Sustainable Urban Transportation: A model-based · PDF file · 2004-08-30Sustainable Urban Transportation K.Fedra 1 of 36 Sustainable Urban Transportation: A model-based approach

Sustainable Urban Transportation K.Fedra

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Sustainable Urban Transportation:

A model-based approach.

DDr. Kurt Fedra

Director, ESS GmbH

A2352 Gumpoldskirchen, AUSTRIA

[email protected] http://www.ess.co.at

ABSTRACT

Transportation and its environmental impacts are a major component of urban

environmental management. At the same time, transportation and mobility are an

important part or urban economics and the quality of life. To analyse urban

transportation and its environmental impacts, a comprehensive, interdisciplinary

approach is needed. No single model can cover the range of spatial and temporal

scales and processes involved. This leads to a multi-tiered approach and a cascade

of models to describe alternative urban development and transportation scenarios

and their multi-criteria assessment and comparative analysis.

This paper describes the methodology and application examples of SUTRA,

Sustainable Urban Transportation (http://www.ess.co.at/SUTRA/) a City of Tomorrow

project under the EU Energy, Environment and Sustainable Development Research

Programme. The primary objective of SUTRA was to develop a consistent and

comprehensive model-based approach and planning methodology for the analysis of

urban transportation problems, to support design strategies for sustainable cities.

This includes an integration of socio-economic, environmental and technological

concepts including the development, integration, and demonstration of simulation

tools to improve scenario design, assessment and policy level decision support.


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Combining an indicator based approach with simulation models ranging from techno-

economic optimisation to street canyon modelling, used for scenario analysis, socio-

economic and environmental impact assessment, and a web-based public

information component, the methodology ranges from awareness building end

educational aspects for citizens and stake-holders participating in urban decision

making processes to detailed technical modelling and optimisation results for the

planning professional.

The models employed in SUTRA describe urban development scenarios, their

implications for the transportation system, a range of economic and environmental

impacts. Special emphasis was given to emissions and ambient air quality, and in

consequence, population exposure and public health consequences, and accidents.

The models used range in scale from street-canyon models with time horizons in

hours, to regional photochemical models considering seasonal patterns,

transportation models describing the city and its environs, techno-economic models

for long-term city-level or regional technological and energy analysis, that estimate,

inter alia, the market penetration of new transportation technologies over 20 to 30

years planning horizons. The links between the models, as well as the initial scenario

assumptions and the overall evaluation framework are formulated in terms of

indicators.

The city case studies of the SUTRA project (Buenos Aires, Gdansk, Genoa, Geneva,

Lisbon, Tel Aviv and Thessaloniki) differ widely in terms of culture, environmental

conditions, size, economic structure, social composition and demography. The

modelling approach developed was tested against this range of cities to ensure

general applicability, and at the same time to provide data for a comparative analysis

of the scenarios explored.


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INTRODUCTION

Transportation problems are among the most pressing strategic development

problems in many cities, often a major constraint for long-term urban development in

general, and very closely related to land development, economic structure, energy

policies, and environmental quality. Since all citizens are either enjoying the

transportation system or, and often at the same time, suffering from it, it is an

important element of the urban quality of life. The problems to be solved are the

inefficiency of urban transportation systems and underlying land use patterns, which

negatively affect quality of life, economic efficiency, and the environment; the high

(and often hidden) costs of urban transportation in both socio-economic and

environmental terms; and in particular the environmental consequences both in terms

of physical aspects that include land and resource use, ecological aspects, and

human health problems.

Efficient tools for comprehensive strategic analysis that are directly useful to city

administrations are lacking. New strategies for sustainable mobility require well

balanced combinations of measures with impacts on

• improved land-use/economic development planning;

• improved planning, management and use of transport infrastructures and

facilities; incorporation of the real costs of both infrastructure and environment

in investment policies and decisions and also in user costs;

• development of public transport and improvement of its competitive position;

continued technical improvement of vehicles and fuels;

• incentives for the use of less polluting fuels; promotion of a more

environmentally rational use of the private car, including behavioural changes.

