ArticlePDF Available

The Social Cost of Automobility, Cycling and Walking in the European Union

  • Choice Behavior Institute, Daejeon South Korea


Cost-benefit-analyses (CBA) are widely used to assess transport projects. Comparing various CBA frameworks, this paper concludes that the range of parameters considered in EU transport CBA is limited. A comprehensive list of criteria is presented, and unit costs identified. These are used to calculate the external and private cost of automobility, cycling and walking in the European Union. Results suggest that each kilometer driven by car incurs an external cost of €0.11, while cycling and walking represent benefits of €0.18 and €0.37 per kilometer. Extrapolated to the total number of passenger kilometers driven, cycled or walked in the European Union, the cost of automobility is about €500 billion per year. Due to positive health effects, cycling is an external benefit worth €24 billion per year and walking €66 billion per year. CBA frameworks in the EU should be widened to better include the full range of externalities, and, where feasible, be used comparatively to better understand the consequences of different transport investment decisions.
Please cite as: Gössling, S., Choi, A., Dekker, K. and Metzler, D. 2018. The social cost of
automobility, cycling and walking in the European Union. Ecological Economics 158: 65-
Cost-benefit-analyses (CBA) are widely used to assess transport projects. Comparing various
CBA frameworks, this paper concludes that the range of parameters considered in EU
transport CBA is limited. A comprehensive list of criteria is presented, and unit costs
identified. These are used to calculate the external and private cost of automobility, cycling
and walking in the European Union. Results suggest that each kilometer driven by car incurs
an external cost of €0.11, while cycling and walking represent benefits of €0.18 and €0.37 per
kilometer. Extrapolated to the total number of passenger kilometers driven, cycled or walked
in the European Union, the cost of automobility is about €500 billion per year. Due to positive
health effects, cycling is an external benefit worth €24 billion/year and walking €66
billion/year. CBA frameworks in the EU should be widened to better include the full range of
externalities, and, where feasible, be used comparatively to better understand the
consequences of different transport investment decisions.
1. Introduction
Transport systems need to change in very significant ways to become aligned with the UN
Sustainable Development Goals (Creutzig et al. 2015; The Lancet 2017; UNFCCC 2015;
WHO 2011, 2016). To reduce levels of local air pollution, accidents, and congestion is a long-
standing policy goal in the European Union (EU) (EC 2011). Negative externalities of
transportation congregate in cities, with a widely held consensus that these can only be
resolved on the basis of new urban transport cultures in which cycling and walking have to
perform important roles (Aldred 2013; Hall et al. 2017; Pucher & Buehler 2017). Only where
the role of the car declines is it realistic to reduce traffic density and air pollution, even in a
scenario where electric, autonomous automobility diminishes noise levels and collision risks
(Zuurbier et al. 2010).
In European cities, cycling and walking are becoming increasingly more common (Hall et al.
2017; Pucher and Buehler 2017). These transport modes can replace trips by car, specifically
in cities, where a majority of trips are short (Blickstein & Hanson 2001). Evidence suggests
that cycling levels increase where physically separated cycle tracks have been built (Frondel
& Vance 2017), where trips are short, and where safe routes to school exist. In contrast,
perceived traffic dangers, exposure to exhaust and noise, or longer trip distances all represent
barriers to cycling (Fraser & Lock 2010; Gössling et al. 2018). As a result, cities seeking to
increase cyclist numbers need to redesign urban environments (Buehler et al. 2017; Forsyth &
Krizek 2011; Larsen et al. 2013), as bicycle cultures will only evolve where the concerns and
expectations of cyclists regarding safety, speed, and comfort are taken into consideration
(Aldred 2013). These insights also apply to walking, with ‘walkable’ environments being
defined as traversable, compact, physically enticing, and safe (Forsyth 2015). Apart from the
politically difficult decision to treat cyclists and pedestrians preferentially in traffic, the
greatest barrier to urban redesign is the issue of costs (Gössling & Choi 2015). This assigns
critical importance to cost-benefit analyses (CBA), which guide decision making in all major
transport construction projects.
CBA involves the assessment of potential impacts of a policy across a specific time horizon,
their monetary valuation, and the comparison of net benefits and costs (Hanley & Spash,
1993). Inclusion of negative externalities in the CBA can highlight ‘hidden’ cost issues
(Bithas, 2011). This is never straightforward, as the selection of aspects to include in the
CBA, as well as their valuation, is influenced by the ideological orientation of the actors
involved in the analysis (Söderbaum, 2007). Yet, where the selection of analysis criteria is
stated explicitly, in particular comparative approaches to transport CBA can contribute to
greater consistency and transparency, providing a more informed basis for decision-making
(Gössling et al. 2015).
Given the cost of implementing new transport infrastructure (Hutton, 2013; Meschik, 2012),
as well as the importance assigned to the various transport modes in contemporary city
planning, various studies have addressed the cost associated with in particular the car (Becker
et al., 2012; CE Delft et al., 2011; Hopkinson & Wardman, 1996; Ortuzar et al., 2000; Krizek,
2007; Meschik, 2012; Rabl and de Nazelle, 2012; Rank et al., 2001). However, to date, a
comparative CBA framework to juxtapose the cost of different transport modes appears to
only be used in Copenhagen (COWI & City of Copenhagen 2009). Against this background,
the purpose of this paper is to review CBA frameworks for transport infrastructure
development, to discuss the comprehensiveness of the parameters included, and to develop a
comparative CBA framework for car, bicycle, and walking, along with unit cost estimates.
2. Cost-benefit analysis and its use in transport contexts
Traffic infrastructure development commonly relies on CBA to guide investment decisions in
public spending contexts (e.g. Boardman et al., 2010; Hanley & Spash, 1993). The use of
CBA implicates that monetary value is assigned to the advantages and disadvantages of a
project, which results in a net cost or benefit to society. Decisions regarding the desirability of
specific investments become more transparent, as CBA helps to determine whether an
investment is economically sound, or whether an alternative project is more favorable. CBA
can consequently be used to justify projects economically, or to rank projects by assigning
priorities (Transportation Economics 2017). However, CBA as a decision-making tool also
has weaknesses, such as the subjective choice of items to be included in the analysis; the
allocation of monetary values (unit costs), for which there may be no market values; as well
as the identification of appropriate time horizons, spanning generations. Further difficulties
arise out of value incommensurability and issues of fairness (Bithas, 2011; Hanley & Spash,
1993). There is consequently a risk that a CBA process is reductionist, valuing impacts only in
economic terms, while lacking transparency and public participation. Weaknesses also include
that CBA may fail to adequately represent effects outside markets or double count effects
(Annema et al., 2007).
Despite these limitations, CBA is a commonly employed and widely accepted economic tool
in transportation contexts, specifically investments in infrastructure (e.g. EC, 2014; for critical
discussions see Bithas, 2011; Hutton, 2013; Parks & Gowdy, 2013). CBA is not a
comprehensive solution to understanding a project’s impacts (Hanley & Spash, 1993).
However, where it is used as a component in the decision-making process that is aligned with
stated policy and developed with input from a range of stakeholders and the public, its
weaknesses can be mitigated (Söderbaum, 2015). Where CBA is used in non-traditional ways,
such as the comparison of different transport modes, its outcomes may provide entirely new
perspectives on investment decisions in transport contexts (Gössling & Choi, 2015).
Existing CBA frameworks have been based on different variables, as well as economic values
assigned to these variables (Grant-Muller et al., 2001). Given the importance of CBA for
transport projects (Annema et al., 2007; EC, 2014; Hutton, 2013; Knudsen & Rich, 2013),
guidebooks for transport planners now seek to streamline CBA methodologies. For example,
the European Commission (EC, 2014) published a guide to CBA of investment projects, as it
“promotes the use of cost-benefit analyses for major infrastructure projects above €50
million” (ibid.: 11), of which over five hundred are expected to be implemented over the
period 2014-2020 in the EU. Investment priorities include projects that support a “Single
European Transport Area” in the trans-European transport network (TEN-T), projects that
enhance regional mobility, and those that “develop and improve environmentally-friendly and
low-carbon transport systems” (ibid.: 77). An important part of EU CBA decisions is based on
demand analyses, i.e. the forecasting of traffic volumes, and the provision of infrastructure to
meet anticipated demand. Similar tools, often software-based, are in use throughout the world
(Transportation Economics, 2017).
Transportation has a range of impacts, such the sector’s contribution to climate change (Stern
2006), accidents (Jacobs et al. 2000; WHO 2015), or health, with for instance 85% of airborne
particulate pollution being linked to fossil fuel combustion (The Lancet 2017). These
constitute negative externalities that need to be considered in CBA. The European
Environment Agency (2003) estimated, for example, that the external cost of transport is in
the order of 8% of GDP in the EU plus Norway and Switzerland. Some 58% of this total is
linked to cars, including accidents, noise, air pollution, climate change, and related
environmental impacts. In a more recent study, CE Delft, Infras and Fraunhofer ISI (CE Delft
et al., 2011) calculated that negative transport externalities amounted to €500 billion in the
EU27 plus Norway and Switzerland in 2008, or 4% of total GDP. This includes accidents, air
pollution, climate change, noise and congestion, as well as other external costs linked to up
and downstream processes, i.e. energy, vehicle, and infrastructure production. In a global
assessment, the World Health Organization (WHO, 2015) quantified the cost of traffic deaths
and injuries to be equivalent of a 3% of global GDP. The Lancet Commission (2017)
calculated that air pollution, to which transportation makes a significant contribution, is
responsible for 16% of deaths worldwide, incurring welfare losses of US$4.6 trillion. Given a
world GDP of US$75.8 trillion in 2016 (World Bank 2017), this corresponds to 6% of world
GDP. Even though no comprehensive, global assessment of motorized transportation’s
negative externalities exists, evidence suggests that these are significant (Becker et al. 2012;
Santos 2017).
Analyses of cycling externalities are rare. Nelson (1995) discussed the implementation of
bicycle access ways as seen against the costs of air pollution, congestion, or noise. Buis
(2000) provided cost-benefit analyses for cycling in Amsterdam, Bogotá, Delhi and
Morogoro. Wittink investigated non-motorized transport in relation to economic growth,
poverty reduction and quality of urban life in the Netherlands (2001). Saelensminde (2002)
studied CBAs for walking and cycle-track networks in Norwegian cities. All concluded that
cycling makes positive contributions to the economy. Only one CBA framework has been
presented for walking. Litman (2004) discussed urban livability, accessibility, transport cost,
health, external costs, efficient land use, economic development and equity. Studies by
Meschik (2012) and Rabl and de Nazelle (2012) assessed the cost of switching from driving
to bicycling (per individual or km cycled). In Copenhagen, a study by COWI and City of
Copenhagen (2009) compared the cost of cars with bicycles to derive conclusions regarding
the financing of transport infrastructure. This study has more recently been complemented
with an economic analysis of walking in Copenhagen (Realise, 2018). In Canada, the Victoria
Transport Policy Institute published a comprehensive comparison of car, bicycle, walking and
other transport modes (Litman and Doherty 2011). All studies found substantial benefits of
cycling and walking over the car.
Existing CBA frameworks can be criticized from comprehensiveness and unit cost
perspectives. While some studies ignore negative externalities altogether (cf. Transportation
Economics, 2017), others have included a number of selected parameters (cf. EC, 2014).
Where negative externalities are considered, the unit cost chosen may underestimate the
actual cost. Furthermore, transportation CBA is usually used to derive a ratio, i.e. benefits
divided by cost, to provide an absolute measure of benefits. This omits discussion of the
distribution of cost and benefits, which may accrue to the individual or society. As focus is
often on one transport mode, usually the car, existing CBAs also make limited contributions
to decision-making in urban contexts, where the substitutability of transportation makes it
possible for planners to favor different transport modes competing for space or prioritization.
3. Method
Comparative cost-benefit analysis
CBA frameworks need to consider two key aspects, the decision on the parameters to be
included in calculations, as well as justified unit costs. Comparative CBA can be used to
assess the economic cost of a kilometer driven, cycled or walked, as well as to assess (ex ante
or ex post) changes in transportation costs as a result of urban re-design or infrastructure
change. The current transport system is the basis for assessments, in which costs may be
external or private. The validity of any CBA will rely on the comprehensiveness of the
parameters included, as well as the complexity of the effects considered, including rebounds
(Santarius et al., 2016). With regard to parameters, the European Commission’s ‘Guide to
Cost-Benefit Analysis of Investment Projects’ suggests, for transport-related projects, to
include travel time, vehicle operating costs, accidents/collisions, noise, air pollution, and
climate change in transport CBAs (EC, 2014). These parameters are also used in other
countries (Litman and Doherty, 2011; NZ Transport Agency, 2016), but they inadequately
represent the full cost of motorized transportation, and, given the lack of comparison, omit
benefits associated with cycling or walking. In this study, four CBA assessment frameworks
are compared to identify a comprehensive list of parameters, including i) The European
Commission’s Handbook on Transport Costs (EC, 2014); ii) the city of Copenhagen’s
comparative CBA (COWI and City of Copenhagen, 2009) along with the citys walking CBA
(Transportministeriet, 2013); iii) the European Cycle Foundation’s study of ‘bicycle benefits’
(ECF, 2016), and the Canadian Victoria Transport Policy Institute’s ‘CBA for transportation’
(Litman and Doherty, 2011). Identical CBA conditions are applied for the three transport
modes studied, i.e. car, bicycle and walking.
