# Large cities are less efficient for sustainable transport: The ABC of
mobility

Rafael Prieto-Curiel
Complexity Science Hub
Josefstaedter Strasse 39
1080 Vienna, Austria
<EMAIL_ADDRESS>
&Juan P. Ospina
Research in Spatial Economics
Universidad EAFIT
Medellín, Antioquia, Colombia
<EMAIL_ADDRESS>

###### Abstract

The use of cars in cities has many negative impacts on its population,
including pollution, noise and the use of space. Yet, detecting factors that
reduce automobile dependency is a serious challenge, particularly across
different regions. Here we model the use of different modes of transport in a
city by aggregating active mobility (A), public transport (B) and cars (C),
thus expressing the modal share of a city by its ABC triplet. Data for nearly
800 cities across 60 countries is used to model car use and its relationship
with city size and income. Our findings suggest that outside the US, longer
distances experienced in large cities reduce the propensity of active mobility
and of cars, but public transport is more prominent. For cities in the US,
roughly 90% of its mobility depends on cars, regardless of city size. Further,
income is strongly related to automobile dependency. Results show that a city
with twice the income has 37% more journeys by car.

##  1 Introduction

Cars present significant challenges to cities due to their resource
requirements, lifestyle implications, and environmental impact. The increase
in the motorisation rate is a massive burden on cities, leading to issues such
as unsafe roads, noise, pollution, sedentary habits, inefficient use of public
space, and costly infrastructure primarily dedicated to parked cars [1, 2].
Additionally, transportation-related pollution claims the lives of thousands,
and road accidents annually result in the loss of 1.3 million lives,
disproportionately affecting pedestrians, cyclists, and motorcyclists [3].

Rapid urbanisation and car-centric infrastructure have resulted in sprawling
low-density suburbs, contributing to longer commuting distances and heightened
environmental pollution [4, 5]. Traffic congestion further exacerbates the
situation, partly influenced by private cars and other drivers [6, 7, 8, 9,
10]. The pathway towards sustainable mobility is quite challenging in cities
where most journeys are on private cars. Despite this, policymakers still
invest disproportionally in car infrastructure, inadvertently reinforcing
incentives for private vehicle usage [11, 12, 13, 14, 15, 16, 17].

Encouraging active mobility and public transport as alternatives to driving
presents numerous physical and environmental advantages. However, changing
travel behaviour is exceptionally challenging [15, 18, 19, 14, 20, 21, 13].
People often choose to drive due to factors such as infrastructure
availability, expected travel time, accessibility, comfort, and security [22,
23, 11, 24, 25, 26]. The choice of transportation mode significantly impacts
cities, potentially leading to either greener or more polluted environments.
In the US, driving accounts for approximately 75% of household-level CO2
emissions, while public transportation, residential heating, and electricity
consumption contribute to the remaining 25% [27]. And even with the growth of
the electric cars market, the burden associated with motorised mobility will
remain, including their manufacture, infrastructure demands, congestion,
particle pollution produced by tyre wear, and others [17].

Promoting sustainable transportation involves prioritising mobility beyond
cars, emphasising fewer and shorter trips, non-motorised modes like walking
and cycling, and shared transportation through public transit [28]. Active
mobility for short distances and public transport for longer distances are the
key answers to sustainable mobility [13]. Yet, active mobility and public
transport face significant barriers across cities of different sizes. Long
commuting distances in medium and large cities make active mobility
challenging. On the other hand, public transport requires sufficient passenger
volume to provide frequent service, and its availability is influenced by
population density and city size [29, 30, 31, 32, 33, 34]. Smaller cities
often lack public transport options, while medium and large cities discourage
active mobility due to long travel distances. City size strongly influences
transportation choices and the feasibility of different modes of transport.

City size is crucial in various aspects of social life and infrastructure.
Larger cities tend to attract more migrants and highly skilled individuals and
exhibit higher income, innovation, and crime rates [35, 36, 37, 38, 39, 40,
41]. Regarding mobility, larger cities experience more road accidents,
although they are less likely to be fatal [42]. However, determining how city
size affects modal share is still an open question. Describing the ideal
mobility for a city of a given size is not trivial. People tend to switch to
faster commuting methods which could be a private vehicle in most cases [43].

