methods to measure passenger mobility: household
travel surveys for walking and cycling, vehicle counts and odometer readings for road motor vehicles,
ticketing counts for rail transport. At the same time, much of the data collection and monitoring of New
Mobility services has occurred at the local and regional levels. Aggregated data are rarely passed upwards
to national authorities for use in international comparison.
The need for a data governance framework is becoming increasingly evident to 1) facilitate data reportingfrom the private sector, which owns the majority of the data, to the public sector and 2) co-ordinate datacollection across multiple levels of government (ITF, 2021b). New Mobility services generate a significant
amount of digital data. Public authorities can use the data to monitor and guide the development of New
Mobility services in line with public policy objectives. However, these data usually remain with the privatesector actors that collect or generate them. Public data governance would help reconcile what is
technically possible, what is desirable and what is legally permitted.
This section focuses on data held by mobility operators. Therefore, it will not include data already available
from other sources. For example, the number of injuries and injury crashes used to calculate safety
indicators 2.1 and 2.2 are collected by the police and hospitals, and thus will not be mentioned below. In
addition, this report focuses on data reporting, not data sharing. Data sharing refers to data shared among
different stakeholders to enable mobility services (ITF, 2021b). Data reporting refers to data reported bymobility operators to public authorities, enabling the latter to monitor outcomes and guide policy (ITF,
2021b).

Approaches to reporting New Mobility data
There are three main approaches for New Mobility service providers to report New Mobility data to publicauthorities. The first is that public authorities request raw data related to a private company’s service anddecide which indicators to measure based on those data. However, this approach is inefficient due to thewide range of indicators, and the risk of calculating indicators that are difficult to interpret or explain to
the public. Furthermore, operators typically employ bespoke data structures and syntaxes. Publicauthorities must analyse data provided on a case-by-case basis or invest in transforming the data into acommon syntax. Analysing these data requires specific knowledge that public entities often do not havein-house. In addition, storing and maintaining large amounts of raw data is costly and risky in terms ofpotential data breaches.
The second approach is for public authorities to define and articulate policy objectives and related
indicators. They should also provide a means of measuring progress towards those objectives (e.g. thosesuggested in the list of indicators in the previous section). On that basis, public authorities can request
either the indicators themselves or the data necessary for calculating those indicators from the operator.

40

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

COLLECTING DATA FOR NEW MOBILITY PERFORMANCE INDICATORS

Adopting this approach would be beneficial for both data providers and data receivers. Data providers
relay only the data necessary to calculate the indicator, most of the time at an aggregate level. This helps
make data reporting compatible with privacy concerns. For example, for indicator 4.2 Number of trips
starting or ending near key destinations, public authorities adopting the first approach would ask for all
the data, including location data, which would create issues with privacy regulations, especially in theEuropean Union. Conversely, if the second approach were adopted, mobility operators would aggregate
the raw data and provide only the total number of trips starting or ending near key destinations (ITF,
2021c). Another advantage is that data producers can offer the same type of data, using the same analysis
technique, to different public authorities, thus achieving economies of scale. Public authorities would
benefit from receiving clean and ready-to-use data, minimising data validation and processing costs.
However, under this model, public authorities must trust the private sector to provide reliable andaccurate data. Formal data quality and accuracy audit mechanisms must be in place to anchor that trust.
The third approach is for public authorities to assign the task of collecting and analysing data to third-party
data processors. In this case, public authorities should inform operators of how their data will be used andassure them that they will not be used for purposes outside of the agreed terms.
This approach has advantages for public authorities and mobility operators alike. Many public authorities
do not have the in-house capacity to process data and often outsource the task to third-party aggregators.
For mobility operators, reporting data using the second approach can be burdensome, especially if
different public authorities do not co-ordinate their requests. Providing raw data directly to third-party
aggregators would reduce mobility operators’ time preparing the data. However, public authorities willneed to compare the cost of hiring a third-party aggregator to that of hiring qualified new staff or training
existing staff. Public authorities must also avail themselves of data audit and vetting processes for datadelivered by mobility operators and data provided by third-party processors. Adding third-party processors
in the mix may increase economies of scale if these interact with many mobility operators and authorities
and may also give rise to innovations in measuring and monitoring trends.
Data collected or generated by the private sector pose two policy-relevant risks: privacy and commercial
sensitivity. Privacy is related to personal data. According to the OECD, personal data refer to “anyinformation relating to an identified or identifiable individual (data subject)” (OECD, 2013). This definitionis extended in Article 4.1 of the EU’s GDPR as:…any information relating to an identified or identifiable natural person (“data subject”); an
identifiable natural person is one who can be identified, directly or indirectly, in particular byreference to an identifier such as a name, an identification number [e.g. social security number],location data, an online identifier or to one or more factors specific to the physical, physiological,
genetic, mental, economic, cultural or social identity [e.g. name and first name, date of birth,
biometrics data, fingerprints, DNA …] of the natural person. (EU, 2016)
Both identification and re-identification – that is, the ability to isolate an individual from anonymised or
pseudonymised – are personal privacy risks. In this context, location data are potential indirect identifiersof individuals and their behaviour. For this reason, personal data should be de-identified through privacypreserving mechanisms (PPMs). Cunha, Mendes and Vilela (2021) provide a helpful taxonomy for selecting
appropriate data de-identification PPMs (see Box 1).