To calculate unit costs, the existing literature on costs/benefits was reviewed. For many
parameters, valuation cannot be based on market values. Common alternative assessment
methods include market prices, stated preferences, revealed preferences, cost savings, human
capital approaches, willingness-to-pay/willingness-to-accept, hedonic pricing, as well as
shadow pricing (EC 2014). Values that constitute a cost are characterized as positive (+) and
those that represent a benefit are negative (-). Values are current, but change over time based
on emerging knowledge. The assessment of health implications and their cost, for example,
has seen considerable progress over the past decade. Yet, external and private cost depends on
context. As an example, the monetary value of time is vastly different between continents and
countries, and also depends on the time of the day. To derive global averages, national studies
are interpolated in comparison to European average per capita GDP. This implies a degree of
abstraction, and a limitation of this review remains that values continue to represent an
approximation. Two meta-studies, Korzhenevych et al. (2014) and Litman and Doherty
(2011), provide comprehensive discussions of weaknesses and shortcomings of the various
valuation methods. Of all national studies, data for Denmark appears to be the most regularly
updated (Center for Transport Analytics 2017). Readers are referred to these studies for
reference, as any full representation of methodological approaches is outside the scope of this
Transport demand and assumptions
Estimates of travel demand suggest that in OECD countries, 1.2 billion people travelled 11.0
trillion passenger miles (17.7 trillion passenger kilometers [pkm]), averaging more than 9,000
miles (14,484 km) per person in 2012 (EIA 2017). In addition, 6 billion people in non-OECD
countries travelled an estimated 12.6 trillion passenger miles (20.3 trillion pkm), averaging
slightly more than 2,000 miles per person (3,219 pkm). More than 80% of passenger travel in
OECD regions and 41% of passenger travel in non-OECD countries involves light-duty
vehicles (EIA 2017). Of the 38 trillion pkm travelled by the world’s population in 2012, it can
be estimated that 22.5 trillion have been travelled by car and other forms of lighter motorized
vehicles. In the EU28, some 4.719 trillion pkm were travelled by car in 2015 (EC 2017), with
an average occupancy rate of 1.54 persons per car (EEA 2010). This level may have declined
in industrialized countries in recent years: Data for Austria indicates, for example, an average
occupancy of 1.4 persons per car in 1990, and 1.2 in 2015 (Umweltbundesamt 2017).
Less information is available regarding cycling. The European Cyclists’ Federation (2016)
claims that in the EU, 134 billion km are cycled every year. Compared to EU (EC 2017)
estimates of 4.719 trillion car pkm, the ratio of car to bicycle pkm is 32:1. There appears to be
no data for European walking. Bassett et al. (2008) suggest that in the USA, people walk 141
km per capita and year, i.e. less than 400 m per day, while in Denmark, this value is three
times higher, at 1.18 km per day, or 431 km per year. Note that this data refers to ‘trips’, i.e.
excludes movement at home or at work. At an estimated daily walking distance of 1.2 km in
the European Union (based on Bassett et al. 2008), the region’s 500 million residents may
walk some 180 billion km per year.
Unit costs
All unit costs represent averaged values per passenger kilometer, though there exist
considerable differences between vehicles, locality of impact, and time of the day. Equally
important are differences related to economic context, i.e. where costs are income-related. All
item cost values represent average values, based on official data for the EU where available
(Korzhenevych et al. 2014), and complemented with data by Litman and Doherty (2011). This
latter data base is regularly updated, last on 24 April 2018 (for data sources see VTPI 2018).
Cost assessments also rely on the peer-reviewed literature (e.g. Coady et al. 2017) as well as
reports and datasets by institutions and organizations (e.g. Center of Transport Analytics
2017; IEA 2017; Lancet Commission 2017). In a few cases, no cost assessments could be
identified, for instance in the case of soil and water pollution caused by cyclists. In such cases,
the authors have provided cost estimates. Only with regard to two items, Quality of Life and
Branding & tourism, no data was found to support calculations, and these have to remain
open. Where data is compared to Canadian (Litman and Doherty, 2011) or Danish values
(Center of Transport Analytics, 2017), averaged European area values are calculated at 60%
and 79% of these respective countries’ GDPs (based on World Bank, 2017). All values are
inflation-adjusted to mid-2017 or averaged 2017 consumer price index values (World Bank,
2017), using, for example, the US of Labour Statistics’ Inflation Calculator, Where necessary, values are converted to
Euros at June 2017 exchange rates ( All calculations and assumptions are
detailed in the Annex.
Wherever costs are averaged, this hides complexity. For example, the climate change
abatement cost is expected to increase over time, as cheaper options for GHG emission
reductions become unavailable. This implies a potential under/overrepresentation of older cost
assessments that have been extrapolated to 2017 in this paper. As cost also depends on car
choices (mass & motorization; electric versus combustion), as well as driving styles, averaged
values do not always provide guidance for transport policy. However, as a general rule, the
highest cost imposed by automobility is related to large cars in urban contexts. Where a cost is
based on estimates, this is explained in the text. Overall, CBA portrays an equilibrium of the
transport system at a given point in time. This dynamic can change, as available infrastructure
influences travel time; while active transport modes have positive repercussions for health,
but increase traffic risks. Costs and benefits of transport projects can also accrue over
different points in time. Where cost-benefit interrelationships change, this can involve non-
linear supply curves reflecting scarcity (e.g. land use, resources). Averaged values as
presented in this paper consequently provide an indication rather than exact assessments.
4. Parameters for comparative CBA
Standard parameters in transport CBAs include travel time, vehicle operating costs, accidents,
noise, air pollution, and climate change. These are commonly considered basis requirements
for CBA, even though they do not represent all externalities that constitute a cost or benefit of
transportation. Where CBA compares transport modes, it is also important to consider how
these incur mutual, interdependent costs. As an example, cyclists or pedestrians are exposed
to various negative externalities created by cars, such as collision risks, distress, noise,
pollutants, or smells. Cyclists may also face disadvantages as a result of public space and
infrastructure predominantly assigned to cars, or prioritization of vehicles in traffic (e.g. red-
light waiting times). Where cars clog roads, this may slow down cyclists. Pedestrians will
generally use separated infrastructure, but they may also face disadvantages, as traffic systems
are designed to maximize vehicle flows.
Table 1 compares the parameters included in the four different CBA assessment frameworks
(COWI & City of Copenhagen 2009; EC 2014; ECF 2016; Litman & Doherty 2011), ranging
from six (EC 2014) to 44 parameters (ECF 2016). ECF (2016) calculates the cost of cars as a
benefit associated with the bicycle (i.e., as ‘avoided costs’), in what is essentially an
assessment of the total economic value of cycling in the European Union. Hence, not all
parameters of the ECF approach are equally valid for inclusion in comparative CBA:
They represent double-counting (‘climate change costs’ vis-á-vis ‘related benefits of
reduced CO2 emissions’; ‘urban design’ vis-á-vis ‘quality of public space’);
They are not focused on externalities (i.e., ‘economic contribution of bicycle
manufacturing’, ‘sales and repairs’, ‘shopping’, ‘bicycle tourism’ or ‘induced’ effects
in associated economic sectors);
They include subjective costs/benefits assessments on which views may vary (i.e.,
‘quality of time when cycling’, ‘social and gender equality’, ‘child welfare’, ‘social
safety’, ‘resilience and robustness’, ‘connectivity’, ‘accessibility’).
In excluding these aspects, the analysis of the three CBA frameworks yields a total of 14
parameters that should be part of any comprehensive, comparative transport CBA. These are
listed in Table 1, where ‘health benefits’ summarize ‘healthier lives’, ‘mental health benefits’,
‘health benefits for children’, ‘reductions in sick-leave’, ‘productivity gains’ and ‘prolonged
lives’ (COWI & City of Copenhagen 2009; ECF 2016).
Table 1: Comparison of parameters considered in CBA transport contexts
Parameter Definition E
1. Climate change Cost of climate change effects linked to
greenhouse gas emissions (CO2, other long-
lived GHG)
2. Air pollution Cost of air pollution, including economic and
health effects of CO, NOx, PM2.5, PM10, SOx,
VOC, and O3.
3. Noise pollution Cost of noise, including amenity costs
(property values, productivity or health
4. Soil and water quality Pollution of ground water and soils related to
contaminants from traffic (heavy metals,
hydrocarbons, road salt, etc.)
5. Land use and
Space requirements for infrastructure
construction, including parking; roadway
land and parking value; loss of ecosystem
service values
6. Traffic infrastructure
Cost of infrastructure maintenance,
administration and traffic police
7. Resource requirements Resources needed to build cars/bicycles, as
well as the cost to recycle resources, or to
deposit wastes (lifecycle based)
Travel time and vehicle operation
8. Vehicle operation Cost of owning and operating a particular
transport mode, including duties and taxes,
insurance, fuel and vehicle depreciation
9. Travel time The cost of travel time associated with the
use of a specific transport mode
10. Congestion Cost of roadway congestion imparted on
other road users, including additional travel
time, operating costs, fuel costs, reliability
costs, pollution, climate change, accidents,
Health, accidents and perceived comfort
11. Health benefits (better
health, productivity gains
and prolonged life)
Savings to the healthcare system as a result
of partaking in active transportation;
reduction in sick leave days; longer lives.
12. Accidents (collisions) The costs of minor and major injuries, and
fatalities, attributed to medical expenses,
pain and suffering, loss of life. Material
damage associated with car accidents
13. Perceived safety &
Perceived accident risks in traffic as a result
of exposure to motorized traffic; discomfort
because of exposure to exhaust fumes
Quality of life, tourism and infrastructure
14. Quality of life,
branding and tourism
Value derived from being considered a
progressive city with a high quality of life;
value of open spaces for tourism
X: considered in respective study
Source: COWI and City of Copenhagen 2009 (‘CPH’ in Table 1); EC 2014 (EC); ECF 2016
(ECF); Litman and Doherty 2011 (VTPI).
1.1 Climate change
Climate change is a result of greenhouse gas emissions, of which CO2 is the most important in
road transport contexts. Transportation requires 27% of final global energy use, corresponding
to emissions of 6.7 GtCO2 or 7 GtCO2-eqivalent in 2010. By 2050, transportation is expected
to emit 12 GtCO2eq per year (IPCC 2014); i.e., the sector will increasingly interfere with
mitigation objectives (UNFCCC 2015). Climate change is expected to cause significant
economic damage (Stern 2006). In CBA, unit costs for CO2 have been based on the market
value of the trade in CO2, which reflect willingness-to-pay by businesses in light of expected
future climate policy. Carbon market cost is not a reflection of the external cost of climate
change, however, and may be better assessed on the basis of the cost of reducing emissions to
a level that is in line with the international 2°C stabilization goal (UNFCCC 2015).
Fossil fuels are subsidized. A recent estimate by Coady et al. (2017), assesses the value of
consumer prices below supply costs, and a ‘Pigouvian’ tax reflecting environmental damages.
Combining air pollution, vehicle externalities, supply costs, and general consumer taxes, puts
the total value of fossil fuel subsidies at 20174.6 trillion (ibid., for discussion see McKitrick
2017). Including only supply costs below consumer prices as subsidies corresponds to
2017504 billion, across all fossil fuels (Coady et al. 2017).
1.2 Air pollution
The cost of air pollution includes economic and health effects of carbon monoxide (CO),
nitrous oxides (NOx), particulate matter (PM2.5, PM10), sulphurous oxides (SOx), volatile
organic compounds (VOC), and ozone (O3) (Crüts et al., 2008; Klæboe et al., 2000; Künzli et
al., 2000; Morelli et al., 2015). The European Environment Agency (EEA 2016) also
distinguishes black carbon (BC), ammonia (NH3), Benzopyrene (BaP), benzene (C6H6, an
additive to petrol), as well as toxic metals such as arsenic (As), Cadmium (Cd), Nickel (Ni),
Lead (Pb) and mercury (Hg), which are linked to combustion of fossil fuels, metal production,
and waste incineration. Pollutants have various health effects, including bronchitis and
asthma, lung cancer and cardiopulmonary diseases (e.g. Hoek et al. 2002; Pope et al. 2002).
Traffic exhaust can be particularly dangerous to children (Patel & Miller 2009; Vette et al.
2013), and lead to respiratory infections, low birth weight, preterm birth and cognitive
impairment (Andersen et al. 2000; Brunekreef and Holgate 2002; Sunyer et al. 2015). Impacts
also include hospital admissions, restricted activity days and work days lost (Korzhenevych et
al. 2014). Apart from these health-related costs, air pollutants also affect biodiversity,
agricultural yield, as well as buildings through the soiling of facades and corrosive processes
(CE Delft et al. 2011).