We compiled data for 797 cities across 62 countries to quantify modal share
across different cities. We used several sources, including the European
Platform on Mobility Management (EPOMM), Local Governments for Sustainability
(ICLEI), Deloitte City Mobility Index, World Conference on Transport Research,
and the US Census, among others [44, 45, 46, 47] (SM A). Most data used is
before the covid pandemic to avoid possible mobility shocks due to the
lockdown.

We analyse the impact of city size on transportation patterns, focusing on
comparisons within and outside the United States (US). While data limitations
and a bias toward European cities exist, our dataset includes nearly 100
observations from non-US and non-European cities. This provides insights into
modal shares and their influence on transport-related emissions globally. We
find similarities in modal shares across regions, such as Canadian cities
displaying similar car dependency to their US counterparts. Many Latin
American cities show a more balanced distribution between active mobility,
public transport, and driving. Active mobility is common in small cities
outside the US, while public transport is preferred in larger cities. In
contrast, car journeys dominate the US, regardless of city size. Additionally,
we highlight the direct impact of income on modal shares, with higher income
levels correlating with increased car usage and reduced reliance on public
transport.

##  2 Results

To simplify the analysis of transportation modes in various cities, we use the
ABC scheme, which categorises them as Active Mobility (A), Bus or Public
Transport (B), and Cars (C). For city $i$, we combine the proportions of
active mobility (including walking, cycling, etc.) into category $A_{i}$,
public transport journeys (including Metro, BRT, etc.) into category $B_{i}$,
and private motorised journeys (including cars, taxis, etc.) into category
$C_{i}$. These categories are represented on a ternary plot (Figure 1), such
that $A_{i}+B_{i}+C_{i}=100\%$ [48, 49]. This representation condenses diverse
transport modes into a triangle, with each corner representing a scenario
where everyone uses a single mode of transport [50, 43].

Figure 1: We aggregate different modes of transport into a category of Active
mobility, public transport and private vehicles. Then, the share of journeys
to work that are active mobility $A_{i}$ (walking, cycling, skating and
others) in the right of the triangle, on public transport $B_{i}$ (bus, BRT,
Metro, Tram and other shared systems) on the left, and by private cars $C_{i}$
(including taxis and mobility apps) on the bottom. Each dot in the ABC
triangle represents the modal share of a city. For each city, the level of
active mobility is observed on the right, buses on the left, and cars on the
bottom.

Comparing modal share across cities is challenging since it depends on the
definition of the urban area, the type of journeys, the different modes of
transport, and how multi-modal journeys are reported [28]. Our analysis
compares the modal share using data from 797 cities across 62 countries. We
find that 70% of the journeys are by car, 19% are walking or cycling (active
mobility), and 11% are by public transport. However, there are substantial
differences between regions (Figure 2). In USA and Canada, 95% of the journeys
are by car, and only 3% are active mobility. In Europe, active mobility is not
preferred since more than half of its trips are by car. In some metropolitan
areas like Greater Manchester, we find that 71% of their journeys are on a
private vehicle. Conversely, in African cities, nearly half of the trips are
active mobility.

Figure 2: ABC modal share for 797 cities (top) where the size of the disc is
proportional to the city’s population, and the colour corresponds to the
region. Density and contours of cities observed (bottom) in Europe and Central
Asia, USA and Canada and in Latin America.

Outside the US, city size has a significant influence on transportation
patterns. Approximately one-third of all journeys are attributed to active
mobility, such as walking or cycling. However, in cities with populations
exceeding one million, longer distances discourage people from choosing these
modes of transport. Active mobility and car journeys are common in small
cities, while public transport options are limited. However, as cities grow,
there is a notable shift towards public transport as the primary mode of
transportation (Figure 3). For cities with fewer than 100,000 inhabitants,
public transport accounts for approximately 10% of journeys. This percentage
increases to around 25% for cities with around one million people and
surpasses 40% for cities with over 20 million inhabitants.