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

41

COLLECTING DATA FOR NEW MOBILITY PERFORMANCE INDICATORS

Box 1. Privacy-preserving mechanisms for data reporting
The ability to associate data with the identity and behaviour of a specific person represents a serious
challenge to privacy rights. Data stripped of direct and indirect identifiers may still be used to re-identify
natural persons. Re-identification techniques are generally not trivial, but many are not difficult to apply
successfully. In light of these risks, multiple forms of privacy-preserving mechanisms (PPMs) have beendeveloped and applied. Privacy-preserving strategies must account for the data type (structured, semistructured and unstructured) and the data sub-type (text, numerical, streaming, geospatial, etc.).
Generally, however, PPMs employ one, or a combination, of three broad approaches:•anonymity-based approaches, which strip identifiers and group records into sufficiently
indistinguishable sets•

obfuscation-based approaches, which introduce spurious records, synthetic records and dataor generalise specific data (e.g. location data) to thwart re-identification•

cryptographic-based approaches, which employ
permissioned data record access to protect data.

computational

cryptography

andPublic authorities seeking to ensure personal data protection should assess if, when and what type of
PPM to apply to any personal data they receive or collect. Taxonomies of data types and appropriate
PPMs are integral to the personal data protection task. They should be updated and re-assessed as PPM
efficacy evolves and re-identification threats grow more sophisticated.
Source: Cunha, Mendes and Vilela (2021).

The second type of risk relates to commercially sensitive data. Private companies are reluctant to report
data that disclose sensitive information about their activities and business models. This information can
prejudice the company’s competitive position. The onward transfer of potentially commercially sensitive
data from mobility operators to authorities should be accompanied by a degree of trust that publicauthorities will be good stewards of that data and will implement sufficient protections against data
breaches. As with any onward data transfer from mobility operators, the third-party processor model does
not prevent the risk of data breaches. The ITF report Reporting Mobility Data: Good Governance Principles
and Practices (ITF, 2021b) recommends data reporting and handling protocols that mitigate these risks.

Data reporting: New Mobility data formats
The second aspect to consider for data reporting is data structure and format. Without a convergence
towards compatible data structures and syntaxes, data reporting requirements can be burdensome or
costly for private companies. Most operate in different cities, regions and countries and must report data
to several public authorities. Likewise, public authorities risk receiving data in incompatible formats whenrequesting data from several service providers. A common understanding of terms (semantics), data
structure (schemas) and data reporting with specific machine-encodable and readable formats (syntaxes)
enables straightforward and efficient data reporting. This can help public authorities carry out theirmandates and analyse the data received from different companies operating in the same jurisdiction. It
can also serve as the basis for upwards compatible and cross-jurisdiction comparable data.

42

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

COLLECTING DATA FOR NEW MOBILITY PERFORMANCE INDICATORS

Data semantics

Traditional transport services, such as public transport, already have accepted semantic models. On thecontrary, most New Mobility services still do not have one. The first section of this report provided ataxonomy of current services and vehicles. It can be a future reference to help build the data semantics
for incorporating New Mobility services.
There are other examples of initiatives that provide a common language to describe New Mobility vehicles,
services and operations. The first one is the Open Standards for Linked Organisations (OSLO) semanticalmodel, developed in the Flanders region of Belgium. It established a lexicon referring to traveller and trip
information, booking actions, network descriptions, operators’ service supplies and operating licences(OSLO, n.d.). SAE provides a standardised set of definitions and methodologies covering commonly used
terms and indicators in several publications: Mobility Data Collaborative Data Sharing Glossary and Metrics
for Shared Micromobility (MDC, 2022), Taxonomy of On-Demand and Shared Mobility: Ground, Aviationand Marine – JA 3163 (SAE, 2021) and “Taxonomy of Powered Micromobility Vehicles – JA 3194” (SAE,2019). The Mobility Data Specification (MDS) comprises a “metrics” application programming interface(protocols that allow data to flow securely between operators and authorities, API) endpoint which
generates indicators from MDS data, incorporating common semantical terms underlying their calculation(OMF, 2020). Finally, the EU Public Transport Reference Data Model (EN 12896) or “Transmodel” includes
a well-documented glossary of mobility services and terms (CEN, n.d.).
Data schemas