These problems can only be addressed with a consistent and comprehensive

approach and planning methodology that helps to design strategies for sustainable

cities. This has to include an integration of socio-economic, environmental and

technological concepts including the development, integration, and demonstration of

methodologies to improve forecasting, assessment and strategic policy level decision

support (Fedra, 2000a,b,c) . No single model can cover the entire range of


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processes, and the spatio-temporal scales characterising them, leading to a multi-

tiered approach (Fedra and Haurie, 1999). The alternative of a loosely coupled set of

models for scenario analysis, embedded in a common framework of indicators and a

multi-criteria assessment methodology was developed in SUTRA.

From a technical perspective, the project did develop and apply and common set of

indicators for urban sustainability for a baseline analysis, ranking and benchmarking

(with a larger set of cities across Europe) that ultimately feeds into a discrete multi-

criteria assessment and selection mechanism. These indicators, at the same time,

provided some of the links, keeping the loosely coupled nested or cascading

simulations models employed consistent. The analytical tools include:

• Techno-economic analysis and energy systems analysis and modelling using

well established modelling approaches such as MARKAL, are used identify

and evaluate cost effective transportation scenarios, consistent with the larger

economic and technological framework, and estimate the market penetration

of new transportation technologies (fleet composition).

• Traffic equilibrium modelling was used to evaluate alternative transportation

policies, including multi-modal systems, high-occupancy vehicles, park and

ride systems, and transportation telematics and their relation to land use,

technological development, socio-economic development, and spatial and

structural urban development (land use scenarios) in general.

• Emission modelling that translate the results of the transportation model such

as traffic frequencies and driving conditions together with the fleet composition

into the emission used in the air quality models

• Air quality modelling is used to translate emission scenarios into ambient air

quality estimates, ranging from street canyons to the regional scale, and

population exposure both for short-term events and for season and annual

time frames.

• A fuzzy rule based system was used to estimate public health impacts and the

probability and costs of accidents.


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• The economic assessment, using classical econometric valuation methods,

estimated the full individual and external costs of the alternative development

scenarios and their transportation strategies on this basis.

• Finally, a discrete multi-criteria method was used for a multi-criteria ranking

analysis, with the results compared to a Europe wide city benchmarking

exercise.

THE BASIC APPROACH

The basic objective of the project was to develop, and apply to a core group of seven

cities, a consistent and comprehensive approach and planning methodology for the

analysis of urban transportation problems. The ultimate goal is to design strategies

for sustainable transportation in sustainable cities, based on a multi-criteria

approach. This will include an integration of socio-economic, environmental and

technological concepts including the development, integration, and demonstration of

tools and methodologies to improve forecasting, assessment and policy level

decision support.

Transportation problems are among the most pressing urban development problems

and related environmental concerns in many cities, often a major constraint for urban

development in general. Sustainable transportation systems was therefore the main

orientation of the project. SUTRA's approach to urban sustainability and sustainable

urban transportation in particular is based on the well-accepted definition of

sustainable development set out in the Brundtland Report (World Commission on

Environment and Development, 1987): "Sustainable development is development

that meets the needs of the present without compromising the ability of future

generations to meet their own needs." In the most simple terms, however, we are

looking for efficient, affordable, and environmentally benign and equitable solutions.

Since the approach is based on the comparative analysis and ranking of scenarios,

there is no need for any absolute values here: what matters is the direction of

optimisation and solutions that are technically and politically feasible.


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The basic approach of SUTRA can be simplified as follows:

• Define a set of indicators for sustainable urban development;

• Evaluate the status quo of the participating cities as a baseline-scenario in

terms of these indicators;

• Define a set of likely or desirable development scenarios;

• Analyse these scenarios using a set of different models and assessment tools,

evaluate in terms of the indicators defined;

• Use a discrete multi-criteria optimisation approach to compare and evaluate

the alternatives, identify preferable strategies, benchmark and compare cities

to find generally applicable strategies;

• Communicate these strategies and the underlying information to policy

makers, major actors, and the general public.