Figure 3: Left - Impact of city size on modal share for cities outside the US
(top) and inside the US (bottom). Right - Impact of city size across the ABC
model for cities between 20,000 and 40 million inhabitants.

In contrast, the US exhibits minimal variation in modal share across cities of
different sizes. Regardless of city size, approximately 95% of mobility relies
on private vehicles. There is a slight trade-off in the remaining 5%, with a
slightly higher proportion of active mobility in small cities and public
transport in larger cities. The majority of cities in the US have been
designed with a strong reliance on cars for transportation. While cities like
New York City and Boulder have developed alternative mobility options, most
cities in the US heavily depend on vehicles. Surprisingly, statistics show
that regardless of city size, individuals in the US have an equal likelihood
of driving. Approximately 90% of the population drives, irrespective of
whether they reside in a small or large city. The remaining 10% predominantly
consists of active mobility for smaller cities and public transport for larger
cities.

Walking as a mode of transport works better in small cities, but as distances
increase, walking is less frequent. For cities outside the US, we estimate
that when it doubles its size, the probability that a person walks as a mode
of transport is reduced by 5%. Something similar happens with cycling,
although it is even more sensitive to large distances. When a city doubles its
size, the probability that a person cycles drops 11% (see SM B). For cities in
the US, walking and cycling are very limited, but they decrease as cities
double in size by 6% and 3%, respectively.

High income is correlated with car dependency (Figure 4). As income rises, the
frequency of active mobility and public transport decreases. Our analysis
reveals a relationship described as $C_{i}\propto Y_{i}^{\gamma_{C}}$, where
$C_{i}$ represents car journeys and $Y_{i}$ represents the income of a city.
The exponent $\gamma_{C}$ is estimated to be $0.455\pm 0.018$, which implies
that a city with twice the income (holding other factors constant) is
associated with a 37% increase in car journeys, primarily reducing the number
of public transport journeys (SM C).

Figure 4: Impact of income on the modal share distribution of a city. Each
disc represents a city, and its colour represents the income per person at the
country level in 2021. The curve represents the predicted modal share of
cities with varying incomes.

##  3 Discussion

By grouping various forms of active mobility, public transport, and private
cars into three overarching categories, we can effectively capture the modal
share of cities using the ABC scheme. This representation allows us to observe
distinct variations in mobility patterns across different cities and
countries. While certain cities, notably in the US, heavily depend on private
cars, alternative modes of transportation are more widely embraced in other
regions.

Other studies comparing the modal share across cities rely on less than 50
metropolitan areas [28, 51, 52]. Our novel data set includes information on
the modal share of 797 cities worldwide. However, this data needs to be more
representative regarding geographical distribution or city size. Most
observations are from Europe and the USA, while data from middle and low-
income countries is limited. Obtaining data on modal share is challenging, as
it is only available for a subset of cities. Additionally, comparability among
the data poses a challenge, as some surveys capture specific types of
motorised mobility (such as motorbikes, cycling, and scooters). In contrast,
others provide aggregate categories, which makes it difficult to separate and
analyse other attributes of urban mobility.

City size plays a crucial role in shaping transportation patterns and choices.
However, it is worth noting that mobility patterns are also affected by the
design and layout of cities. In larger cities, active mobility options
(walking or cycling) can be less attractive due to longer distances, yet,
driving also faces increased costs. Our results show that as a city’s
population doubles in non-US cities, the likelihood of individuals choosing
active mobility decreases by 10%, and the probability of using a private car
decreases by 7%. However, public transport compensates for these challenges.
As cities grow, the probability of using public transport increases by 18%.
Across cities of varying sizes in the US, there is minimal variation in the
modal share of transportation. Approximately 95% of mobility in the US is
dominated by private vehicle usage, regardless of the city’s size. The
remaining 5% represents a minor trade-off, with small cities showing a
slightly higher proportion of active mobility and larger cities relying more
on public transport.