A common understanding of semantical terms (or a common and consistent translation logic between
different terms) is a necessary pre-condition for data reporting and sharing. However, it seems a far reachto envisage a common data syntax for all mobility services, or even all New Mobility services. Though it istheoretically possible to describe operations for e-scooter-sharing and a subway using the same syntaxicalmodel, it is not easy or practical. This level of syntaxical convergence would also prove costly andburdensome, as it would require service operators to adapt their systems. In addition, adopting a single
data syntax (or locking one syntax in for each type of mobility service) can potentially hamper innovation.
A preferable approach is encouraging convergence around functional data structures or schemas andletting actors adopt syntaxes within those common structures. This ensures that each discrete data syntax
can be mapped to another, facilitating data collection and processing (ITF, 2021d).
The EU Transmodel is a well-known example of a data schema. It provides a consistent language thatcovers many aspects of public transport. It has been developed and enhanced over several decades by
public transport experts from European countries (CEN, n.d.). In the latest version of Transmodel,
additional concepts were added to simplify bridging traditional public transport and alternative modes,
such as cycling and carsharing.
Data syntaxes

Mobility data syntaxes structure data and enable their processing in a consistent and coherent manner.
Currently, no universal data reporting standard has been adopted, but there is a benefit in converging
towards a limited set of open and mode-neutral syntaxes. This convergence can reduce burdens for bothdata providers and data receivers. However, they may create additional risks concerning personal orcommercially sensitive data due to the detail and granularity of the data collected. For this reason, thedata syntaxes should include personal data protection by default and design to minimise the risks ofcollecting and storing commercially sensitive data.
MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

43

COLLECTING DATA FOR NEW MOBILITY PERFORMANCE INDICATORS

Different data syntaxes already exist and may become a standard in the future. Examples of these are
Network and Timetable Exchange (NeTEx), General Bikeshare Feed Specification (GBFS) and the Mobility
Data Specifications (MDS).
NeTEx is a standard developed by the European Committee for Standardisation (CEN) for exchanging
public transport schedules and related data (network, timetable and fare information). Many NeTEx
concepts are taken directly from Transmodel. The definitions and explanations of these concepts are
extracted directly from the respective standard and reused in NeTEx, sometimes with adaptions to fit the
NeTEx context. Data in NeTEx format are encoded as XML documents that must conform precisely to theTransmodel schema. In 2017, the EU Commission Delegated Regulation (EU) 2017/1926 recommended
developing a data exchange format dedicated to new modes (EU, 2017). This work resulted in NeTEx Part5, which describes carsharing, cycle-sharing, carpooling, car and cycle rental and other services and their
inclusion in the Service Interface for Real Time Information, or SIRI (CEN, 2020). While the NeTEx format
does not create privacy issues in public transport data, it may risk re-identifying unique users. Therefore,
there should be a system to de-identify personal data.
The General Bikeshare Feed Specification (GBFS) is an open standard that defines shared mobility services.
It has supported the recent integration of various shared mobility services in trip planning applications. It
was created by Mitch Vars and endorsed by the North American Bikeshare Association (NABSA) in 2015
before the transfer of its maintainership to MobilityData in 2022. It is now used in hundreds of cities acrossat least 45 countries (MobilityData, 2020). Trip planning and Mobility as a Service (MaaS) applications use
these data to help travellers in choosing shared mobility, integrating public transport into the offer. In
addition, GBFS facilitates public authorities’ analysis and comparison of data generated by shared mobility
systems (MobilityData, 2021). In 2020, GBFS version 2.0 was launched to meet new needs, especiallyconcerning the protection of personal data. For example, the latest version recommends rotating theunique IDs of a vehicle once it has finished a trip to prevent the reconstruction of a trip made by a single,
potentially identifiable rider.
The Mobility Data Specification (MDS) is a set of APIs that helps public authorities manage transport better.
It standardises communication and data-sharing between mobility operators and public authorities.
Through it, authorities can share and validate policy digitally, facilitating vehicle management and better
outcomes for the population. In addition, it provides mobility service operators with a framework they canuse in new markets. (OMF, n.d.1)
MDS was initially developed for the City of Los Angeles, California in the United States. It is managed by
the Open Mobility Foundation (OMF). MDS’s initial purpose was to help cities monitor and manage
micromobility services. Cities have called for expanding the scope of MDS. The release of MDS
version 2.0.0 will cover additional mobility services – including ridesourcing and taxis (OMF, n.d.2).
At its core, MDS is a set of APIs (OMF, n.d.3). As described in Box 2, there are three primary sets of APIs
(agency, provider and policy) and three additional ones (geography, jurisdiction and metrics).
MDS is a very promising tool. However, there are concerns about the detail and granularity of the datacollected by the initial formulation of some APIs. As a result, OMF issued a privacy guide to help authorities
implement MDS, although it primarily focused on the US context (OMF, 2020). In December 2021, OMF
also released detailed guidance on using MDS in the context of the GDPR in Europe (OMF, n.d.4). MDS
now includes a “policy requirements” endpoint in the Policy API that enables public authorities to specify
exactly which data they require and at what level of aggregation to meet their data privacy concerns (OMF,
n.d.5).