Various recent assessments have highlighted that air pollution is responsible for a
considerable part of global morbidity (diseases) and mortality (premature deaths). While the
Lancet Commission (2017) estimates that 16% of all deaths worldwide are related to
pollution, European assessments concluded that air pollution is responsible for 6% of total
mortality, half of this attributed to motorized transport (Künzli et al. 2000). Notably, this
would indicate that air pollution contributes to at least twice as many deaths as traffic
accidents (Künzli et al. 2000; see also Brauer et al. 2013). EEA (2017) suggests that in the
EU28, road transport accounted for 19% of total greenhouse gases (GHG, in CO2-
equivalents), 39% of NOx, 11% of PM2.5 and PM10, 10% of NMVOCs, 20% of CO and 29% of
BC. Road transport also contributed to 1-16% of emissions of toxic metals (As, Cd, Ni, Pb,
1.3 Noise
The cost of noise from traffic consists of two elements: the cost of annoyance as well as the
cost of health impacts due to noise exposure (CE Delft et al. 2011). Noise causes stress and
has been linked to tinnitus, mood changes, chronic sleep disturbance and lack of recovery
from tiredness, nervousness, anxiety and phobia, cardiovascular diseases, and cognitive
impairment of children (Babisch 2011, 2015; Öhrström,1995; Poenaru et al. 1978; WHO
2011). Economic costs of noise pollution include devaluation in house prices as a result of
traffic exposure, productivity losses (poor concentration, fatigue, hearing problems), as well
as the cost related to premature death or morbidity (cardiovascular diseases). There is also an
indirect cost linked to property prices, which are in steep decline in proximity to busy roads
(Łowicki & Piotrowska 2015).
Exposure to noise pollution is a problem specifically in cities, with estimates that 40% of the
EU population are exposed to road noise exceeding the safe health limit of 55 dB(A) (WHO
1999). As many as “one million healthy life years are lost every year from traffic-related noise
in the western part of Europe” (WHO 2011: v). The calculation of noise impacts is complex
and it is difficult to average costs, which depend on critical noise levels above 55 dB(A) and
population exposure during specific periods of the day (Korzhenevych et al. 2014). The EEA
(2017b) concludes that in the EU28, noise is responsible for 16,000 premature deaths, and 32
million adults annoyed by noise, as well as a further 13 million suffering from sleep
disturbance. Noise cost includes amenity value loss (property prices), treatment costs for
health, sick days, as well as premature deaths.
1.4 Soil and water quality
The construction and maintenance of transport systems, the production of transport modes, as
well as fuel burn lead to the pollution of ground water and soils. This includes pollutants
released to soil, water bodies and groundwater, such as hydrocarbons, non-gaseous exhaust,
heavy metal particulates from the wear of mechanical components such as brake pads, as well
as salt and gravel used for anti-icing or winter maintenance (e.g. Sörme & Lagerkvist, 2002).
Additionally, impacts related to increased storm water runoff from impervious surfaces such
as concrete and asphalt must be considered.
1.5 Land use and infrastructure
Space requirements for transport infrastructure, including parking, are considerable (IEA
2013). Land use represents a negative externality as a result of land lost for other purposes,
such as agriculture, as well as its value for ecosystem services (Daily 1997). Land is often
provided for free, for instance in the form of parking (Shoup 2011). Road land should
consequently be priced and taxed at the same rate as for competing uses (Litman & Doherty
2011). The cost of land use can be calculated on the basis of a forecast of the land needed
(annually) for additional infrastructure, including both the cost of land and infrastructure
construction. The International Energy Agency suggests that road, rail and parking
infrastructure by 2050 is expected to account for between 250,000 km2 and 350,000 km2 of
built surface area (IEA, 2013). By 2050, under the IEA’s 4DS scenario, in which light duty
vehicle travel increases to 43 trillion vehicle kilometers, 25 million paved road km, as well as
44,500 km2 of parking space will be added to existing traffic infrastructure.
1.6 Traffic infrastructure maintenance
The cost of traffic infrastructure maintenance comprises constructions, major repairs, renewal,
and construction maintenance, winter maintenance, marking, cleaning, cutting, checks, as
well as administrative tasks, such as traffic control (Litman & Doherty 2011). In contrast to
road infrastructure demand, which is largely driven by growth in vehicle numbers, road
maintenance needs arise mostly out of freight transportation, as a result of the greater weight
of trucks and their disproportionally larger impact on roads (Small & Winston, 1988). As data
for the US suggests, total highway expenditures consist of maintenance & operations (26%),
highway capacity expansion (23%), reconstruction, rehabilitation and restoration (19%),
administration (9%), patrol and safety (8%), local road capital improvements (8%), interest on
debt (4%) and other (3%) (Litman & Doherty, 2011).
1.7 Resource requirements
Resource requirements, or the cost related to up and downstream processes, refer to the
resources needed to build cars/bicycles and transport infrastructure, including all energy
requirements on a lifecycle basis, the cost to maintain vehicles or bicycles, to recycle these,
and to deposit wastes. The cost of these aspects is associated with emissions of CH4, N2O,
CO2, NOx, or PM.
1.8 Vehicle operation
Vehicle operation comprises the costs of driving a car, including fuel, oil & tire wear;
maintenance and depreciation, parking fees and road tolls; as well as financing, insurance,
registration fees and taxes.
1.9 Travel time
Travel time is considered a (private) cost that has to be minimized by optimizing traffic flows
(Hutton, 2013). The assessment of the marginal value of travel time is complex and
sometimes contentious, as it depends on research method, sociodemographic factors and
transport mode (Hensher 2009; Shires and de Jong 2009). The value of travel time has also
been linked to travel time reliability, i.e. the uncertainty experienced by travelers as to when
they will reach their destination (Carrion and Levinson 2012). Transport CBA usually assesses
the value of travel time based on traffic participants' willingness to pay for time (Axhausen et
al. 2015; Center for Transport Analytics 2017; Korzhenevych et al. 2014; Litman and Doherty
1.10 Congestion
Congestion is the time loss imposed on other travelers because of simultaneous use of the
road network, including travel time, operating cost, fuel cost, reliability cost (arrival time),
pollution, collisions, noise, as well as driver stress and reductions in subjective wellbeing
(Litman & Doherty 2011). Travel time cost is considered a private cost. As some cost aspects
are already considered in other cost calculations (see previous sections), ‘congestion’ only
includes the time cost of driving an additional km in a congestion situation compared to a
situation of free traffic flow.
1.11 Health benefits
Transport-related health effects can be external or private, and health enabling or damaging
(Litman and Doherty 2011). For example, savings to the healthcare system as a result of
active transportation represent a benefit to society. Cycling is known to enable health,
including a reduced risk of cardiovascular disease, various cancer types, type-2 diabetes or
depression (Genter et al., 2008; Litman and Doherty, 2011; Holm et al., 2012). Cycling can
also reduce obesity levels (Bassett et al. 2008). Cycling benefits include reduced costs for
medical treatments, fewer days of sick leave (external benefits), though there also exist
private benefits (better fitness, longer life expectancy) (Genter et al., 2008; NZ Transport
Agency, 2016). In Denmark, where these benefits have been quantified, cycling is estimated
to prevent about 3,000 deaths, more than 3,000 cases of type 2 diabetes, almost 6,000 cases of
cardiovascular disease as well as in excess of 2,000 cases of cancer per year (Andersen et al.,
2000; Blond et al., 2016; Rasmussen et al., 2016). In contrast, health damaging aspects
associated with cars include traffic collisions, air and noise pollution, stress and anxiety, or
constraints on active transport (traffic risks imposed on cyclists or other active transportation).
These health damaging aspects of the car are covered in other sections (1.2, 1.3, 1.12).
1.12 Accidents (collisions)
Collision cost comprises public services (police, rescue and treatment), the loss of net
productivity, premature deaths, medical expenses, as well as the cost of pain, grief and
suffering. Collisions include damages and risks to the individual, and uncompensated
damages and risks imposed on society (Litman & Doherty 2011). The external cost thus only
includes damages uncovered by private insurances (Korzhenevych et al. 2014). EPA (2017)
suggests that mortality risks are the most significant cost factor that can be calculated on the
basis of willingness to pay for reductions in the risk of dying, i.e., the value of a statistical life.
Apart from fatal accidents, there is also a considerable cost associated with injuries.
1.13 Perceived safety & discomfort
Perceived risks in traffic represent a cost for cyclists. This may include physical traffic risks,
i.e. to become involved in an accident; annoyance, for instance with regard to traffic noise; as
well as perceived health risks, such as exposure to pollutants. Exhaust fumes can also be
perceived as a discomfort (Gössling et al. 2018). It has been highlighted that perceived traffic
risks represent a cost to cyclists (COWI and City of Copenhagen 2009), but this cost has
never been quantified. Likewise, the cost of exhaust exposure smells has never been assessed.
As outlined by Klæboe et al. (2000), smell is perceived as an annoyance that is interdependent
with noise and other environmental pollutants, and its effects may be enhanced where smells
are more strongly associated with health threats.
1.14 Quality of life, branding and tourism
Quality of life is an inherently subjective concept. In transportation contexts, the concept has
been linked to physical, mental, social, economic wellbeing (Lee and Sener 2016),
employment opportunities and social connectedness (Steg and Gifford 2005). Preceding
sections have captured some of these aspects, including risks of exposure to collisions, or
better health. In this CBA, quality of life refers to the difference between a current life-
situation in comparison to a potentially ideal life state (Gardner & Weinberg 2013), for
instance in terms of gains in mental wellbeing as a result of physical activity (Jia & Lubetkin
2005; Lee & Sener 2016; see also Hall et al. 2017; Saelens et al. 2003). More walkable or
bikeable neighborhoods are also perceived more positively by tourists and have positive
branding effects (COWI and City of Copenhagen 2009).
5. Results & discussion
Table 2 provides an overview of findings for the EU. Values represent approximations,
confirming that the car represents a cost to society, at an average of €0.11 per pkm (Table 2).
This value is higher in countries with a higher GDP. The most important external cost facctors
are infrastructure construction, parking land provisions, roadway land use and climate change.
The private cost of the car is eight times higher, at €0.85 per pkm. This is largely owed to
congestion and the value of travel time. Vehicle operation is also a significant cost factor.
Cycling and walking incur external benefits, at €0.18/pkm and €0.37/pkm, respectively.
Benefits are largely associated with health. These health benefits are positive even in
situations where cycling and walking take place under less favorable conditions, such as
higher levels of air pollution (De Hartog et al. 2010). Better health also leads to a small
external cost as a result of extended pension payment needs (prolonged lives).
The private cost of cycling is significantly lower than for driving, and arises mostly out of
travel time, as vehicles are prioritized in traffic. Given their higher respiration rates (Panis et
al. 2010), cyclists are specifically exposed to air pollutants from motorized transportation.
Cyclists are known to engage in detours to avoid negative externalities of the car, at a
considerable time cost (Gössling et al. 2018). This also applies to walking.
Extrapolated to the number of km driven (4.719 trillion pkm), cycled (0.134 trillion pkm) or
walked (0.180 trillion pkm) in the EU, the external cost of automobility is about €500 billion
per year, while cycling and walking represent benefits of €24 billion and €66 billion. In the
future, these costs can be expected to change. The cost of health services may increase, for
example. Other aspects can be expected to lose relevance, as motorized traffic becomes
quieter and cleaner. Whether the overall cost of the transport system will increase or decrease
will depend on transport governance. Current policies continue to favor the automobile (e.g.
Hutton 2013). As this paper suggests, the reason for this may be that the true cost of
automobility is systematically underestimated (cf. EC 2014).
Table 2: The external and private cost of car, bicycle and walking
Parameter Car, €2017/pkm Bicycle, €2017/pkm Walking, €2017/pkm
External Private External Private External Private
1. Climate change
Climate change 0.011 0 0 0 0 0
Subsidies 0.003 0 0 0 0 0
2. Air pollution
Air pollution 0.007 0 0 0 0 0
3. Noise pollution
Noise pollution 0.007 0 0 0 0 0
4. Soil and water quality
Soil and water quality 0.005 0<0.001 0<0.001 0
5. Land use and infrastructure
Infrastructure Construction 0.030 00.002 00.002 0
Roadway land use 0.011 0<0.001 0<0.001 0
Parking land use 0.021 0.022 <0.001 <0.001 - -
Ecosystem services ?0?0?0
6. Traffic infrastructure maintenance
Traffic infrastructure maintenance 0.004 0<0.001 0<0.001 0
7. Resource requirements
Resource requirements 0.007 0<0.001 0<0.001 0
8. Vehicle operation
Vehicle operation 0 0.250 00.047 00.041
9. Travel time
Travel time 0 0.253 00.474 01.264
10. Congestion
Congestion 0 0.355 0<0.001 0<0.001
Barrier effects 0 0.005 0<0.001 0<0.001
11. Health benefits
Health benefits 0 0 -0.193 -0.134 -0.386 -0.268
Prolonged life 0 0 0.007 -0.320 0.014 -0.640
12. Accidents (collisions)
Accidents 0.002 ? <0.001 0.066 <0.001 0.066
13. Perceived safety & discomfort
Perceived safety & discomfort ? ? - 0.014 -0.036
14. Quality of life, branding and tourism
Quality of life, branding and tourism 0 0 ? ? ? ?