Income level also influences transportation choices. Additionally, when a
city’s income doubles, more car journeys are expected, primarily at the
expense of public transport usage. However, compelling examples around the
globe illustrate how income and reliance on automobiles can be decoupled. In
cities such as Zurich, Copenhagen, Tokyo, and Amsterdam, diverse
transportation options, including cycling paths, tramways, underground
railways, buses, and pedestrian pathways, intertwine with the urban landscape.
These cities serve as shining models where the wealthiest and most
economically vibrant urban centres are at the forefront of reshaping
transportation infrastructure. Their objective is to foster neighbourhoods
prioritising safety, well-being, and a more enjoyable living environment for
their inhabitants.

The current balance between cars, public transport, and active mobility in
certain cities does not guarantee a sustainable future, nor does it imply that
cities are progressing in the right direction. For instance, in cities such as
Mexico City or Bogota, the public transport system is overwhelmed,
uncomfortable, and lacks security, leading to an estimated one in five
individuals opting for private cars if they could afford them [17, 53, 54, 55,
56, 57]. The primary cause of having a small car dependency in many cities is
income rather than the availability of favourable conditions for sustainable
mobility. Many public transport users have no alternative options for travel,
making them captive riders [32, 58]. Therefore, achieving sustainable mobility
involves promoting more active mobility and public transport in car-dominated
cities while reducing the attractiveness of driving as a mode of
transportation [28].

Our findings reinforce that our cities must adequately address their
transportation-related challenges. As the repercussions of climate change
continue to inflict harm upon our planet and its inhabitants, urban
environments centred around oversized, single-occupant vehicles appear
increasingly out of sync with our evolving needs. It is increasingly evident
that a fundamental shift in approach is required. Current strategies and
practices must overcome these urban challenges. By recognising the need for a
paradigm shift, we can embark on a transformative journey to develop
innovative solutions and embrace new approaches to transportation that can
effectively tackle these pressing issues.

By promoting a diverse transportation ecosystem, cities can improve mobility
options, reduce congestion, enhance accessibility, and create more vibrant and
livable urban environments for their residents. An ever-growing body of
evidence highlights the potential environmental and public health benefits of
reducing car dependency. This shift encompasses a reduction in pollution and
an increase in active modes of transportation, such as walking, biking, and
other physical activities. By embracing alternative means of transportation,
we can foster a healthier and more sustainable future for ourselves and our
planet.

##  4 Methods

In the ABC scheme, we assign individuals’ city trips to Active, Bus, or Car
categories. This allocation is based on the assumption that everyone travels
and can be categorised accordingly. Let $P_{i}$ be the population of the city
$i$, and let $A_{i}$ be the proportion of active mobility journeys, including
walking, cycling, and others, $B_{i}$ the proportion of Bus journeys,
including other types of public transport, such as Tram, Metro and different
public transport journeys and let $C_{i}$ be the proportion of car journeys in
the city $i$ including private car drivers, taxis and others. Multimodal
journeys are assigned first to the car and then to public transport categories
if either is used for some part of the journey. We model the weekly travel
intensity, $\mu$, so that a person expects to make $\mu$ journeys weekly. The
modal share distribution is determined by the probabilities corresponding to
each city’s modal share. Hence, the expected number of car journeys for a
person in city $i$ is $\mu P_{i}C_{i}$, and the same applies to active
mobility and public transport.

We investigate the influence of city size on the modal share distribution by
expressing the frequency of active mobility journeys as $\mu P_{i}A_{i}\propto
P_{i}^{\beta_{A}}$, for bus journeys as $\mu P_{i}B_{i}\propto
P_{i}^{\beta_{B}}$ and for car journeys as $\mu P_{i}C_{i}\propto
P_{i}^{\beta_{C}}$. This implies that the probability of an individual in the
city $i$ choosing active mobility is proportional to $A_{i}\propto
P_{i}^{\beta_{A}-1}$, and this probability may vary across different city
sizes. If we obtain that $\beta_{A}=1$, then the probability of selecting
active mobility would be the same across cities of various sizes. With
$\beta_{A}>1$, active mobility would be more prevalent in larger cities, while
$\beta_{A}<1$ indicates a lower frequency of active mobility in large cities.
Although conventional mobility surveys only consider people older than five
years, we consider the total number of people living in a city $i$ for our
estimations.