This seemingly sequential approach however is implemented in iterative cycles,

Indicators of sustainable development

The philosophy and approach of SUTRA is based on the integration of a number of

different methods and models into a coherent and comprehensive assessment. The

overall common framework, that will guarantee a well structured analysis for direct

comparison of alternatives within and between cities, is defined by a set of indicators

of sustainable urban development, and urban transportation in particular. This

common set of indicators is identified or defined for all cities, and applied to a

benchmarking exercise involving a much larger set of urban conglomerates.


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Indicators defined, for example, by UNCED (United Nations Conference on

Environment and Development)/Agenda 21, UNEP (United Nations Environment

Programme), or the Dobriš Report and the indicators for sustainable cities defined by

the EEA (European Environment Agency) are used as a starting point for this activity.

Another major source is the Sectoral Infrastructure Project for the Transport Sector

(SIP) of the European Commission, which is part of the ESEPI (European System of

the European Pressure Indices) programme. This project elaborated driving force

and pressure indicators for the transport sector.

Urban development involves numerous activities and processes which are, in part,

actively planned and in part happen as a reaction to external forces and

development. Major processes include demographic change, in particular ageing and

migration, and urbanisation or land use change including urban growth and sprawl,

centralisation of material services especially in retailing, development of suburban

structures or the revitalisation of urban centres, decentralisation of information

services and employment opportunities exploiting information technologies,

technological change in the transportation sector such as zero-emission vehicles,

and many more. Opposite these developments, and trying to control or at least

influence them, are public policies and incentives, positive and negative, direct

regulatory interventions, and the business strategies of the private sector. They

include mechanism like taxation and subsidies, land use planning and zoning, the

developments of the real-estate market, the management of parking space, traffic

control from speed limits to provision of levels of services such as public

transportation or road building, pricing of these services, location of public institutions

from schools to hospitals, the closure of urban sectors to individual traffic, emission

limits or taxes, education, and many more.

Technically, and for the limited scope of transportation and emission control, there

are different options for controlling the environmental impact of traffic in cities, that

work both on the demand side as well as the supply side of the system. Measures

include changing transportation demand by appropriate spatial planning; to induce

different user behaviour, like using public transport; to better control traffic efficiency,


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i.e., limit street congestion; or to introduce new technologies with reduced or zero

emission. Some policies can be a mixture of these options like e.g. the introduction of

shared electric cars for urban short travels. Other policies could consist in providing

incentives for using reduced emission technologies for urban transport.

For each city, a set of likely development scenarios has been identified using

European policy and individual urban plans as the starting point. These scenarios

include both a common set, shared by all cities, as well as a number of specific,

individual options dictated by the peculiarities of each situation. They are expressed

as a set of indicators values as plausible relative changes from the baseline and

translated into model inputs for the simulation. A typical example would be land use

changes, that are represented by scaling the original origin-destination matrices of

the transport model, and modifying average distances or trip frequencies (e.g., based

on the spatial density of distribution of services) accordingly.

INDICATORS OF URBAN DEVELOPMENT

Indicators are quantities that give a schematic and informative representation of the

reality of complex systems. There are many different definitions of indicators. OECD

(Organisation for Economic Co-operation and Development), 1993, uses the

following “a parameter or a value derived from a parameter that gives information

with regard to a particular phenomenon” (OECD, 1993).

Indicators are thus instruments that give synthetic information by means of several

representations of a complex and wide phenomenon, thereby making clear a

situation or a characteristic that is not directly perceivable. They represent an

empirical model of the reality, implicitly assuming that a complex phenomenon could

be represented by a limited number of variables (Musu et al., 1998).

The indicators defined for and used in SUTRA were grouped according to the DPSIR

(Driving forces, pressures, states, impacts, responses) framework. The driving forces

indicators encountered the greatest changes with respect to the original TERM set of

indicators (EEA 1999a,b) and are therefore discussed in more detail. Driving forces,

and responses indicators provide information that enters the SUTRA models system


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as inputs; on the other side, impacts and pressure indicators are built on the basis of

model output.

DRIVING FORCES INDICATORS

Driving forces indicators measure the forces (how many people, where do they work

and live) that drive the actual demand of transport (km of passengers and freight).