Total 0.108 0.885 -0.184 0.147 -0.370 0.499
Results should have various implications for transport policy, as they indicate that the
automotive system relies on significant subsidies. Active forms of transport, on the other
hand, should be supported for health reasons (The Lancet 2017). This is feasible specifically
in cities, where the substitutability of transport modes is high. Policies supporting walking and
cycling in cities will also be warranted from systemic development perspectives: There are
widespread expectations for car numbers to increase, in a situation where transport systems
face capacity limits in virtually all large cities (Dargay et al. 2007, EIA 2017). EU cost
calculations as presented in this paper suggest that to shift mobility from the car to the bicycle
is worth about €0.30/pkm, and from the car to walking €0.48/pkm.
Despite its limitations, the importance of CBA in transport contexts can be expected to grow.
As this paper argues, CBA needs to be comprehensive and comparative, specifically in
contexts where substitutable transport modes compete for space or prioritization. Questions
remain regarding the allocation of costs, specifically with regard to spillover externalities (e.g.
Jansson 1994). For example, as cars cause most accidents, it may be argued that the cost of
traffic density (collisions, perceived risks) is attributable to cars. In other words, current CBA
analyses accept that a considerable part of car-related externalities represents a private cost to
active transport users.
6. Conclusions
This paper reviewed different transport CBA frameworks, concluding that these omit
important cost parameters. As these represent significant negative externalities, a central
conclusion is that transport investment projects in the European Union systematically
underestimate the cost of automobility. To become more inclusive, CBA frameworks need to
be expanded. Furthermore, in urban transport planning contexts where transport mode choices
are often substitutable, CBA assessments should be comparative to adequately consider the
implications of transport mode prioritization. Where CBA is used in non-comparative ways,
and with a view to address growth in individual motorized transportation, it has a self-
fulfilling nature, i.e. the conclusion will often be that adding transport infrastructure is
meaningful. Fundamentally different insights may be gained from more comprehensive and
comparative CBA frameworks. As this research indicates, automobility is heavily subsidized
in the European Union, at an estimated €500 billion per year, while active transportation
represents a benefit to society currently worth an annual €24 billion (cycling) and €66 billion
(walking). Specifically in cities, the long-standing focus on automobility as the favored
transport mode should consequently change.
Future research may seek to improve the database for various parameters to refine and
validate cost estimates. This may also include a better distinction of cost distributions, for
instance between different age or population groups. It is also warranted for comparative CBA
to include public transport.
ADAC (2017). Die Top 10 in den Autokosten. Available:
autokosten/default.aspx?ComponentId=35261&SourcePageId=0 Accessed 13 March
Aldred, R. (2013). Incompetent or Too Competent? Negotiating Everyday Cycling Identities
in a Motor Dominated Society. Mobilities 8(2): 252-271.
Andersen, L.B., Schnohr, P., Schroll, M. and Hein, H.O. (2000). All-cause mortality
associated with physical activity during leisure time, work, sports, and cycling to
work. ArchInternMed 2000; 160: 1621-8.
Annema, J.A., Koopmans, C., Van Wee, B. (2007). Evaluating Transport Infrastructure
Investments: The Dutch Experience with a Standardized Approach, Transport
Reviews, 27:2, 125-150.
Axhausen, K.W., Ehreke, I., Glemser, A., Hess, S., Jödden, C., Nagel, K., Sauer, A. and Weis,
C. (2015). Ermittlung von Bewertungsansätzen für Reisezeiten und Zuverlässigkeit
auf der Basis eines Modells für modale Verlagerungen im nicht-gewerblichen und
gewerblichen Personenverkehr für die Bundesverkehrswegeplanung. TNS Infratest
und IVT, ETH Zürich.
Babisch, W. (2011). Cardiovascular effects of noise. Noise and Health, 13 (52), 201-204.
Bassett, D. R., Pucher Jr, J., Buehler, R., Thompson, D. L., & Crouter, S. E. (2008). Walking,
cycling, and obesity rates in Europe, North America, and Australia. Journal of
Physical Activity and Health, 5(6), 795-814.
Becker, U.J., Becker, T., Gerlach, J. (2012). The True Costs of Automobility: External Costs
of Cars. Overview on existing estimates in EU-27. Available from: http://www.greens-
pdf Accessed 1 May 2014.
Bithas, K. (2011). Sustainability and externalities: Is the internationalization of externalities a
sufficient condition for sustainability? Ecological Economics 70: 1703-1706.
Blickstein, S., & Hanson, S. (2001). Critical mass: forging a politics of sustainable mobility in
the information age. Transportation, 28(4), 347-362.
Blond, K., Jensen, M.K., Rasmussen, M.G., Overvad, K., Tjønneland, A., Østergaard, L.
and Grøntved, A. (2016). Prospective Study of Bicycling and Risk of Coronary Heart
Disease in Danish Men and Women. Circulation 134(18): 1409-11.
Boardman, A., Greenberg, D., Vining, A., Weimer, D. (2010). Cost-Benefit Analysis.
Concepts and Practice (4th Edition). Pearson Prentice Hall: New Jersey.
Brauer, M., Reynolds, C., & Hystad, P. (2013). Traffic-related air pollution and health in
Canada. CMAJ : Canadian Medical Association Journal, 185 (18), 1557–1558.
Brunekreef, B., & Holgate, S. T. (2002). Air pollution and health. The Lancet, 360 (9341),
Buehler, R., Pucher, J., Gerike, R., & Götschi, T. (2017). Reducing car dependence in the
heart of Europe: lessons from Germany, Austria, and Switzerland. Transport
Reviews, 37(1), 4-28.
Buis, J. (2000). The Economic Significance of Cycling: A study to illustrate the costs and
benefits of cycling policy. Den Haag, Interface for Cycling Expertise.
Bureau of Transportation Statistics (2016). Average Fuel Efficiency of US Light Duty
Vehicles. Available:
tion_statistics/html/table_04_23.html Accessed 27 November 2017.
Carrion, C., & Levinson, D. (2012). Value of travel time reliability: A review of current
evidence. Transportation Research Part A: Policy and Practice, 46(4), 720-741.
CE Delft, Infras, Fraunhofer ISI (2011). External Costs of Transport in Europe. Available
from: http://ecocalc-
Accessed 1 May 2014.
Center for Transport Analytics (2017). Transportøkonomiske Enhedspriser. Available:
Accessed 16 February 2017.
Coady, D., Parry, I., Sears, L., & Shang, B. (2017). How large are global fossil fuel
subsidies?. World Development, 91, 11-27.
Costanza, R., dArge, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., Limburg, K.,
Naeem, S., O’Neill, R.V., Paruelo, J., Raskin, R.G., Sutton, P., van den Belt, M.
(1997). The value of the world’s ecosystem services and natural capital. Nature 387:
COWI and City of Copenhagen (2009). Samfundsøkonomiske analyser af cykeltiltag -
metode og cases, Januar 2009. Available: Accessed 20 May 2017.
Creutzig, F., Jochem, P., Edelenbosch, O.Y., Mattauch, L., van Vuuren, D.P., McCollum, D.,
Minx, J. (2015). Transport: A roadblock to climate change mitigation? Science 350
(6263), 911-912.
Crüts, B., van Etten, L., Törnqvist, H., Blomberg, A., Sandström, T., Mills, N. L., & Borm, P.
J. (2008). Exposure to diesel exhaust induces changes in EEG in human volunteers.
Particle and Fibre Toxicology, 5 (4), 6.
Daily, G. (1997). Nature’s Services. Washington D.C.: Island Press.
Dargay, J., Gately, D., & Sommer, M. (2007). Vehicle Ownership and Income Growth,
Worldwide: 1960-2030. The Energy Journal, 28 (4), 143-170.
Dave, S. (2010). Life cycle assessment of transportation options for commuters. Available: Accessed 15 February 2018.
De Hartog, J. J., Boogaard, H., Nijland, H., & Hoek, G. (2010). Do the health benefits of
cycling outweigh the risks? Environmental Health Perspectives, 118(8), 1109.
EC (2011). White Paper. Roadmap to a Single European Transport Area – Towards a
Competitive and Resource Efficient Transport System. COM (2011) 144 final.
Brussels, European Commission.
EC (2012). Mobility and Transport. Statistical pocketbook 2012. Available: Accessed
22 December 2017.
EC (2017). Statistical Pocketbook
2017. Accessed
22 December 2017.
EEA (2010). Occupancy rates of passenger vehicles. Available:
94/occupancy-rates-of-passenger-vehicles-1.pdf?direct=1 Accessed 3 January 2018.
EEA (2017a). Air quality in Europe 2017. Available: Accessed 10
February 2018.
EEA (2017b). Noise. Available:
Accessed 10 February 2017.
EEA (2017c). Heavy metal emissions. Available:
maps/indicators/eea32-heavy-metal-hm-emissions-1/assessment-8 Accessed 15
February 2017.
EEA (European Environment Agency) (2011). Laying the foundations for greener transport.
Accessed 27 November 2017.
EEA (2016). Air quality in Europe – 2016 report. EEA Report 28/2016. European
Environment Agency: Copenhagen.
EIA (2017). International Energy Outlook 2017. Available: Accessed 23 November 2017.
EPA (2010). Guidelines for Preparing Economic Analyses. Available:
analyses Accessed 21 December 2017.
EPA (2017). Mortality Risk Valuation. Available:
economics/mortality-risk-valuation Accessed 21 December 2017.
European Commission (2014) Guide to cost-benefit analysis of investment projects.
Accessed 16 November 2017.
European Commission (EC) (2014). Update of the Handbook on External Costs of Transport.
Final Report. Available from:
costs-transport.pdf Accessed 1 May 2014.
European Cyclists’ Federation (ECF) (2016). The EU Cycling Economy – Arguments for an
integrated EU cycling policy. European Cyclists’ Federation, Brussels, December
European Environment Agency (2003). External costs of transport. Available from:
Accessed 1 May 2014.
Fontaras, G., Zacharof, N. G., & Ciuffo, B. (2017). Fuel consumption and CO2 emissions
from passenger cars in Europe–Laboratory versus real-world emissions. Progress in
Energy and Combustion Science, 60, 97-131.
Forsyth, A. (2015). What is a walkable place? The walkability debate in urban design. Urban
Design International, 20(4), 274-292.
Forsyth, A. and Krizek, K. (2011). Urban Design: Is there a Distinctive View from the
Bicycle?, Journal of Urban Design, 16:4, 531-549
Frank, L. D., Sallis, J. F., Saelens, B. E., Leary, L., Cain, K., Conway, T. L., & Hess, P. M.
(2010). The development of a walkability index: application to the Neighborhood
Quality of Life Study. British Journal of Sports Medicine, 44(13), 924-933.
Fraser, S. D., & Lock, K. (2011). Cycling for transport and public health: a systematic review
of the effect of the environment on cycling. European Journal of Public Health, 21(6),
Fritz, S., & Lusardi, M. (2009). White paper: “walking speed: the sixth vital sign”. Journal of
Geriatric Physical Therapy, 32(2), 2-5.
Frondel, M. and Vance, C., 2017. Cycling on the extensive and intensive margin: The role of
paths and prices. Transportation Research Part A: Policy and Practice, 104, 21-31.
Gardner, M., & Weinberg, J. (2013). How lives measure up. Acta Analytica, 28(1), 31-48.
Genter, J.A., Donovan, S., Petrenas, B. & Badland, H. (2008). Valuing the health benefits of
active transportation modes. NZ Transport Agency.
Gössling, S., & Choi, A. S. (2015). Transport transitions in Copenhagen: Comparing the cost
of cars and bicycles. Ecological Economics, 113, 106-113.
Gössling, S., Humpe, A., Litman, T. and Metzler, D. (2018). Effects of perceived traffic risks,
noise, and exhaust smells on bicyclist behaviour: An economic evaluation. Submitted.
Grant-Muller, S., Mackie, P., Nellthorp, J., Pearman, A. (2001). Economic appraisal of
European transport projects – The state of the art revisited. Transport Reviews 21 (2),
Hall, C. M., Ram, Y., & Shoval, N. (Eds.) (2017). The Routledge International Handbook of
Walking. London: Routledge.
Hanley, N. and Spash, C.L. (1993). Cost Benefit Analysis and the Environment. Edward
Elgar: Cheltenham, UK.