44

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

COLLECTING DATA FOR NEW MOBILITY PERFORMANCE INDICATORS

Box 2. Mobility Data Specification application programming interfaces
The Mobility Data Specification (MDS) uses application programming interfaces (APIs). APIs areprotocols that allow data to flow securely between operators and authorities. MDS offers three primarysets of APIs:•

Agency API: enables mobility service operators to push real-time data to a database managed
and housed by the public authority (or a designated third party). It enables real-time datacollection and supports real-time analysis and adaptive regulation. It is implemented byregulatory agencies.

•

Provider API: is designed to enable public authorities to pull historical or real-time data fromthe mobility service operator. It allows authorities to directly manage their reporting flows
from operators and potentially reduce their data storage requirements. It is implemented bymobility service providers.

•

Policy API: allows regulatory agencies to publish geography-based regulations and service
providers to adjust their offers as policies change. It reduces the burdens of regulatory agencies
by removing the need to communicate changes manually to providers. It is implemented byregulatory agencies.

Three additional APIs support the core MDS APIs:•

Geography API: looks at spatial coverage and boundaries that define where the rules set out
in the Policy API apply or that trigger data logging and reporting in the Agency, Provider andMetrics APIs.

•

Jurisdiction API: is used to define and communicate hierarchical or overlapping administrativeor operational areas associated with specific data access rights.

•

Metrics API: establishes common methodologies for creating indicators from MDS data. This
API defines common indicator semantics and ensures that indicators are consistently being
calculated.

Source: OMF (n.d.3).

Public authorities and mobility operators should co-operate to define standardised data formats thatenable data reporting efficiencies in line with the need to monitor and guide policy. Convergence towards
standards is most important with each type of New Mobility service. In contrast, convergence around a
set of standards covering all mobility services – including new ones – is a longer-term goal.

Public-private agreements for reporting New Mobility data
The last aspect to consider for data reporting is which public-private agreement is best suited for privatecompanies reporting New Mobility data to public authorities. There are different possibilities that publicauthorities can apply. ITF (2016) identified five main models of data reporting: 1) public-private data
partnerships; 2) public-citizen data partnerships; 3) mandatory data sharing; 4) new data-sharing model;
and 5) open data. Based on this, the European Investment Bank (EIB) elaborated seven ways for reporting
data: 1) public procurement of data; 2) intermediaries; 3) financially compensated partnerships between

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

45

COLLECTING DATA FOR NEW MOBILITY PERFORMANCE INDICATORS

the public and private sectors; 4) in-kind partnerships between the public and private sectors;5) mandatory data sharing; 6) collaboration between authorities; and 7) crowdsourcing (EIB, 2021). In
addition, ITF (2021b) pinpointed four over-arching data acquisition models for gathering data generated
by private companies and needed by public authorities (see Box 3).

Box 3. Data acquisition methods
In the report Reporting Mobility Data: Good Governance Principles and Practices, the International
Transport Forum identified four data acquisition models:•Compulsion: The act of requiring or compelling the transfer of data from an individual or a firm
to a public authority is the strongest and most constraining method by which a public authority
may acquire data. This approach is generally deployed when the social benefits of acquiring
data from individuals or firms outweigh individual privacy or commercial sensitivities linked tothose data remaining out of government hands. Public authorities must ensure that personalor sensitive data are not released or used in ways not aligned with public policy objectives,
including protecting individual privacy and commercial competitiveness.

•

Conditionality: The act of requiring the transfer of data in order to access a service, a set ofrights, or a licence to operate is also a common data acquisition pathway for public authorities.
It is common in the transport sector to require an exchange of data in order to access an
outcome. This allows the public authority to monitor the conditions in which the outcome isdelivered (driver licencing, delivery of public transport contracts and granting mobility service
licences to operate).

•

Co-operation: The act of firms or individuals volunteering data to public authorities in order toobtain mutually beneficial outcomes is a less common data-acquisition pathway for publicauthorities. Firms may wish to share data with public authorities via voluntary agreements asa way of building trust and avoiding more constraining data-reporting requirements.

•

Commercial terms: Governments may also acquire data by purchasing it from data aggregatorsand processors. These data are typically obtained by private firms from a number of sources
and may include personal or sensitive data in its raw form. The data are aggregated,anonymised and processed to meet particular public authorities’ needs for which governments
are willing to pay.

Source: ITF (2021b).