SUTRA uses driving forces indicators as the main levers for scenarios: alternative

scenarios are defined by changes in the driving forces indicators. The scope of the

indicators chosen reflects the scope of the scenario analysis in SUTRA

(concentrating on demographic, economic, technological, an land use change)

The Driving Forces indicators describe the basic features of the city: the size and the

ageing structure of city population; the spatial distribution of urban functions and

resident population; the relative wealth of the city and the structure of its economy,

with particular attention to those features that are likely to influence most the demand

for transportation (employment in services, presence of high-tech activities); and the

tele-working and commuting patterns that characterise the city and contribute to

determine transport demand.

Demographic indicators measure the size and dynamism of the urban population:

population size and age distribution were used.

Large cities are characterised by different transport problems with respect to smaller

cities; a growing city needs to design new strategies to accommodate the growing

population (e.g., sprawling vs. increasing density in the city core) that needs to be

assessed and compared; individual mobility requirements vary with the age class an

individual belongs to. Transport intensity of urban society varies with the age

structure of its population; impacts of pollution on individual health vary with the age

class an individual belongs to. Impacts of pollution on public health also vary with the

age structure of its population.

Land-use indicators measures the spatial distribution of resident population and city

functions, e.g., where the people live and where they work/shop/go for leisure

activities. Literature on urban studies has long debated the impacts of urban sprawl

The value of the indicator capture the extent to which a city is sprawling/densifying.


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Economic indicators summarises information on the economic structures of the city:

GDP per capita [EUR, expressed in PPP, Purchasing Power Parity] were used. This

indicator summarises information on the relative wealth of the city, in comparison to

other cities. It is proposed to measure income in PPP, to control for differences in

prices. Cities generate traffic differently at different stages of economic development.

In particular, the GDP per capita affect the mobility rate, which is actually used in the

simulation of scenarios: Mobility rate [trip/day/capita] This indicators measures the

individual mobility demand. It is often strictly correlated to the level of income.

Employment in services [% of total employment] Service activities have different

traffic requirements, e.g. from manufacturing activities, and may have a higher

flexibility in terms of hours relative to rush hours. Employment on tele-working [% of

total employment] affects the number of trips to the working place.

PRESSURE INDICATORS

Pressure indicators collate information on the pressures on people and the

environment through emissions to the air, materials and vehicles movement,

consumption of natural resources and energy.

Indicators of passengers transportation, measuring transportation supply and

demand, include: Total passenger transport demand [pkm/year]; Public passenger

transport demand [pkm/year]; Average distance travelled, per person [pkm/

capita/year]

This indicators measure the actual transport intensity of the urban society.

Another set of pressure indicators are fuel consumption and traffic generated

emissions.

STATE INDICATORS

State indicators collate information on the State of the environment as determined by

Pressures. Changes in air quality and noise levels and increased fragmentation of

habitats are examples relevant for transport.

Atmospheric concentration of key pollutants: NOX, CO, PM10, O3 are expressed as

peak and average values, leading to inhabitants exposed [% of population; relative to


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EU air quality standards and averaged over one year]; Exceedances of air quality

standards [frequency of violations, yearly base] Specific definition, space and time

resolution varies with the pollutant. Similarily, population exposed to noise above 65

dB(A)[% of total population] measures the intensity of noise nuisance. It is calculated

overlaying the spatial distribution of noise that has to be mapped for a given traffic

result, and the population distribution and applying some weight between rush hours

and average; we then get population exposure normalised as a percentage of

population exposed to noise > 65 dB(A).

STRESS INDICATORS

Other disruptive effects of traffic on urban population include crowding: hours per

capita spent on overcrowded public transport [vehicle-hour/capita/year].. The

measure aggregates over all in-vehicle-times for all urban public transport links using

a density (passenger/m2) threshold. In the same way, time spent in traffic jams

(speed below 10km/h) is used.

IMPACT INDICATORS

Changes in State may lead to Impacts such as ill health, time losses, or increased

costs in general. Two types of indicators are used: economic indicators that measure

the direct and indirect costs of the transportation system, and physical indicators. For

comparison between cities, all of them are normalised as per-capita and per person-

km.