Hensher, D. A. (2011). Valuation of Travel Time Savings. In de Palma, A., Lindsey, R.,
Quinet, E., Vickerman, R. (Eds). A Handbook of Transport Economics. Cheltenham:
Edward Elgar, pp. 135–159.
Hoek, G., Brunekreef, B., Goldbohm, S., Fischer, P., & van den Brandt, P. A. (2002).
Association between mortality and indicators of traffic-related air pollution in the
Netherlands: a cohort study. The Lancet, 360 (9341), 1203-1209.
Holm, A.L., Glümer, C. & Diderichsen, F. (2012). Health Impact Assessment of increased
cycling to place of work or education in Copenhagen. BMJ Open, 2(e001135).
Hopkinson P, Wardman M. (1996). Evaluating the demand for new cycle facilities. Transport
Policy, 3(4): 241–249.
Hutton, B. (2013). Planning Sustainable Transport. Routledge: London.
IEA (2013). Global land transport infrastructure requirements. Available:
nsights_FINAL_WEB.pdf Accessed 3 January 2018.
IEA (2017). International Energy Outlook 2017, Transportation.
Available: Accessed 10
February 2017.
IPCC (2014). Climate Change 2014: Mitigation of Climate Change. Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA.
Jacobs, G., Aeron-Thomas, A., & Astrop, A. (2000). Estimating global road fatalities.
Crowthorne: Transport Research Laboratory.
Jansson, J.W. (1994). Accident Externality Charges. Journal of Transport Economics and
Policy, 28: 31-43.
Jia, H., & Lubetkin, E. I. (2005). The impact of obesity on health-related quality-of-life in the
general adult US population. Journal of Public Health, 27(2), 156-164.
Klæboe, R., Kolbenstvedt, M., Clench-Aas, J., & Bartonova, A. (2000). Oslo traffic study–
part 1: an integrated approach to assess the combined effects of noise and air pollution
on annoyance. Atmospheric Environment, 34(27): 4727-4736.
Knudsen, M.A. and Rich, J. (2013). Ex post socio-economic assessment of the Oresund
Bridge. Transport Policy 27: 53-65.
Korzhenevych, A., Dehnen, N., Bröcker, J., Holtkamp, M., Meier, H., Gibson, G., Varma, A.
and Cox, V. (2014). Update of the Handbook on External Costs of Transport.
nsport_2014_0.pdf Accessed 29 November 2017.
Krizek, K. (2007). Estimating the economic benefits of bicycling and bicycle facilities: An
interpretive review and proposed methods. Essays on Transportation Economics.
Leipzip, Germany: Physica-Verlag HD.
Künzli, N., Kaiser, R., Medina, S., Studnicka, M., Chanel, O., Filliger, P., Herry, M., Horak
Jr., F., Puybonnieux-Texier, V., Quénel, P., Schneider, J., Seethaler, R., Vergnaud, J. C.,
Sommer, H. (2000). Public-health impact of outdoor and traffic-related air pollution: a
European assessment. The Lancet, 356 (9232), 795–801.
Larsen, J., Zachary Patterson & Ahmed El-Geneidy (2013). Build It. But Where? The Use of
Geographic Information Systems in Identifying Locations for New Cycling
Infrastructure, International Journal of Sustainable Transportation, 7:4, 299-317
Lee, R. J., & Sener, I. N. (2016). Transportation planning and quality of life: Where do they
intersect?. Transport Policy, 48, 146-155.
Litman, T. (2004). Economic Value of Walkability. World Transport Policy & Practice 10:1,
Litman, T.A. and Doherty, E. (2011). Transportation Cost and Benefit Analysis - Techniques,
Estimates and Implications. Victoria: VTPI Victoria Transport Policy Institute.
Łowicki, D., & Piotrowska, S. (2015). Monetary valuation of road noise. Residential property
prices as an indicator of the acoustic climate quality. Ecological Indicators, 52, 472-
McKitrick, R. (2017). Global energy subsidies: An analytical taxonomy. Energy Policy, 101,
Meschik, M. 2012. Reshaping city traffic towards sustainability. Why transport policy should
favor the bicycle instead of car traffic. Procedia-Social and Behavioral Sciences, 48,
Morelli, X., Foraster, M., Aguilera, I., Basagana, X., Corradi, E., Deltell, A., Ducret-Stich, R.,
Phuleria, H., Ragettli, M.S., Rivera, M., Thomasson, A., Künzli, N. & Slama, R.,
(2015). Short-term associations between traffic-related noise, particle number and
traffic flow in three European cities. Atmospheric Environment, 103, 25-33.
Nelson, A.C. (1995). Private provision of public pedestrian and bicycle access ways : public
policy rationale and the nature of public and private benefits. Transportation Research
Record 1502: 96-104.
NZ Transport Agency (2016). Economic evaluation manual. Wellington: NZ Transport
Öhrström, E. (1995). Effects of low levels of road traffic noise during the night: a laboratory
study on number of events, maximum noise levels and noise sensitivity. Journal of
Sound and Vibration, 179 (4), 603-615.
Ortuzar J, Iacobelli S, Valeze C. (2000). Estimating demand for a cycle-way network.
Transportation Research Part A 34(5):353–373.
Panis, L. I., De Geus, B., Vandenbulcke, G., Willems, H., Degraeuwe, B., Bleux, N., Mishra,
V., Thomas, I., & Meeusen, R., (2010). Exposure to particulate matter in traffic: a
comparison of cyclists and car passengers. Atmospheric Environment, 44 (19), 2263-
Parks, S., Gowdy, J. (2013). What have economists learned about valuing nature? A review
essay. Ecosystem Services 3: e1-e10.
Patel, M. M., & Miller, R. L. (2009). Air pollution and childhood asthma: recent advances and
future directions. Current opinion in pediatrics, 21 (2), 235.
Poenaru, S., Rouhani, S., Poggi, D., Colas, C., Cohen, E., Blacker, C., Belon, J. P. & Dall'
Ava-Santucci, J. (1978). Study of Pathophysiological effects of chronicle exposure to
environmental noise in man. Acoustics Letters, 11, 80-88.
Pope III, C. A., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., & Thurston, G.
D. (2002). Lung cancer, cardiopulmonary mortality, and long-term exposure to fine
particulate air pollution. Jama, 287 (9), 1132-1141.
Pucher, J. and Buehler, R. (2017). Cycling towards a more sustainable transport future.
Transport Reviews 37(6), 689-694.
Rabl, A., De Nazelle, A. (2012). Benefits of shift from car to active transport. Transport
Policy, 19(1), 121-131.
Rank, J. Folke, J., Homann Jespersen, P. (2001). Differences in cyclists and car drivers
exposure to air pollution from traffic in the city of Copenhagen. The Science of the
Total Environment 279: 131-136.
Rasmussen MG, Grontved A, Blond K, et al. Associations between Recreational and
Commuter Cycling, Changes in Cycling, and Type 2 Diabetes Risk: A Cohort Study of
Danish Men and Women. PLoS Med 2016; 13(7): e1002076.
Realise (2018). Samfundsøkonomisk nøgletalsanalyse for fodgængertrafik i København.
Copenhagen: Realise ApS.
Rockström, J., Gaffney, O., Rogelj, J., Meinshausen, M., Nakicenovic, N., & Schellnhuber, H.
J. (2017). A roadmap for rapid decarbonization. Science, 355(6331), 1269-1271.
Saelens, B. E., Sallis, J. F., & Frank, L. D. (2003). Environmental correlates of walking and
cycling: findings from the transportation, urban design, and planning
literatures. Annals of Behavioral Medicine, 25(2), 80-91.
Saelensminde, K. 2002. Walking- and cycling-track networks in Norwegian cities: Cost-
benefit analyses including health effects and external costs of road traffic. Oslo,
Institute of Transport Economics: 50.
Santarius, T., Walnum, H.J., Aall, C., 2016. Rethinking Climate and Energy Policies: New
Perspectives on the Rebound Phenomenon. Springer, Berlin.
Santos, G. (2017). Road fuel taxes in Europe: Do they internalize road transport
externalities?. Transport Policy, 53, 120-134.
Schäfer, A., Heywood, J. B., Jacoby, H. D., & Waitz, I. A. (2009). Transportation in a
climate-constrained world. Cambridge: MIT Press.
Shires, J. D. and Jong, G. C. D. (2009). An International Meta-Analysis of Values of Travel
Time Savings. Evaluation and Program Planning, 32(4): 315–325.
Shoup, D. (2011). The High Cost of Free Parking. New York: Routledge.
Small, K. A., & Winston, C. (1988). Optimal highway durability. The American economic
review, 78(3), 560-569.
Söderbaum, P. (2007). Issues of paradigm, ideology and democracy in sustainability
assessment. Ecological Economics, 60(3), 613-626.
Sörme, L., & Lagerkvist, R. (2002). Sources of heavy metals in urban wastewater in
Stockholm. Science of the Total Environment, 298(1-3), 131-145.
Steg, L., & Gifford, R. (2005). Sustainable transportation and quality of life. Journal of
Transport Geography, 13(1), 59-69.
Stern N. (2006). Stern review report on the economics of climate change. 2006. Summary.pdf
(accessed Sept 29, 2017).
Sunyer, J., Esnaola, M., Alvarez-Pedrerol, M., Forns, J., Rivas, I., López-Vicente, M., Suades-
González, E., Foraster, M., Garcia-Esteban, R., Basagaña, X., Viana, M., Cirach, M.,
Moreno, T., Alastuey, A., Sebastian-Galles, N., Nieuwenhuijsen, M., & Querol, X.
(2015). Association between Traffic-Related Air Pollution in Schools and Cognitive
Development in Primary School Children: A Prospective Cohort Study. PLoS
Medicine, 12(3).
The Lancet (2017). The Lancet Commission on pollution and health. October 19, 2017,,
Tilahun, N.Y., Levinson, D.M., Krizek, K.J. (2007). Trails, lanes, or traffic: Valuing bicycle
facilities with an adaptive stated preference survey. Transportation Research Part A
41: 287-301.
Transportation Economics (2017). When to use benefit-cost analysis. Available:
Accessed 28 November 2017.
Transportministeriet (2013). Cyklingens effekter og samfundsøkonomi. Available:
Accessed 2 May 2018.
Umweltbundesamt (2017). Klimaschutzbericht 2017. Available: Accessed
4 April 2018.
UNFCCC (2015). Various documents. Retrieved from:
Vette, A., Burke, J., Norris, G., Landis, M., Batterman, S., Breen, M., Isakov, V., Lewis, T.,
Gilmour, M.I., Kamal, A., Hammond, D., Vedantham, R., Bereznicki, S., Tian, N., &
Croghan, C. (2013). The near-road exposures and effects of urban air pollutants study
(NEXUS): Study design and methods. Science of the Total Environment, 448, 38-47.
VTPI (Viktoria Transport Policy Institute) (2018). Transportation Cost Literature Review.
Available: Accessed 20 November 2018.
WHO (1999). Health Costs due to Road Traffic-related Air Pollution. An impact assessment
project of Austria, France and Switzerland. Economic Evaluation. Technical Report on
Economy, Federal Department of Environment, Transport, Energy and
Communications, Bureau for Transport Studies, Bern, Switzerland.
Wittink, R. (2001). Promotion of mobility and safety of vulnerable road users. SWOV
Institute for Road Safety Research, Leidschendam, The Netherlands, Report Number
World Bank (2017). Gross domestic product 2016. Available: Accessed 8 February 2018.
World Health Organization (2015). Global Status Report on Road Safety 2015. Retrieved
Accessed 8 July 2017.
World Health Organization (2011). Burden of Disease from Environmental Noise. Retrieved
World Health Organization (2016). Global Status Report on Road Safety 2015. Retrieved
Zuurbier, M., Hoek, G., Oldenwening, M., Lenters, V., Meliefste, K., van den Hazel, P., &
Brunekreef, B. (2010). Commuters’ exposure to particulate matter air pollution is
affected by mode of transport, fuel type, and route. Environmental Health
Perspectives, 118(6), 783.
Annex – Calculations
1. Climate change
Costs of mitigation have been estimated at €10-40/tCO2eq (EC, 2014; see also Becker et al.,
2012; EPA, 2010), and up to more than €85/t CO2eq for electric vehicles and aviation (IPCC,
2014). More recently, Rockström et al. (2017) suggested a “floor price” of €42.5/tCO2, rising
to €340/tCO2 by 2050. Korzhenevych et al. (2014) propose a 201798/tCO2-equivalent for the
EU, including CH4 and N2O warming effects, representing an averaged abatement cost of
20170.011/pkm at averaged emissions of 0.168kgCO2/vkm in Europe (Fontaras et al. 2017),
equivalent to 0.112kgCO2/pkm at an average load factor of 1.5 passengers/vehicle (UBA
Transportation receives 27% of energy subsidies, estimated at €2017500 billion/year (based on
Coady et al., 2017), calculated proportionally to its share in energy use/emissions. On the
basis of this estimate, transport is subsidized with some2017135 billion per year. As 46% of
all transportation energy use falls on passenger road travel (light duty vehicles; IEA, 2017),
the share of subsidies forwarded to cars may be in the order of €201762 billion, or €20170.003 per
pkm if considering 22.5 trillion pkm travelled with light duty vehicles.