Identifying one single best agreement under which private companies report data to public authorities isdifficult. Therefore, public authorities should choose the most appropriate agreement to request the data
based on their adopted indicators.
Public authorities should require compulsory reporting of all data necessary for priority indicators. For
example, a study conducted in 13 cities showed that the majority required the submission of trip data tomonitor and control the industry’s growth, improve safety, increase access, understand drivers’ working
conditions and effectively track the use of their public streets (Holm Møller and Simlett, 2020).
Another viable option for measuring the prioritised indicators is to make the data reporting a condition forreceiving the licence to operate in a jurisdiction. In 2019, Los Angeles, California, in the United States,
46

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

COLLECTING DATA FOR NEW MOBILITY PERFORMANCE INDICATORS

launched the Dockless Vehicle Pilot Program to regulate dockless modes (LADOT, 2020). There wereconcerns about public safety, accessibility, and right-of-way management linked to New Mobility services.
Among the requirements needed to apply to the programme, private companies had to maintain MDS
compliance and submit quarterly reports on usage rates of equity service options.
Public authorities can also decide to co-operate with the private sector. The city of Paris’ early engagementwith micromobility operators was an example of this type of co-operation. In 2019, the city established aworking group for all e-scooter stakeholders, inviting e-scooter operators to sign a Code of Good Conduct.
The Code provided a guiding principle for operating in the city and included a data-sharing section (Figg,
2022). Thereafter, however, the city choose a public tender-based model to select three operators. Under
the terms of the tender, data reporting to the city became compulsory for the selected operators.

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

47

REFERENCES

References
ADEME (2020), L’observatoire des mobilités emergentes (The Observatory of Emerging Mobility), Third
edition, L’Agence de la transition écologique (Agency for Ecological Transition), Montrouge,
https://librairie.ademe.fr/mobilite-et-transport/3793-observatoire-des-mobilites-emergentes-3emeedition-2020.html.
Beekman, D. (2020), “Will rental scooters serve all Seattle neighborhoods? Vendor missed equity target
with bikes last year”, The Seattle Times, https://www.seattletimes.com/seattle-news/transportation/willrental-scooters-serve-all-seattle-neighborhoods-vendor-missed-equity-target-with-bikes-last-year/(accessed 8 July 2022).
CEN (n.d.), “Standards Context”, NeTEx: Network Timetable Exchange, European Committee forStandardization, https://netex-cen.eu/?page_id=58 (accessed 12 December 2022).
CEN (2020), NeTEx extension for New Modes – Scope and overview, European Committee forStandardization, https://www.netex-cen.eu/wp-content/uploads/2021/03/NeTEx-extension-for-NewModes-Detailed-Scope-v04.pdf (accessed 12 December 2022).
Cho, J.-H., Y.-H. Seo and D.-K. Kim (2021), “Efficiency Comparison of Public Bike-Sharing Repositioning
Strategies Based on Predicted Demand Patterns”, Transportation Research Record, Vol. 2675/11,
pp. 104-118, https://doi.org/10.1177/03611981211016859.
City of Boulder (2019), Low-Stress Walk and Bike Network Plan, Boulder, https://bouldercolorado.gov/
media/884/download?inline.
City of Chicago (2021), “E-Scooter Pilot: Evaluation”, Chicago, https://www.chicago.gov/content/dam/city/depts/cdot/Misc/EScooters/2021/2020%20Chicago%20E-scooter%20Evaluation%20-%20Final.pdf.
CMAP (2019), “New Data Allows an Initial Look at Ride Hailing in Chicago”, Chicago Metropolitan Agency
for Planning, City of Chicago, https://www.cmap.illinois.gov/documents/10180/1005256/PU-0026_TNC+
POLICY.pdf/06def1f1-b0b1-0365-2386-2011dfb96fa9.
Cohen, A., S. Shaheen and E. Farrar (2021), “Urban Air Mobility: History, Ecosystem, Market Potential,
and Challenges”, IEEE Transport on Intelligent Transportation Systems, Vol. 22, no. 9, pp. 6 074-6 087,
doi: 10.1109/TITS.2021.3082767, https://escholarship.org/uc/item/8nh0s83q#main.
Cunha, M., R. Mendes and J.P. Vilela (2021), “A survey of privacy-preserving mechanisms forheterogeneous data types”, Computer Science Review, Vol. 41, 100403, Elsevier, https://doi.org/
10.1016/J.COSREV.2021.100403.
Cunningham, F. and M. Carlsson (2019), “What is New Moblity? Emerging sector brings about
tremendous possibilities and is about to change our ways of moving for good”, Bird&Bird,
https://www.twobirds.com/en/insights/2019/global/what-is-new-mobility-emerging-sector-bringsabout-tremendous-possibilities#:~:text=New%20Mobility%20is%20on%20everyone's,and%20using%
20the%20traffic%20infrastructure (accessed 8 July 2022).
De Maio, P. et al. (2021), “The Meddin Bike-sharing World Map”, https://bikesharingworldmap.com/#/all/2.1/0/48.88/ (accessed 8 July 2022).