Data for 797 metropolitan areas is used to calibrate the parameters. For some
cities, we have more than one year of data available, up to seven years for
Graz and six for Vienna. We use the latest observation for the 797 cities to
estimate the values of the parameters (Table 1). We then subdivide the results
by comparing 454 cities outside the US and 343 cities in the US. Comparing two
cities $C_{1}$ and $C_{2}$, where $P_{1}=2P_{2}$, so that city $C_{1}$ has
twice the population of city $C_{2}$, we get that the frequency of car
journeys decreases 5% in the large city, and also active mobility is reduced
by 2%, but they compensate with an increase in 18% in the frequency of public
transport journeys. Thus, the probability that a person uses public transport
increases by 18% for a city with twice the population.

| 797 cities | 454 non-US cities | 343 US cities
---|---|---|---
Mode | $\alpha$ | $\beta$ | $\alpha$ | $\beta$ | $\alpha$ | $\beta$
$A$ \- active | $0.6389$ | $0.8528$ | $0.6666$ | $0.9289$ | $0.0408$ | $0.9646$
| $(0.3188)$ | $(0.0319)$ | $(0.3327)$ | $(0.0153)$ | $(0.0203)$ | $(0.0307)$
$B$ \- bus | $0.0039$ | $1.1737$ | $0.0071$ | $1.2437$ | $1.4e-5$ | $1.4645$
| $(0.0019)$ | $(0.0483)$ | $(0.0035)$ | $(0.0167)$ | $(7.1e-6)$ | $(0.0669)$
$C$ \- car | $1.1444$ | $0.9532$ | $1.5371$ | $0.9043$ | $1.0203$ | $0.9949$
| $(0.5711)$ | $(0.0118)$ | $(0.7671)$ | $(0.0113)$ | $(0.5092)$ | $(0.0018)$
Table 1: Parameters estimated for the number of $A$ Active journeys, $B$ bus
journeys, and $C$ car journeys.

###  4.1 The ABC data and its comparability

Modal share data is typically gathered through travel surveys, which primarily
focus on the primary mode of transportation a person employs for each trip
during a typical weekday. These surveys may differentiate between car drivers,
car passengers (including taxi passengers), and motorbike riders. However, in
some cases, surveys do not provide distinctions between different types of
private vehicles. To ensure comparability, we combine all private motorised
trips into a category referred to as “Car" journeys. Similarly, public
transport surveys may encompass various modes such as metro, tram, bus, and
other forms of public transportation. To facilitate uniformity, we aggregate
all types of public transport into a single category known as “Bus". Active
mobility encompasses a range of physical activities, including walking,
cycling, electric bicycles, skating, rollerblading, and more. To consolidate
the various forms of active mobility, we combine them into a category called
“Active mobility". By reducing transportation modes into these broad
categories, we create a standardised framework for analysing and comparing
modal share data across cities and regions.

The definition of cities or metropolitan areas is crucial in determining many
urban indicators and also the mobility types observed across different
locations [59]. Surveys that rely on spatial or political boundaries to define
cities can introduce spatial biases into the data. To mitigate this, we focus
on urban areas and their respective populations as the primary observation
units. This approach allows us to exclude observations that do not precisely
align with a specific metropolitan area, ensuring greater accuracy and
consistency in our analysis.