Primary (direct) costs of transportation system measures the costs such as fares or

operating costs including cleaning, maintenance and construction of infrastructures.

Unit costs are derived from the literature and scaled up or down using wage costs.

Secondary (external) costs of transportation system measure the external costs as

estimate of aggregate damage caused by transport, per capita [EUR/capita/year].

Mortality and morbidity caused by pollution generated by transport are expressed as

loss of productive time, and percentage of total costs (based on the methodology

used in Kuenzli et al., (2000), with a parallel method for accidents.


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RESPONSE INDICATORS

These Impacts finally lead to societal Responses to Impact of transports. Societal

Responses include Regulation (technical standards, movement restrictions, speed

limits); Taxes (fuel, road pricing, subsidies); Investment (public transport, transport

infrastructures); measures such as ‘smog’ warnings.

Car occupancy rate is important in the evaluation of the efficiency in the use of

private vehicles; a similar measure of efficiency is the Share of public/private

transport

The use of public transport instead of private vehicles is considered as a focal point

of the sustainability of an urban transportation system. The penetration rates of new

vehicle propulsion technologies can be affected by incentives or other regulatory

mechanisms, in addition to market forces:

SCENARIOS OF URBAN DEVELOPMENT

Major processes that strongly influence transportation demands, distance covered

and environmental and health impacts are demographic change, economic change,

technological change and land use development.

These processes have been modelled in different scenarios; in fact, each scenario is

defined in terms of change of one single parameter, in which, under assumptions

made on the basis of past trends and survey of existing literature, all relevant

variables describing those processes had been collapsed.

Parameters used in the scenarios are:

• growth and age of the population (demographic change);

• new services, information technology and teleworking (economic change);

• modal split, fleet composition, car occupancy rate and new vehicle propulsion

technologies (technological change);

• distribution of commercial and residential areas, building densities (land use

development).


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Common scenarios have been designed to represent general trends within plausible

limits, but representing the extreme points of these limits: this suggest that any

feasible future should be found within the interval explored.

Demographic change:

In SUTRA, the age structure of the population is represented by the percentage of

youth (age 0-18) to total population, percentage of working age population (19-64) to

total population, percentage of pensioners (over 65) to total population. The interval,

whose extremes represent the common scenarios and in which cities will select

values for the specific scenarios, is defined in terms of variation of percentage points;

common scenarios are:

- low-growth scenarios: youth share decreases by 5%, working age decreases by

10% pensioners share increases by 15%.

- high-growth scenarios: youth share increases by 0pp ,working age decreases by

3%, pensioners share increases by 3%.

Economic change:

• Fast-change scenarios: share of service employment increases by 20 pp over

2000-2030; share of teleworking equal to 70%. In this scenarios, the service

share of employment in some cities will reach 100%. This change

corresponds to the expectations if past trends continue. Mobility rate

increases.

• Slow-change scenarios: share of service employment increases by 5 pp over

2000-2030. Share of teleworking equal to 15%. Mobility rate decreases.

Technological change is expressed in terms of: 1. passenger car occupancy rate;

2. modal share: public and private transport;

3. Information Technology in traffic control & management;

4. penetration rates of new vehicle propulsion technologies

Land use change is represented by the concepts of urban sprawl and mixed land

use. Urban sprawl happens when population growth does not increase the population


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density of high-density areas, but promotes densification of less-developed areas

and expansion at the urban fringe. Mixed land use locates land uses with

complementary functions (e.g., housing, shopping, offices, restaurants and movie

theatres) close together, minimising transportation demand for at least a part of

urban amenities.

Within the broad set of possible scenarios a narrower sub-set of 4 scenarios has

been identified. The selected four scenarios were extracted and considered as the

most relevant and meaningful ones.