48

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

REFERENCES

EIB (2021), EIB Technical Note on Data Sharing in Transport, European Investment Bank, Luxembourg,www.eib.org/attachments/publications/technical_note_on_data_sharing_in_transport_en.pdf.
Eltis (2019), “Sustainabale Urban Mobility Plan”, The Urban Mobility Observatory, Glossary,
https://www.eltis.org/glossary/sustainable-urban-mobility-plan (accessed 1 February 2023).
EU (2017), “Commission Delegated Regulation (EU) 2017.1926”, Journal of the European Union, L 272-1,
Vol. 60, European Union, https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32017R1926&rid=6#:~:text=matter%20and%20scope-,1.,transport%20network%20of%20the%20Union (accessed
12 December 2022).
EU (2016), Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on
the protection of natural persons with regard to the processing of personal data and on the freemovement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation),
https://eur-lex.europa.eu/eli/reg/2016/679/oj (accessed 12 December 2022).
Figg, H. (2022), “Local governance of micromobility – The Paris example”, Eltis, https://www.eltis.org/
resources/case-studies/local-governance-micromobility-paris-example (accessed 12 December 2022).
Figg, H. (2021), “New SUMP Topic guide on safe use of micromobility devices in urban areas”,
https://www.eltis.org/in-brief/news/new-sump-topic-guide-safe-use-micromobility-devices-urban-areas(accessed 1 February 2023).
Flores Dewey, O. (2019), “App-Based Collective Transport Service in Mexico City: A Start-Up Case Study”,
International Transport Forum Discussion Papers, OECD Publishing, Paris, https://www.itf-oecd.org/
sites/default/files/docs/app-based-collective-transport-mexico-city_0.pdf.
Fluctuo (2022), “European Shared Mobility Index 2022”, https://european-index.fluctuo.com/ (accessed
1 February 2023).
Foljanty, L. (2020), “On-Demand Ridepooling Market: 2020 Recap”, Medium, https://lukas-foljanty.
medium.com/on-demand-ridepooling-market-2020-recap-71a229f2e7b9 (accessed 8 July 2022).
FTA (2020), “Mobility Performance Metrics (MPM) for Integrated Mobility and Beyond”, United States
Federal Transit Administration, Research Report No. 0152, U.S. Department of Transportation,
Washington, D.C., https://www.transit.dot.gov/sites/fta.dot.gov/files/docs/research-innovation/147791/
mobility-performance-metrics-integrated-mobility-and-beyond-fta-report-no-0152.pdf.
Global Network for Popular Transport (2023), “What is the Global Network for Popular Transportation?”,Shared-Use Mobility Center, Chicago, https://www.populartransport.net/ (accessed 20 February 2023).
Grant Thornton, Mobility City and Fundación iberCaja (2021), “La nueva movilidad: revolución y desafíos”(New Mobility: Revolution and Challenges), https://www.grantthornton.es/perspectivas/tendencias/lanueva-movilidad-revolucion-y-desafios/ (accessed 8 July 2022).
Holm Møller, T. and Simlett, J. (2020), “Micromobility: moving cities into a sustainable future”, EY,
https://assets.ey.com/content/dam/ey-sites/ey-com/en_gl/topics/automotive-and-transportation/
automotive-transportation-pdfs/ey-micromobility-moving-cities-into-a-sustainable-future.pdf.
ITF (2022), Broadening Transport Appraisal: Summary and Conclusions, ITF Roundtable Reports, No. 188,
OECD Publishing, Paris, https://doi.org/10.1787/a0e2e0a6-en.
ITF (2021a), Micromobility, Equity and Sustainability: Summary and Conclusions, ITF Roundtable Reports,
No. 185, OECD Publishing, Paris, https://doi.org/10.1787/b71317cd-en.