The available data for analysis is not consistently aligned in terms of time.
Travel surveys are conducted only occasionally for most cities, which presents
a challenge regarding temporal consistency. To address this, we employ the
most recent data for each city in our analysis. To avoid any disruptions
caused by the COVID-19 lockdowns, we specifically consider data from 2019 or
earlier for US cities. Overall, our compiled dataset includes information from
797 cities. In 76% of these cities, the data is from 2010 or more recent, and
in 93% of the cities, the data is from 2005 or more recent. Despite the
temporal misalignment, we assume that changes in the modal share at the city
level occur gradually, allowing for meaningful analysis. Consequently,
variations observed in the modal share across cities are attributed to
underlying structural and social differences rather than temporal or other
survey-specific discrepancies.

The modal split analysis relies on data primarily sourced from reputable
organisations such as the European Platform on Mobility Management (EPOMM),
Deloitte City Mobility Index, World Conference on Transport Research, US
Census, EcoMobility Alliance Cities, TUMI’s Global Mobility Challenge 2018
Winners, EcoLogistics project cities, and CitiesSHIFT project cities. However,
the data quality may vary due to changes in definitions and data collection
methods across different cities. In some cases, actual data may be available,
while in others, estimates based on alternative methodologies may be utilised.

To represent the modal share of a city, we assign fractions to different types
of journeys: active mobility ($A_{i}$), bus journeys ($B_{i}$), and car
journeys ($C_{i}$). These fractions are constrained to the range $[0,1]$,
ensuring that their sum equals 100%. This data structure allows us to
visualise the modal share of a city as a triplet $(A_{i},B_{i},C_{i})$. We
display the modal share on a two-dimensional triangle known as a 2-simplex by
utilising ternary data representation [48, 49]. It provides a visual framework
to understand the relative proportions of active mobility, bus journeys, and
car journeys within a city.

###  4.2 Parameter estimation

In the method to estimate the scaling relationship between city size and the
modal share of a city, we assume that each person takes $\mu$ journeys per
week and that the probability of choosing some transport mode corresponds to
the city’s modal share. Thus, we estimate that on a week, a city with $P_{i}$
individuals expects $\mu P_{i}A_{i}$ active mobility journeys, $\mu
P_{i}B_{i}$ public transport trips, and $\mu P_{i}C_{i}$ car journeys. These
values correspond to the city’s expected weekly $A$, $B$, and $C$ journeys.
Then, we express the number of active mobility journeys as
$\alpha_{A}P_{i}^{\beta_{A}}$ for some values of $\alpha_{A}$ and $\beta_{A}$
and similarly for public transport and car journeys. The values and standard
error of $\alpha_{A}$ and $\beta_{A}$ are estimated through a regression
(Figure 5).

Figure 5: Public transport journeys (vertical axis) vs population (horizontal
axis). The results of the coefficients are obtained for all cities (model
displayed) and by considering only cities outside and inside the US.

To estimate the impact of income, we consider the GDP per person, compiled by
the World Bank for 2021. We then fit the regression $\log
C_{i}=\alpha+\beta\log Y_{i}$, where $Y_{i}$ corresponds to the income per
person in that country (expressed in current US dollars). Based on that
equation, we obtain that the car share in city $i$ can be expressed as

$C_{i}=0.005Y_{i}^{0.455},$ (1)

meaning that with a higher income, more car journeys are expected. Similarly,
we express the number of active mobility journeys as $A_{i}\propto
Y_{i}^{-0.847}$, and the number of public transport journeys as $B_{i}\propto
Y_{i}^{-1.435}$, meaning that with more income, fewer active mobility and
public transport journeys are expected.