In total, the climate change cost of car travel is about 20170.014 per pkm (Table 2). This
estimate does not include the lifecycle cost of manufacturing and scrappage of cars (see
Resource requirements), and increases over time. In comparison to the climate change cost of
cars, cycling and walking do not incur a significant climate change or subsidy cost (Litman
Doherty 2011).
Table 2: Cost assessment of climate change and fossil fuel subsidies
Assessment Cars Cost external Cost private Reference
External cost of CO220170.011/pkm 0 Korzhenevych et al. 2014; Litman and
Doherty 2011
Fossil fuel subsidies 20170.003/pkm 0 Coady et al. 2017
Assessment Bicycles Cost Reference
External cost of CO20 0 Litman and Doherty 2011
Fossil fuel subsidies 0 0 Coady et al. 2017
Assessment Walking Cost Reference
External cost of CO20 0 Litman and Doherty 2011
Fossil fuel subsidies 0 0 Coady et al. 2017
2. Air pollution
An assessment by the Lancet Commission (2017) on pollution and health suggests that 16%
of all deaths worldwide are related to pollution, corresponding to welfare losses of €3.91
trillion per year, or 6.2% of global economic output. In the European Union, it is estimated
that the total health-related cost of air pollution ranged between €330-940 billion in 2010,
including €15 billion from lost workdays. Air pollution also caused 436,000 premature deaths
in the EU28 (in 2013; EEA 2016). While the Lancet Commission does not specify sector
contributions, it is clear that transportation has central relevance.
The unit cost for different pollutants is difficult to quantify. Mortality and morbidity costs of
main pollutants from transport have been assessed by Korzhenevych et al. (2014) at EU
average values for PM2.5 at €201028/kg (rural) to €2010270/kg (urban); NOx at €201010.6/kg; VOC
at €20101.6/kg; and SO2 at €201010.2/kg. Depending on car model and location (urban, suburban,
rural, highway), this results in a cost from €20100.001/pkm to €20100.067/pkm (no average value
is provided by Korzhenevych et al. 2014). Litman and Doherty (2011) estimate that average
air pollution cost is in the order of 20170.017/pkm. This includes CO, PM2.5, PM10, NOx, VOC
as well as the effects of O3 on agricultural crops and exterior materials. Values refer to a car
with an average 21mpg fuel efficiency, equivalent to 11.2Lfuel/100vkm (Litman & Doherty
2011). EU average passenger car emissions have been estimated at 0.168kgCO2/vkm
(Fontaras et al. 2017), or about 6.75 Lfuel/100vkm, assuming an equal share of diesel/petrol
cars in the European fleet. Based on Litman and Doherty (2011) values, this translates into an
average cost of 20170.006/pkm, if calculated as the ratio of nominal European area GDP
(based on World Bank 2017) (Table 3). Bicyclists and people walking do not generate an air
pollution cost (Litman & Doherty 2011).
Table 3: Cost assessment of air pollution
Assessment Cars Cost external Cost private Reference
CO, PM2.5, O3, PM10, NOx, VOC 20170.006/pkm 0 Litman and Doherty, 2011
Assessment Bicycles Cost external Cost private Reference
Air pollution 0 0 Litman and Doherty, 2011
Assessment Walking Cost external Cost private Reference
Air pollution 0 0 Litman and Doherty, 2011
3. Noise pollution
Depending on traffic situation and time of the day, the cost of noise amounts to
20170.027/pkm (Korzhenevych et al. 2014). In comparison, Litman and Doherty (2011)
suggest averaged values of 20170.003/pkm. Detailed calculations for Copenhagen considering
residential property values, health treatment costs, sick leave days, and premature deaths,
result in an urban noise cost of 20170.011/pkm (Center for Transport Analytics 2017). This
value is adjusted to the average European area GDP at 60% of Denmark’s GDP
Table 4: Cost assessment noise
Assessment Cars Cost external Cost private Reference
Noise, general 20170.007/pkm 0 Litman and Doherty, 2011
Assessment Bicycles Cost external Cost private Reference
Noise, general 0 0 Litman and Doherty, 2011
Assessment Walking Cost external Cost private Reference
Noise, general 0 0 Litman and Doherty, 2011
4. Soil and water quality
In the only assessment that seeks to quantify water pollution and hydrologic impacts,
including oil drips, de-icing, roadside herbicides, storage tank leakages, air pollution
settlement; as well as increased impervious surfaces, concentrated runoff, wetland loss,
shoreline modification and constructions along shorelines (hydrolic), Litman and Doherty
(2011) calculate a cost of20170.006/pkm. This value is used for the EU (Table 7), at 74% of
Canada’s GDP, corresponding to 20170.005/pkm. Bicycle and walking are likely to incur
significantly more limited contamination impacts.
Table 5: Cost assessment soil and water pollutants
Assessment Cars Cost external Cost private Reference
Pollutants to soil and water 20170.005/pkm 0 Litman and Doherty, 2011
Assessment Bicycles Cost external Cost private Reference
Pollutants to soil and water <€20170.001/pkm 0 Authors
Assessment Walking Cost external Cost private Reference
Pollutants to soil and water <€20170.001/pkm 0 Authors
5. Land use and infrastructure
The IEA (2013) suggests that space requirements for transport infrastructure are in the order
of 25 million paved road km as well as 44,500 km2 of parking space to 2050. This entails an
estimated average annual cost of about US$2017843 billion (road construction) and US$2017225
billion (parking). Assuming that most parking and a higher share of road construction (80%)
are necessary to meet the growing transport demand of passenger cars, the annual cost of new
infrastructure (€2017801 billion) can be compared to the additional transport demand it enables
(64.5 trillion pkm). Assuming linear growth in new transport capacity, total new capacity
(42.5 trillion pkm by 2050) will accommodate transport demand growth of 20 trillion pkm per
year, if averaged over 40 years. This results in a cost of €20170.040/pkm (global average). It is
evident that the cost of land use for cycling infrastructure and parking, or boardwalks for
walking, is only a fraction of the cost of automobility (Erznoznik et al. 2014), here estimated
at an estimated 10% of car infrastructure (€20170.003/pkm). Notably, the value of lost
ecosystem services as well as agricultural productivity, which may not be reflected in market
prices, would have to be added.
To reflect the economic value of land used for infrastructure, Litman and Doherty (2011)
suggest a value for Canada that is 20170.015/pkm. The equivalent cost for bicycles/walking is
20170.001/pkm. Parking also incurs a land cost that is partially private (residential parking)
partially external (off-street parking that is uncharged), amounting to €20170.029/pkm (private)
and20170.027/pkm (external). Litman and Doherty (2011) values are adjusted to European
area GDP, at 76% of Canada’s GDP. This results in new infrastructure cost of20170.030/pkm
(cars, external), 20170.002/pkm (cycling/walking, external) as well as 20170.022/pkm (car
private). Roadway land use cost is in the order of 20170.011/pkm (car external) and <
20170.001/pkm for bicycles and walking. Parking land use costs are 20170.021/pkm for cars
(external) and €20170.022/pkm (private). Again, these are < €20170.001/pkm for bicycles, while
there is no parking cost for walking.
Ecosystem services lost as a result of traffic infrastructure construction are not considered
here though their value is potentially significant (Costanza et al. 1997).
Table 6: Cost assessment of land use
Assessment Cars Cost external Cost private Reference
New infrastructure construction
20170.030/pkm 0 IEA 2013
Roadway land use 20170.011/pkm 0 Litman and Doherty, 2011
Parking land use 20170.021/pkm €20170.022/pkm Litman and Doherty, 2011
Lost ecosystem services ? 0 -
Assessment Bicycles Cost external Cost private Reference
Bicycle track construction 20170.002/pkm 0 Erznoznik et al. 2014
Roadway land use <€20170.001/pkm 0 Litman and Doherty, 2011
Parking <€20170.001/pkm <€20170.001/pkm Litman and Doherty, 2011
Lost ecosystem services ? 0 -
Assessment Walking Cost external Cost private Reference
Boardwalk construction 20170.002/pkm 0 Authors
Boardwalk land use <€20170.001/pkm 0 Authors
Lost ecosystem services ? 0 -
6. Traffic infrastructure maintenance
As IEA (2013) highlights, the global annual cost of maintaining roads is in the order of
US$2017450 billion, and increasing with the extent of road systems. Korzhenevych et al. (2014)
calculated a car-related deterioration cost in the EU28 of 20170.004/pkm, i.e. not including
other aspects of traffic infrastructure maintenance (Table 6). Both cycling and walking will
incur a cost that is only a fraction of the vehicle-related maintenance cost, and hence lower
than <€20170.001/pkm.
Table 7: Cost assessment of traffic infrastructure maintenance
Assessment Cars Cost external Cost private Reference
Maintenance costs 20170.004/pkm 0 Korzhenevych et al. 2014
Assessment Bicycles Cost external Cost private Reference
Maintenance costs <€20170.001/pkm 0 Authors
Assessment Walking Cost external Cost private Reference
Maintenance costs <€20170.001/pkm 0 Authors
7. Resource requirements
Korzhenevych et al. (2014) suggest, for the EU28, that the marginal costs for cars may vary
between 20100.005/vkm to 20100.019/vkm, depending on vehicle size, Euro-class and
environment (urban/rural/motorways). An average value for passenger cars is about
20170.007/pkm. There is limited data for bicycle production, though a study in the USA
suggests that this cost may be close to two orders of magnitude lower (Dave 2010) (Table 8).
Table 8: Cost assessment of up and downstream processes
Assessment Cars Cost external Cost private Reference
Resource requirements 20170.007/pkm 0 Korzhenevych et al. 2014
Assessment Bicycles Cost external Cost private Reference
Energy requirements lifecycle <€20170.001/pkm 0 Dave 2010
Assessment Walking Cost external Cost private Reference
Resource requirements <€20170.001/pkm 0 Authors
8. Vehicle operation
A study by German car association ADAC (2017) estimates that the private cost of vehicle
operation is in the order of 20170.266/pkm to 20170.364/pkm (compact cars), including
depreciation, oil and tire wear, inspections and maintenance, oil and fuel costs. In Denmark,
fuel costs, engine oil, tires, repair and maintenance, as well as car value depreciation are
estimated at 20170.280/pkm, for the entire car fleet. An average amount of20170.250/pkm is
used to represent European values, considering that in many European countries, cars are
smaller than in Germany (Table 9). In comparison, the cost of bicycle operation may be one
tenth of the cost of a car, and has been estimated to amount to 20170.040/pkm in Denmark
(Center for Transport Analytics 2017). Including the cost of breakdowns, the overall cost for
bicycle operation in Denmark has been quantified at 20170.078/pkm, which, adjusted to
European area values at 60% of Danish GDP corresponds to 20170.047/pkm (Table 9). The
cost of footwear for walking is based on Litman and Doherty (2011), 20170.056/pkm, and
adjusted to European area values at 76% of nominal GDP value (€20170.041/pkm).
Table 9: Operational costs
Assessment Cars Cost external Cost private Reference
Vehicle operation costs 0 20170.250/pkm Authors
Assessment Bicycles Cost external Cost private Reference
Bicycle operation costs 0 20170.047/pkm Center for Transport Analytics 2017
Assessment Walking Cost external Cost private Reference
Walking cost 0 20170.041/pkm Litman and Doherty, 2011
9. Travel time
There are considerable differences in travel cost derived from value of time studies, and these
are generally not comparable (Hensher 2009; Shires and de Jong 2009). In Denmark, where
considerable efforts have been made to determine the travel time cost associated with
different transport modes, the marginal value of travel time of car drivers is €21.38/hour for
driving time and €32.14/hour for delays; while the time of cyclists is valued at €12.37/hour
for cycling and €18.69/hour for delays (Center for Transport Analytics 2017). At average
speeds of 50 km/h for cars, and 16 km/h for bicycles, the Center for Transport Analytics
(ibid.) proposes a time travel cost of €20170.422/pkm and for drivers, and of €20170.790/pkm for
cyclists. Assuming identical time values for cycling and walking, the cost of walking is
20172.107/pkm, at a speed of 6km/h (Center for Transport Analytics 2017, Realise 2018). Note
that a wide range of higher and lower travel time values have been reported for the EU (Shires
and de Jong 2009). Calculations in this paper are based on the progressive work of the Danish
Center for Transport Analytics (2017), and adjusted to EU values by using ratios of respective
nominal GDPs per capita. This results in averaged travel time values of20170.253/pkm (cars),
20170.474/pkm (bicycles) and €20171.264/pkm (walking) (Table 10).