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

49

REFERENCES

ITF (2021b), “Reporting Mobility Data: Good Governance Principles and Practices”, International
Transport Forum Policy Papers, No. 101, OECD Publishing, Paris, https://doi.org/10.1787/b988f411-en.
ITF (2021c), Big Data for Travel Demand Modelling: Summary and Conclusions, ITF Roundtable Reports,
No. 186, OECD Publishing, Paris, https://doi.org/10.1787/08378837-en.
ITF (2021d), “The Innovative Mobility Landscape: The Case of Mobility as a Service”, International
Transport Forum Policy Papers, No. 92, OECD Publishing, Paris, https://doi.org/10.1787/46903a00-en.
ITF (2020a), "Safe Micromobility", International Transport Forum Policy Papers, No. 85, OECD Publishing,Paris, https://doi.org/10.1787/0b98fac1-en.
ITF (2020b), "Good to Go? Assessing the Environmental Performance of New Mobility", International
Transport Forum Policy Papers, No. 86, OECD Publishing, Paris, https://doi.org/10.1787/f5cd236b-en.
ITF (2019), Regulating App-based Mobility Services: Summary and Conclusions, ITF Roundtable Reports,
No. 175, OECD Publishing, Paris, https://doi.org/10.1787/94d27a3a-en.
ITF (2018), “Safer Roads with Automated Vehicles?”, International Transport Forum Policy Papers, No. 55,
OECD Publishing, Paris, https://doi.org/10.1787/b2881ccb-en.
ITF (2017), "Shaping the Relationship Between Public Transport and Innovative Mobility", International
Transport Forum Policy Papers, No. 34, OECD Publishing, Paris, https://doi.org/10.1787/7a1f7b89-en.
ITF (2016), "Data-Driven Transport Policy", International Transport Forum Policy Papers, No. 20, OECD
Publishing, Paris, https://doi.org/10.1787/5jlwvz8g4vbs-en.
ITF (2013), Cycling, Health and Safety, ITF Research Reports, OECD Publishing, Paris, https://doi.org/
10.1787/9789282105955-en.
Joshi, M. et al. (2019), “E-Hail Regulation in Global Cities”, NYU Rudin Center for Transportation,
https://wagner.nyu.edu/files/faculty/publications/RUDIN_EHAIL_REPORT.pdf.
Kanygin, J. (2019), “Location data from shared e-scooters helps city map your movements”, CTV News,
Calgary, https://calgary.ctvnews.ca/location-data-from-shared-e-scooters-helps-city-map-yourmovements-1.4549929 (accessed 15 November 2022).
LADOT (2020), Year One Snapshop: A Review of the 2019-2020 Dockless Vehicle Pilot Program, Los
Angeles Department of Transportation, https://ladot.lacity.org/sites/default/files/documents/ladotdockless-year-one-report.pdf.
Lau, R. (2020), “No more shared e-scooters in Montreal because they weren’t being parked legally: city
officials”, CTV News, Montreal, https://montreal.ctvnews.ca/no-more-shared-e-scooters-in-montrealbecause-they-weren-t-being-parked-legally-city-officials-1.4818347 (accessed 15 November 2022).
MAPC (2018), Fare Choices: A Survey of Ride-Hailing Passengers in Metro Boston, Report #1, MAPC
Research Brief, Metropolitan Area Planning Council, https://www.mapc.org/wp-content/uploads/2018/02/Fare-Choices-MAPC.pdf.
MDC (2022), Mobility Data Collaborative, https://mdc.sae-itc.com/#work (accessed 12 December 2022).
Micromobility Industries (n.d.), “Micromobility Landscape”, https://micromobility.io/landscape (accessed
20 February 2023).
MobilityData (2021), “GBFS and Shared Mobility Data Policy for European Cities: Helping cities support
seamless and sustainable mobility options through GBFS”, https://mobilitydata.org/gbfs-and-sharedmobility-data-policy-in-europe/ (accessed 12 December 2022).
50

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

REFERENCES

MobilityData (2020), “What’s new in GBFS v2.0.”, Medium, https://mobilitydata.medium.com/whatsnew-in-gbfsv2-0-63eb46e6bdc4 (accessed 12 December 2022).
NABSA (2022), “3rd Annual Shared Micromobility State of the Industry Report”, North American Bike
Share Association, https://doi.org/10.7922/G2HD7T0P.
NACTO (2019), “Shared Mobility Data in the US: 2019”, National Association of City Transportation
Officials, https://nacto.org/Shared-Micromobility-2019/ (accessed 8 July 2022).
NUMO (2022), “New Mobility Atlas”, New Urban Mobility Alliance, https://www.numo.global/newmobility-atlas#2/22.9/19.5 (accessed 8 July 2022).
NYC TLC (2022), “Taxi and Ridehailing Usage in New York City”, https://toddwschneider.com/
dashboards/nyc-taxi-ridehailing-uber-lyft-data/ (accessed 1 February 2023).
OECD (2022), “Competition and Regulation in the Provision of Local Transportation Services”, OECD
Competition Policy Roundtable Background Note, www.oecd.org/daf/competition/competition-andregulation-in-the-provision-of-local-transportation-services-2022.pdf.
OECD (2013), Recommendation of the Council concerning Guidelines Governing the Protection of Privacy
and Transborder Flows of Personal Data, https://legalinstruments.oecd.org/en/instruments/OECDLEGAL-0188 (accessed 12 December 2022).
OMF (n.d.1), “About MDS”, Open Mobility Foundation, www.openmobilityfoundation.org/about-mds/(accessed 12 December 2022).
OMF (n.d.2), “Release 2.0.0”, Open Mobility Foundation, https://github.com/openmobilityfoundation/
governance/wiki/Release-2.0.0 (accessed12 December 2022).
OMF (n.d.3), “Understanding MDS APIs”, Open Mobility Foundation, https://github.com/openmobility
foundation/governance/blob/main/technical/Understanding-MDS-APIs.md (accessed
12 December 2022).
OMF (n.d.4), “Using MDS under GDPR”, Open Mobility Foundation, www.openmobilityfoundation.org/
using-mds-under-gdpr/ (accessed 12 December 2022).
OMF (n.d.5), “Mobility Data Specification: Policy”, Open Mobility Foundation, https://github.com/
openmobilityfoundation/mobility-data-specification/tree/main/policy#requirement (accessed
20 February 2023).
OMF (2020), Privacy Guide for Cities: Mobility data specification, Open Mobility Foundation,
https://www.swlaw.edu/sites/default/files/2022-03/OMF-MDS-Privacy-Guide-for-Cities.pdf.
OSLO (n.d.), “OSLO – Open Standards for Linked Organisations”, Joinup, European Commission,
https://joinup.ec.europa.eu/collection/oslo-open-standards-linked-organisations-0 (accessed
12 December 2022).
PBOT (2020), “New Mobility Snapshot 2019”, Portland Bureau of Transportation, Portland,
https://www.portland.gov/sites/default/files/2020-08/2019newmobilitysnapshot_pbot.pdf.
SAE (2021), “Taxonomy of On-demand and Shared Mobility: Ground, Aviation and Marine”, Surface
Vehicle/Aerospace Recommended Practice, JA3163, SAE International, Warrendale,
https://www.sae.org/standards/content/ja3163_202106/preview/.