##  5 Supplementary Materials

### A - List of sources

Table 2: List of sources, number of cities included on each source, and references. Source | #_cities | Reference
---|---|---
2011 Transport characteristics Survey | 1 | [60]
ABS census data 2016 journey to work | 6 | [46]
Background to the Transport Evidence informing the Ipswich Local Plan | 2 | [61]
Beijing Yearbook 2011 | 1 | [62]
Bonn - Modal split | 1 | [63]
Census of India 2011 | 4 | [64]
Census Profile Canada | 11 | [65]
Central Bureau of Statistics - The Netherlands | 1 | [66]
Cerema | 1 | [67]
Change trends in the use of passenger cars on urban trips: car-pooling in Gdynia | 1 | [68]
Civitas | 9 | [69]
CODATU | 1 | [70]
Cycling Academics. Bringing science on cycling to practice and back | 1 | [71]
Darmstadt - Modal split | 1 | [72]
Dataplatform - The Netherlands | 3 | [73]
Deloitte City Mobility Index | 4 | [74]
Dhaka mobility study | 1 | [75]
Edinburgh City Mobility Plan-2030 | 1 | [76]
ELTIS - The Urban Mobility Observatory | 4 | [77]
EOD Bogotá | 2 | [78]
Erfurt.de – das offizielle Stadtportal | 3 | [79]
Ergebnis der Verkehrserhebung 2012 | 2 | [80]
Fürth - Modal split | 1 | [81]
Global BRT data | 4 | [82]
GPSM_Discover Leipzig by Sustainable Transport | 2 | [83]
Grundsatzbeschluss zur Förderung des Radverkehrs in der Stadt Fürth | 1 | [84]
Haushaltsbefragung zum Mobilitätsverhalten der Dortmunder Bevölkerung 2013 | 1 | [85]
Haushaltsbefragung zum Mobilitätsverhalten in Essen 2019 | 2 | [86]
How did Bicycle Share Increase in Vitoria-Gasteiz | 3 | [87]
Korea National Transportation DB Report 2013 | 1 | [88]
Leeds Transport Strategy | 1 | [89]
Liverpool City Region Household Travel Survey | 3 | [90]
Local Governments for Sustainability - ICLEI | 33 | [45]
Mobilität in Deutschland MiD | 1 | [91]
Mobilität in Deutschland 2017 | 2 | [92]
Mobilität in Nürnberg | 1 | [93]
Mobilitätsbefragung 2020 Stadt Wuppertal | 2 | [94]
Mobilitätsbefragung: Verkehrswende nimmt Fahrt auf | 2 | [95]
Mobilitätserhebung Vorarlberg 2017 | 3 | [96]
MOBILITEITSVISIE ALMERE 2020-2030 | 1 | [97]
Mobility Minsk | 1 | [98]
Mobility survey AMVA 2012 | 1 | [99]
Mobility survey AMVA 2017 | 1 | [100]
Mobility survey Manizales 2017 | 1 | [101]
Mobility survey Montevideo | 1 | [102]
Mobility survey Santiago | 1 | [103]
Mobility survey São Paulo | 1 | [104]
Mobility survey Valle de Mexico | 1 | [105]
Monografia UFRJ | 1 | [106]
Newcastle Empower | 1 | [107]
Nürnberg Modal split | 1 | [108]
NZ Census 2018 | 3 | [109]
Oberhausen - Mobilitätskarte | 1 | [110]
Observatorio de Movilidad de Bogotá | 1 | [78]
Offene Daten Konstanz | 3 | [111]
Osaka City Hall | 1 | [112]
Passenger Transport Mode Shares in World Cities | 1 | [113]
Past, Present and Future Mobility challenges and opportunities in Bucharest | 1 | [114]
Piling up or Packaging Policies? An Ex-Post Analysis of Modal Shift in Four Cities | 1 | [115]
Plan de Déplacements Urbains d’Annemasse Agglo 2014 | 1 | [116]
PLAN DE DÉPLACEMENTS URBAINS. AMIENS MÉTROPOLE. 2013 - 2023 | 1 | [117]
POLIS - Cities and regions for transport innovation | 1 | [118]
Quantifying dimensions of Transportation Diversity: A city-based comparative approach | 5 | [47]
Realities of Modal Choice in Kuala Lumpur: Transport Planning for the Disadvantaged | 1 | [119]
Regeneration strategies and transport improvement in a deprived area | 1 | [120]
Regional Travel Survey of Jyväskylä 2009 | 1 | [121]
San Sebastián-Donostia. Calidad urbana: El espacio recuperado | 1 | [122]
Seoul | 1 | [123]
Shanghai Yearbook 2009 | 1 | [124]
Sheffield transport data | 3 | [125]
Städtische Mobilität im Vergleich | 2 | [126]
Taipei Yearbook 2010, Taipei City Government | 1 | [127]
The effect of Public Transport Investment on car ownership | 4 | [128]
The European Platform on Mobility Management | 405 | [44]
The Metropolitan Mobility Observatory of Spain | 2 | [129]
The Potential of cycling for Sustainable Mobility in Metropolitan Regions | 2 | [130]
Tokyo Statistical Yearbook 2009, Japan | 1 | [131]
Traffic behavior in Augsburg | 1 | [132]
Transforming Travel in Watford: the strategy for 2021-2041 | 1 | [133]
Travel Survey 2011, Land Transport Authority, Singapore | 1 | [134]
Urban Audit | 63 | [135]
Urban structure and sustainable modes’ | 1 | [136]
US CENSUS DATA - Commuting | 343 | [137]
User Perceptions and Attitudes on Sustainable Urban Transport among Young Adults | 1 | [138]
Utrecht Mobiliteitsplan 2040 | 2 | [139]
Wiesbadener Stadtanalysen | 1 | [140]
World Bank - Sofia-Bulgaria | 1 | [141]