Table 10: Travel time
Assessment Cars Cost external Cost private Reference
Travel time value 0 20170.253/pkm Litman and Doherty, 2011
Assessment Bicycles Cost external Cost private Reference
Travel time value 0 20170.474/pkm Litman and Doherty, 2011
Assessment Walking Cost external Cost private Reference
Travel time value 0 20171.264/pkm Center for Transport Analytics
2017, Realise 2018
10. Congestion
Korzhenevych et al. (2014) distinguish working and non-working time values, suggesting a
marginal congestion cost under conditions of overcapacity that is between20170.225/pkm in
rural and up to 20171.769/pkm in cities with >250,000 people. Even in near-capacity
conditions, the lowest cost of congestion is20170.098/pkm on motorways in rural areas. The
Victoria Transport Policy Institute (Litman & Doherty, 2011) distinguishes congestion from
barrier effects, which are defined as delays and lack of access that motor vehicle traffic
imposes on non-motorized travel (i.e. pedestrians and cyclists). Car congestion cost is valued
at 20170.015/pkm, and barrier effects at 20170.006/pkm (Table 11). Here, the lowest cost for
the European Union in urban contexts, at near capacity, is used (€20170.355/pkm), based on
Korzhenevych et al. (2014). Congestion effects on bicyclists and pedestrians are added based
on Litman and Doherty (2011), at 79% of Canadian values (€20170.005/pkm). Congestion and
barrier costs of cyclists are lower, at €20170.001/pkm (Litman and Doherty, 2011). These values
are also used for walking.
Table 11: Value of congestion/barrier effects
Assessment Cars Cost external Cost private Reference
Congestion 0 €20170.355/pkm Litman and Doherty, 2011
Barrier effects 0 20170.005/pkm Litman and Doherty, 2011
Assessment Bicycles Cost external Cost private Reference
Congestion 0 <€20170.001/pkm Litman and Doherty, 2011
Barrier effects 0 <€20170.001/pkm Litman and Doherty, 2011
Assessment Walking Cost external Cost private Reference
Congestion 0 <€20170.001/pkm Litman and Doherty, 2011
Barrier effects 0 <€20170.001/pkm Litman and Doherty, 2011
11. Health benefits
Litman and Doherty (2011) quantify external and private health benefits for cyclists at2017-
0.062/pkm each, i.e. the overall benefit of 2017-0.124/pkm is considered to be a shared
external (50%) and private (50%) benefit. They do not provide a calculation of prolonged life
benefits. More extensive work has been carried out in Denmark, determining benefits from
improved health in the order of €2017-0.223/pkm (private) as well as €2017-0.321/pkm (external)
(Center for Transport Analytics 2017). Prolonged life benefits amount to 2017-0.534/pkm
(private) while the external cost incurred in greater life expectancy is 20170.011/pkm as a
result of extended pension payments (ibid.). Danish values are used for calculation, as a ratio
of nominal European area GDP (World Bank 2017), resulting in improved health benefits of
2017-0.193/pkm (external) and 2017-0.134/pkm (private), as well as prolonged life effects of
20170.007/pkm (external) and 2017-0.320/pkm (private). Walking effects on health have not
been assessed in similar detail, but both Litman and Doherty (2011) and Realise (2018)
consider these to be at least twice as high as the health effects for cycling (Table 12).
Table 12: Health costs bicycle
Assessment Bicycle Cost external Cost private Reference
Improved health -0.193/pkm €-0.134/pkm Center for Transport Analytics 2017;
Litman and Doherty, 2011
Prolonged life €0.007/pkm €-0.320/pkm Center for Transport Analytics 2017
Assessment Walking Cost external Cost private Reference
Improved health -0.386/pkm €-0.268/pkm Center for Transport Analytics 2017;
Litman and Doherty, 2011
Prolonged life €0.014/pkm €-0.640/pkm Center for Transport Analytics 2017
12. Accidents (collisions)
The most significant cost of traffic collisions is associated with fatal accidents. The EPA
recommends that a statistical life be valued at US$2008$7.9 million or 2017$8.3 million, and
that “analyses […] quantify mortality risk reduction benefits regardless of the age, income, or
other population characteristics of the affected population” (EPA 2017: no page). In
comparison, Korzhenevych et al. (2014) suggest a value of €20101.87 million per traffic fatality
(EU28), as well as 2010243,000 per injury, and201018,700 per slight injury (including direct
and indirect economic costs). These translate into a car accident cost of €20100.002/pkm for the
EU28 (average value; Korzhenevych et al. 2014). This value is low as car accident damages
will be largely covered by insurance. Detailed national studies arrive at higher costs. Litman
and Doherty (2011) estimate that 37% of the crash cost are not covered by insurance in
Canada, and suggest an external cost of20170.041/pkm (car) and 20170.001/pkm (bicycle) as
well as a private cost of 20170.054/pkm (car/bicycle). For the European area, the lower
estimate by Korzhenevych et al. (2014) is used for cars. Notably, this omits the private cost of
pain, grief and trauma, i.e. aspects not covered by insurances. For bicycles and walking,
Litman and Doherty (2011) values are used, as these represent a private cost not necessarily
covered by insurance. The resulting crash cost is20170.085/pkm (external) and €20170.085/pkm
(private). These values are adjusted to European area GDP (Table 13).
Table 13: Accident costs
Assessment Car Cost external Cost private Reference
Collisions/crashes €20170.002/pkm ? Korzhenevych et al., 2014
Assessment Bicycle Cost external Cost private Reference
Collisions/crashes <€20170.001/pkm €20170.066/pkm Litman and Doherty, 2011
Assessment Walking Cost external Cost private Reference
Collisions/crashes <€20170.001/pkm €20170.066/pkm Authors
13. Perceived safety and discomfort
Cyclists and pedestrians are exposed to significant perceived traffic risks, noise and exhaust
smells. The cost of these aspects may be assessed on the basis of willingness to pay for
avoidance or willingness to accept exhaust, as well as on the basis of detours cycled to reduce
levels of discomfort. Tilahun et al. (2007) found, for instance, that cyclists are willing to travel
up to 20 minutes more to access dedicated bicycle trails. Only one study appears to quantify
the costs of perceived safety and discomfort, based on an assessment of detours cycled as a
result of perceived traffic risks, noise and exposure to exhaust fumes (Gössling et al. 2018).
Cyclists in Germany and Austria reported to cycle 6.4% longer distances to increase their
safety and to avoid noise and exhaust fumes. The study also assessed willingness-to-pay for
the avoidance of and willingness to accept exhaust fumes. Median WTA values translated into
a median per pkm cost of 20170.240/pkm, with a mean value of 20170.018/pkm. Perceived
safety, noise, and exhaust smells may also affect drivers, but these effects have not as yet been
quantified. At 75% of the averaged German/Austrian GDP, the European area cost of
perceived cycle safety and discomfort is20170.014/pkm (Table 14). In the absence of data, it
is assumed that the cost imposed on walking is identical, though given the lower speed of
pedestrians (6km/h, compared to 16km/h for cyclists), exposure time is higher per km, at
20170.036/pkm. Car drivers also impose a cost on each other, but there is no data to quantify
this cost.
Table 14: Costs of traffic risks, noise and exhaust fumes
Assessment Car Cost external Cost private Reference
Perceived safety and discomfort ? ? -
Assessment Bicycle Cost external Cost private Reference
Perceived safety and discomfort - 20170.014/pkm Gössling et al. 2018
Assessment Walking Cost external Cost private Reference
Perceived safety and discomfort - 20170.036/pkm Authors
14. Quality of life, branding and tourism
Quality of life, branding and tourism effects associated with cities that have high bicycle and
walking shares have been confirmed, but not quantified (Table 15). COWI and City of
Copenhagen (2009) estimated that cycling has a branding and tourism effect of2008-
0.003/pkm for Copenhagen. Such effects are mostly relevant in urban contexts, and have
more limited importance in rural contexts. It remains currently difficult to assign a value to
quality of life, branding and tourism.
Table 15: Quality of life, branding and tourism costs
Assessment Car Cost external Cost private Reference
Quality of life
Branding & tourism
Assessment Bicycle Cost external Cost private Reference
Quality of life
Branding & tourism
Assessment Walking Cost external Cost private Reference
Quality of life
Branding & tourism
... L'utilisation du vélo est un enjeu de santé publique Tainio et al., 2016) et de transition écologique (Courbe, 2020) constituant un impératif des politiques d'aménagement urbains. Dans une analyse coûts-bénéfices à l'échelle de l'Union Européenne, Gössling et al. (2019) estiment ainsi que chaque kilomètre parcouru en voiture génère un coût social de 0,11€, tandis que la même distance parcourue à vélo serait à l'origine d'un bénéfice social de 0,18€, en partie grâce aux bienfaits sur la santé. ...
Full-text available
Nous étudions, pour la ville de Tours, l'impact de la fermeture du Pont Wilson aux voitures entre le 13 août 2020 et le 24 mai 2021 sur la fréquentation du pont par les vélos. Nous disposons de données quotidiennes de compteurs vélos entre le 2 juin 2016 et le 24 mai 2021, de variables de contrôles calendaires, climatiques et sanitaires. En recourant à la méthode des séries temporelles segmentées, nous trouvons que cette fermeture est associée à une hausse de 7,66 % (IC95 % : +0,8 % ; +14,8 %) du trafic cycliste. En raison de certaines limites inhérentes à nos données (e.g., changements de compteurs vélos, aménagements cyclistes transitoires dans la ville de Tours), il semble toutefois difficile d'imputer la totalité de l'effet obtenu à la fermeture du Pont Wilson.
... shows the cumulative distribution function of travel time change under scenario 2, for drivers who are able to at least partially shift away from car use. Actually, almost half of them would experience a net decrease in daily travel time if they used a low-emission17 The health benefits of walking and cycling induced by the increase in physical activity have been shown to significantly outweigh the risks due to pollution inhalation and cyclists' accidents(Rojas-Rueda et al., 2011;Rabl and de Nazelle, 2012;Gössling et al., 2019) 23 mode. The other half would experience an increase in daily travel time of between 0 and 20 minutes. ...
Full-text available
Tackling car emissions in urban areas: Shift, Avoid, Improve
... ). Der anschließende und bis heute andauernde Ausbau wurde seit den 1960er Jahren von ingenieurs-und wirtschaftswissenschaftlichen Überlegungen geprägt und bezieht sich auf die Leistungsfähigkeit der Infrastruktur als Grundlage für Wirtschaftswachstum. Dabei fließen insbesondere Fahrtzeiteinsparungen in die Betrachtung von Kosten-Nutzen-Analysen ein, die jedoch aufgrund der Umwandlung in längere Wege(Metz 2008) oder des nicht Einbeziehens relevanter, aber schwer zu monetarisierender Faktoren (z. B. Folgen von Emissionen) in der Kritik stehen(Gössling et al. 2019). Das Leitbild der integrierten Verkehrsplanung, dessen Ursprünge in den 1920erJahren liegen(Schwedes 2017), kam insbesondere in den 1960er und 1970er Jahren als Gegenentwurf zur Anpassungsplanung mit dem Ziel auf, die soziale Teilhabe und den gesellschaftlichen Austausch sicherzustellen. ...
Full-text available
Der interdisziplinäre Forschungsverbund LILAS (Lineare Infrastrukturen im Wandel) skizziert in dieser Publikation interdisziplinäre Perspektiven auf sowie konzeptionelle Planungs- und Gestaltungsansätze für die Weiterentwicklung kanalisierter Gewässer und Stadtstraßen für eine nachhaltige Transformation linearer Infrastrukturen in urbanen Räumen. Das Diskussionspapier dokumentiert einen Zwischenstand des laufenden Forschungsvorhabens. Die Publikation beschreibt zunächst den thematischen Fokus, das Verständnis sowie den Status Quo und dessen Entstehungskontext, um wesentliche Faktoren für die Planung und die Transformation blauer (kanalisierte Gewässer) und grauer (Stadtstraßen) Infrastrukturen zu erfassen. Neben der Darstellung ausgewählter Leitbilder der Planung sowie der Richtlinien und Vorgaben für die Gestaltung von Straßen und Gewässern werden auch zentrale Herausforderungen für die Transformation dieser Räume benannt. Konzeptuelle Bezugspunkte für die Analyse der Transformation sind das Multi-Level-Perspective Modell (MLP) und das Modell der socio-ecological-technical systems (SETS), die auf das räumliche Handlungsfeld linearer Infrastrukturen angewandt werden. An diese Grundlagen und Herleitungen knüpfen die Autor:innen mit der Erarbeitung von sektorübergreifenden Perspektiven auf die räumliche Transformation linearer Infrastrukturen an. Durch die leitenden Prinzipien der Multifunktionalität und der Multicodierung sollen die notwendigen technischen Ansprüche mit sozialen Nutzungsmustern und Resilienz steigernden ökologischen Maßnahmen für lineare Infrastrukturen integriert betrachtet werden. Eine übergreifende Typologisierung urbaner Infrastrukturkorridore beschreibt deren zentrale Funktionen und die Teilräume mit ihren spezifischen Kernaufgaben. Drei mögliche räumliche Transformationsansätze für Stadtstraßen und kanalisierte Gewässer runden die Betrachtung ab – illustriert durch Beispiele aus der internationalen Praxis. Das Spektrum reicht dabei von punktuellen Einzelmaßnahmen über veränderte Raumaufteilungen im Infrastrukturkorridor bis hin zur grundlegenden Umgestaltung von Flächen. In this publication, the interdisciplinary research network LILAS (Linear Infrastructures in Transition) outlines interdisciplinary perspectives on and conceptual planning and design approaches for the further development of canalised water bodies and urban roads for a sustainable transformation of linear infrastructures in urban spaces. The discussion paper documents an interim status of the ongoing research project. The publication first describes the thematic focus, the understanding of the subject as well as the status quo and the context of its development in order to summarise essential factors for the planning and transformation of blue (canalised water bodies) and grey (urban roads) infrastructures. In addition to presenting selected guiding principles of planning as well as guidelines and specifications for the design of roads and water bodies, key challenges for the transformation of these spaces are also identified. Conceptual reference points for the analysis of transformation are the Multi-Level-Perspective Model (MLP) and the socio-ecological-technical systems (SETS) model, which are applied to the spatial context of linear infrastructures. The authors build on these foundations and derivations by developing cross-sectoral perspectives on the spatial transformation of linear infrastructures. Through the guiding principles of multifunctionality and multicoding, the necessary technical requirements are to be considered in an integrated way with social usage patterns and resilience-increasing ecological measures for linear infrastructures. An overarching typology of urban infrastructure corridors describes their central functions and the subspaces with their specific core tasks. Three possible spatial transformation approaches for urban roads and canalised water bodies round off the analysis - illustrated by examples from international practice. The spectrum ranges from selective individual measures to transformed spatial divisions in infrastructure corridors to the fundamental transformation of entire areas.