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

51

REFERENCES

SAE (2019), “Taxonomy and Classification of Powered Micromobility Vehicles”, Surface Vehicle
Recommended Practice, J3194, SAE International, Warrendale, https://www.sae.org/standards/
content/j3194_201911/preview/.
SAE (2018), “Taxonomy and Definitions for Terms Related to Shared Mobility and Enabling
Technologies”, Surface Transportation Recommended Practice, J3163, SAE International, Warrendale,
https://www.sae.org/standards/content/j3163_201809/preview/.
SCAG (2019), Bike Share in Los Angeles County: An analysis of LA Metro Bike Share and Santa Monica
Breeze, Southern California Association of Governments, Los Angeles, https://scag.ca.gov/sites/main/f
iles/file-attachments/labikeshare_scag.pdf?1604261539.
SFCTA (2017), “TNCs Today: A Profile of San Francisco Transportation Network Company Activity”, San
Francisco County Transportation Authority, https://www.sfcta.org/sites/default/files/2019-02/TNCs_
Today_112917_0.pdf.
SFMTA (2017), On-Street Car Sharing Pilot Program, Evaluation Report, San Francisco Municipal
Transportation Agency, https://www.sfmta.com/sites/default/files/projects/2017/Carshare_
eval_final.pdf.
Shaheen, S. et al. (2019), “Shared Mobility Policy Playbook”, http://dx.doi.org/10.7922/G2QC01RW.
SHARE-North (2021), “Shared Mobility Rocks: A Planner’s Guide to the Shared Mobility Galaxy”,
https://share-north.eu/wp-content/uploads/2021/03/Shared-Mobility-Guide-Sneak-Preview.pdf.
Slowik, P. and F. Kamakaté (2017), “New Mobility: Today’s Technology and Policy Landscape”, White
Paper, International Council on Clean Transportation, https://theicct.org/sites/default/files/publications/New-mobility-landscape_ICCT-white-paper_27072017_vF.pdf
Stocker, A. and S. Shaheen (2017), “Shared Automated Vehicles: Review of Business Models”,
International Transport Forum Discussion Papers, OECD Publishing, Paris, https://www.itfoecd.org/sites/default/files/docs/shared-automated-vehicles-business-models.pdf.
TCCS (2021), “Review of Shared Micromobility”, Transport Canberra and City Services, Dickson,
https://www.transport.act.gov.au/__data/assets/pdf_file/0007/1882240/Micromobility-Review-FinalAccessible.pdf.
USDOT (n.d.), “Docked Bikeshare Ridership in the USA”, United States Department of Transport,
https://maps.dot.gov/BTS/Dockedbikeshare-COVID/ (accessed 8 July 2022).
Venkataram, P. and M. D’Agostino (2022), “The Controversy Over How to Regulate Wheelchair Service by
Uber and Lyft in California”, Streetsblog California, https://cal.streetsblog.org/2022/08/18/thecontroversy-over-how-to-regulate-wheelchair-service-by-uber-and-lyft-in-california/ (accessed
15 November 2022).
WEF (2022), “Principles for Reporting Emissions from Urban Mobility”, Briefing Paper, World Economic
Forum, Cologny, https://www3.weforum.org/docs/WEF_Principles_for_Reporting_Emissions_from_
Urban_Mobility_2022.pdf.
Wellington City Council (2020), “E-scooter survey results (wave 1 and 2)”, https://wellington.govt.nz//media/parking-roads-and-transport/parking-and-roads/smart-transport/files/survey-report.pdf?la=en&
hash=F4E576AFB8D4FEE3C2006B6FB8F0BAAD2D909866.

52

MEASURING NEW MOBILITY: DEFINITIONS, INDICATORS, DATA COLLECTION © OECD/ITF 2023

Measuring New Mobility
Definitions, Indicators, Data Collection

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
Funded by

Published: 05/2023 | Photo credit: © Cüneyt Sarvazlar/ShutterstockThis report proposes a comprehensive classification of “New Mobility”
services and vehicles that are changing the way people travel. It also
identifies performance indicators to help set the right policies as New
Mobility evolves. It sets out a framework to systematically collect andcompare New Mobility-related data. Finally, the report examines how
governments and private stakeholders can collaborate to improvetheir understanding of New Mobility and determine if and where policy
interventions are needed.