### B - Walking and cycling in cities

For 712 out of the 797 cities in the database, we can distinguish the
percentage of journeys that are walking and the percentage that are cycling.
For the remaining 85 cities, walking, cycling, and other active mobility modes
are reported as a single mode, so it is impossible to distinguish between
them, and they are dropped from this analysis. Here we explore the scaling
impact of city size on the probability that a person decides to walk or that a
person chooses to cycle. We divide between cities outside the US and cities
within the US for the analysis (Figure 6).

Figure 6: Impact of city size (horizontal axis) in the probability of
selecting active mobility as a mode of transport (vertical axis). The red line
corresponds to the modelled probability that a person walks, and the blue line
corresponds to the probability that a person cycles. Thick lines correspond to
the model outside the US, and thin lines to the model within US cities.

A person’s probability of walking or cycling in a city is lower in bigger
cities. However, for cities outside the US, we observe that cycling is more
sensitive to city size, so the probability that a person travels by bicycle is
smaller in larger cities. This is observed since
$\beta_{Y}=0.8349<0.9409=\beta_{W}$. Further, comparing cities of similar
size, we observe that it is seven to eight times more likely that a person
decides to walk if they are outside the US than if they are within the US.
Similarly, a person is between eight and 14 times more likely to use a bicycle
if the person is outside the US than if the person is within the US.

| non-US cities | US cities
---|---|---
Mode | $\alpha$ | $\beta$ | $\alpha$ | $\beta$
$W$ \- walking | $0.4011$ | $0.9409$ | $0.0707$ | $0.9162$
| $(0.0988)$ | $(0.0176)$ | $(0.3327)$ | $(0.0310)$
$Y$ \- cycling | $0.4006$ | $0.8349$ | $0.0066$ | $0.9568$
| $(0.2093)$ | $(0.0336)$ | $(0.0066)$ | $(0.0466)$
Table 3: Parameters estimated for the number of $W$ walking journeys, and $Y$
cycling journeys.

### C - Modal share and income

The relationship between income and modal share is computed by fitting
$C_{i}\propto Y_{i}^{\gamma_{C}}$, where $C_{i}$ represents car journeys and
$Y_{i}$ represents the income of a city. A similar model is applied to the
relationship between income and active mobility (with $\gamma_{A}$) and public
transport (with $\gamma_{B}$). Results are shown in Table 4.

Mode | $\delta$ | $\gamma$
---|---|---
$A$ \- active | 901.5 | -1.435153
| (776.2) | (0.08349467)
$B$ \- public transport | 177784.2 | -1.435153
| (259500) | 0.08349467
$C$ \- cars | 0.004784346 | 0.4545897
| 0.001005238 | 0.01769237
Table 4: Impact of income on the modal share of cities.

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