... Using cost-benefit analysis in non-traditional ways, such as comparing different transport modes, allows for new perspectives on transport investment decisions (Gössling and Choi 2015). In Europe, for example, it was found that cycling and walking provide a societal benefit of US$0.21 and US$0.42 per kilometer, respectively, while car travel represents a cost to society of US$0.12 per kilometer on average (Gössling et al. 2019). In terms of the number of kilometers driven, the external cost of the car represents US$565 billion per year, while cycling and walking bring benefits of US$27 billion and US$75 billion per year, respectively. ...
Full-text available
Uneven distribution of employment opportunities and services, the imbalances in access to housing and job opportunities for the entire population, and the difficulties of providing access to urban services for all urban dwellers may also increase socio spatial inequalities. Chapter 3 describes emerging issues related to the tradeoff between affordable housing location and transport and the need of promoting integrated planning as essential for economic development in Latin American cities and a source of opportunities for low-income populations. Many of the urban transport projects in Latin American cities have prioritized the development of mass transit corridors, which generate better access conditions for hundreds of thousands of low-income citizens. However, in some cases these projects can have an unintended impact of decreased affordability of housing options located near the new system, making access to opportunities more difficult to the city’s poorest. The degree of displacement or gentrification associated with the introduction of mass transit corridors remains unknown given the lack of research on this topic, as indicated by the related gap in the literature. Studies in which the socioeconomic and socio-spatial distribution changes occurring due to the implementation of mass transit projects are urgently needed. Additionally, land value increments generated on property values are not often captured by the public sector to leverage the financing of mass transit projects or their expansion. The experience in the region suggests that coordination between transport and land use planning is difficult due to a mismatch and variation in the implementation and development timelines of each, low technical capacity, and a lack of funding for TOD projects. TOD projects provide the opportunity to strength the coordination between the transportation, land use planning and housing sectors. It is important that each city defines a TOD policy, with pilot projects based on the previous research into the dynamics of real estate as well as the land and housing markets, within a long term planning process that includes citizen participation. TOD pilot projects can certainly improve the integration of transportation planning and land use planning. TOD projects in the region should be employed as a strategy to promote value capture mechanisms, including cross housing subsidies in which the promotion of affordable housing near transit systems becomes a reality Affordable housing initiatives require to become more diverse and innovative in order to increase the quality of these projects through a portfolio of options linked to mass transit and other infrastructure investments that increase the accessibility for their residents. As in the case of transportation infrastructure projects, it is important that those projects include accessibility indicators to evaluate the effects of these investments on the poor. The recent experience with the implementation of Cable Cars that include slum upgrading measures, and the generation of new affordable housing units with infill development measures, constitute an innovation in the region.
... Another reason for this phenomenon is erosion when part of the sand glides along the hill. In this case, beach formation is caused by two different processes, also known from other studies (Zhu and Fan 2018;Gössling et al. 2019). Moreover, beach conditions are quite suitable for forming patches of vegetation, even ruderal, under conditions of high human affluence. ...
Full-text available
This study aims to analyze the impact of outdoor recreational activities on the status of forest plant communities based on long-term data. The study was conducted in 2008–2019 in the vicinity of St. Petersburg (Russian Federation). A total of 1,284 transect lines 50–500 m long, 6 m wide, and at 25 m distance between each other were laid. Since 2011, the study area received the status of Specially Protected Area. According to the data obtained, maps were drawn showing an increase or decrease in the degradation rate due to recreation activities. Following the closure of vehicle access to the study area in 2011, the flow of vacationers nearly halved (25 people per 100 m of shoreline, p ≤ 0.05 vs. 2008). In 2018, their number virtually did not change (p > 0.05 vs. 2012). The emergence of parking lots and the prohibition on motor vehicles did not affect the number of vacationers on sand beaches (p > 0.05 between 2018 and previous years). However, their number significantly decreased (2-fold, p ≤ 0.05 between 2018 and 2008) on undulating and gently undulating plains. Recreation impacts on the condition of various landscapes were graded according to the state of the forest site.
Full-text available
Especially in urbanized contexts, mobility has long been faced with environmental challenges that seem to determine the inevitable marginalization of the traditional car use. The complex relationship between individuals and cars (in its environmental, cultural and economic implications) seems to still await a complete exploration, also due to the innumerable ways of the use of vehicles themselves. The essay reflects on the use of historic cars, a particular form of mobility considered able to connect multiple knowledge, as well as in the light of its main aspects: movement, its collective and shared representation, therefore the experiences and practices in which it is translated. Furthermore, proposals are formulated to enhance the cultural and economic role of this mobility in Italy, a country with a long automotive tradition.
There is growing evidence that longer travel time by private car poses physical and mental risks. Individual-level obesity and diabetes, two of the main public health challenges in low- and middle-income contexts, could be associated to city-level travel times by car. We used individual obesity and diabetes data from national health surveys from individuals in 178 Latin American cities, compiled and harmonized by the SALURBAL project. We calculated city-level travel times by car using the Google Maps Distance Matrix API. We estimated associations between peak hour city-level travel time by car and obesity and diabetes using multilevel logistic regression models, while adjusting for individual characteristics and other city-level covariates. In our study we did not observe a relationship between city-level peak-hour travel time by car and individual obesity and diabetes, as reported in previous research for individual time spent in vehicles in high-income settings. Our results suggest that this relationship may be more complex in Latin America compared to other settings, especially considering that cities in the region are characterized by high degrees of population density and compactness and by a higher prevalence of walking and public transportation use.
Full-text available
This work presents an economic analysis that illustrates the feasibility and the possible benefits related to the replacement of internal combustion vehicles (ICVs)by electric vehicles (EVs) public transportation in medium-sized cities. According to the current operating conditions, we calculate the cost of operating internal combustion vehicles and compare them with a selected EV with approximately the same passenger capacity. We calculate the CO 2 emissions in both cases. Moreover, for the case of EV, we analyze two scenarios: 1) Use the grid to charge the EV and 2) a grid-connected photovoltaic system using the available land in the store terminals. The net present value (NPV) indicates the feasibility of two EV replacement scenarios: EV fleet using energy from the grid and EV fleet with a PV system energy generation interconnected to the grid. The economic analysis considers the different prices of electricity according to the existing tariff schemes in Mexico. Due to the electricity generation mix in Mexico, in the case of CO 2 emissions, the reduction is not as expected in the only grid connection; but a PV system reduces more than 30% CO 2 . This analysis was carried out for two medium-sized cities: Morelia, Michoacán, and Cuernavaca, Morelos, both in Mexico.
Transportation networks, from bicycle paths to buses and railways, are the backbone of urban mobility. In large metropolitan areas, the integration of different transport modes has become crucial to guarantee the fast and sustainable flow of people. Using a network science approach, multimodal transport systems can be described as multilayer networks, where the networks associated to different transport modes are not considered in isolation, but as a set of interconnected layers. Despite the importance of multimodality in modern cities, a unified view of the topic is currently missing. Here, we provide a comprehensive overview of the emerging research areas of multilayer transport networks and multimodal urban mobility, focusing on contributions from the interdisciplinary fields of complex systems, urban data science, and science of cities. First, we present an introduction to the mathematical framework of multilayer networks. We apply it to survey models of multimodal infrastructures, as well as measures used for quantifying multimodality, and related empirical findings. We review modeling approaches and observational evidence in multimodal mobility and public transport system dynamics, focusing on integrated real-world mobility patterns, where individuals navigate urban systems using different transport modes. We then provide a survey of freely available datasets on multimodal infrastructure and mobility, and a list of open-source tools for their analyses. Finally, we conclude with an outlook on open research questions and promising directions for future research.
Full-text available
Active transport policies are being developed across Europe designed to have health and environmental benefits. There is little evidence of impact on physical activity of active transport strategies which modify the built environment. Cycling represents one virtually carbon-neutral form of transport that can help to address declining levels of exercise. A systematic literature review of experimental or observational studies that objectively evaluated the effect of the built environment on cycling. A total of 21 studies met the inclusion criteria, all of which were observational studies. Eleven studies identified objectively measured environmental factors with a significant positive association with cycling. The environmental factors identified as being positively associated with cycling included presence of dedicated cycle routes or paths, separation of cycling from other traffic, high population density, short trip distance, proximity of a cycle path or green space and for children projects promoting 'safe routes to school'. Negative environmental factors were perceived and objective traffic danger, long trip distance, steep inclines and distance from cycle paths. Of the seven studies which focused primarily on the impact of cycle routes, four demonstrated a statistically significant positive association. Although the study identified environmental factors with positive and negative associations with cycling behaviour, many other types of environmental policies and interventions have yet to be rigorously evaluated. Policies promoting cycle lane construction appear promising but the socio-demographic distribution of their effects on physical activity is unclear. The wider impact of active transport policies on health and inequalities across Europe must be explored.
Full-text available
The US Preventive Services Task Force recently recommended screening all adult patients for obesity due in part to the strong association between obesity and numerous chronic diseases. However, how obesity affects health-related quality-of-life (HRQL), particularly for persons without any chronic diseases, is less clear. The relationship between obesity and HRQL was examined using data from the 2000 Medical Expenditure Panel Survey. Respondents > or =18 years were classified as underweight, normal weight, overweight, class I obesity, and class II obesity based on their BMI. HRQL was measured by the 12-item Short Form physical and mental summary scores (PCS-12 and MCS-12, respectively) and EuroQol EQ-5D index and visual analogue scale (EQ VAS). The impact of obesity on HRQL was examined through multivariate regression, adjusting for sociodemographics and disease status. After adjustment, HRQL decreased with increasing level of obesity. Compared to normal weight respondents, persons with severe obesity had significantly lower scores with scores on the PCS-12, MCS-12, EQ-5D index, and EQ VAS being 4.0, 1.1, 0.073, and 4.8 points lower, respectively. Such decrements of HRQL for severe obesity were similar to the decrements seen for diabetes or hypertension. Persons with moderate obesity or who were overweight also had significantly lower HRQL scores, particularly on the PCS-12 and EQ-5D index. Underweight persons also had lower MCS-12 and EQ VAS scores. Persons with obesity had significantly lower HRQL than those who were normal weight and such lower scores were seen even for persons without chronic diseases known to be linked to obesity.
Research in transportation, urban design, and planning has examined associations between physical environment variables and individuals' walking and cycling for transport. Constructs, methods, and findings from these fields can be applied by physical activity and health researchers to improve understanding of environmental influences on physical activity. In this review, neighborhood environment characteristics proposed to be relevant to walking/cycling for transport are defined, including population density, connectivity, and land use mix. Neighborhood comparison and correlational studies with nonmotorized transport outcomes are considered, with evidence suggesting that residents from communities with higher density, greater connectivity, and more land use mix report higher rates of walking/cycling for utilitarian purposes than low-density, poorly connected, and single land use neighborhoods. Environmental variables appear to add to variance accounted for beyond sociodemographic predictors of walking/cycling for transport. Implications of the transportation literature for physical activity and related research are outlined. Future research directions are detailed for physical activity research to further examine the impact of neighborhood and other physical environment factors on physical activity and the potential interactive effects of psychosocial and environmental variables. The transportation, urban design, and planning literatures provide a valuable starting point for multidisciplinary research on environmental contributions to physical activity levels in the population.