The identification of the four “common scenarios” was based on the expected

correlation between the four main issues. The scenarios are as follows (in brackets

the set of values attributed to each main issue – in order demographic, economic,

technological and land use):

Dynamic, rich and virtuous city (H, H, H, H)

a young and growing city moving fast to high-tech services jobs, which cares about

the environment and adopts clean technologies and careful planning

Dynamic, rich and vicious city (H, H, L, L)

a young and growing city moving fast to high-tech services jobs, which, however,

saves on clean technologies and grows chaotically into the countryside around

Virtuous pensioner city (L, L, H, H)

a city becoming a city of pensioners, it does not grow, it does not change its

economic structures, yet, it cares about the environment and adopts clean

technologies and careful planning

Vicious pensioner city (L, L, L, L)

a city becoming a city of pensioners, it does not grow, it does not change its

economic structures. It does not adopt clean technologies or careful planning.


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TECHNO-ECONOMIC MODELLING

the MARKAL-Lite energy / environment model that has been designed for modelling

urban energy systems. Based on MARKAL (Abilock et al, 1980; Franier and Haurie,

1996) MARKAL-Lite is a bottom-up model that gives a representation of the energy

and technology choices that could best deliver the needed energy services in a city.

Within the SUTRA framework, this provides two important sets of parameters to the

model cascade: technological choice (fleet composition) for the transportation

system, and emissions related to the overall energy services going beyond the

transportation sector.

Bottom-up energy models like MARKAL have been developed mostly for analyzing

energy/environment policies at a national or regional levels. Implementations of

MARKAL models at the urban scale have been initiated in Sweden and more recently

in Switzerland where an integer and stochastic programming version of MARKAL has

been developed to analyze the energy/environment policies in the canton of Geneva.

In national MARKAL models, the environmental problems taken into account have

been mostly the emissions of atmospheric pollutants like N02, SO2 and the emission

of greenhouse gases, in particular CO2. When dealing with urban air quality, a major

concern is the control of ozone O3 episodes. Ozone is a secondary pollutant

resulting from photo-chemical reactions that involve a large variety of primary

pollutant, like NOx, VOCs and many others. Ozone concentrations can be modelled

through large-scale Eulerian models describing the transport, diffusion and chemical

processes that take place under different weather conditions. The work reported here

is part of a project that aims at coupling a bottom-up energy model with an ozone

concentration model. In developing this coupling we have to deal with an energy

system which is often a small subset of the national system; however this reduced

size energy system has to be represented in details. Some energy technologies

correspond to indivisible projects, for example the district heat systems; furthermore

the location of primary pollutant emissions is of paramount importance for the control

of ozone episodes. Therefore we have to envision a model based on mixed-integer

programming, with technologies indexed over time and space.


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MARKAL-Lite is an adaptation of MARKAL-Geneva, implemented in a versatile

modeling language (AMPL ) facilitating the design of urban energy models. The

version of MARKAL-Lite used in SUTRA has the following features:

• The model focus is on power generation, residential or commercial space heating

and transport technologies.

• The model can be easily linked with an industry process model that could

describe the dominant local industry (e.g., in Gdansk).

• The model can be easily linked with a traffic equilibrium model.

• The model deals explicitly with the indivisibility issue in project selection, by

allowing (0,1) decision variables and logical choice constraints.

• The model may deal explicitly with the major uncertainties through a stochastic

programming formulation.

• The model is designed to be integrated with a geographical information system

(GIS) that can provide the basic data and display the scenarios simulated.

TRANSPORTATION MODELLING

The Traffic Assignment software package VISUM has been enhanced with modules

to allow the modelling of particular scenarios of sustainable transportation, namely

Park+Ride, High Occupancy Vehicles, and Road User Charging. As a number of

decision support indicators can estimated from dedicated models (for emissions, air

quality, public health, economic and energy system analysis) relying on the output of

the transport model their relevant input data have to be made available.

The data structures of the transport models have been adapted to enable data

exchange in both directions between the transport model and the emission model

TREM as well as post-processing of emission data through the transport model.


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Table 1: O/D matrices

City Population Area N of nodes N of links

Geneva 413,585 282 936 2,900

Genoa 635,201 241 360 888

Lisbon 2,682,676 2,793 1,124 2,940

Tel Aviv 2,611,500 1,447 3,144 11,850

Thessaloniki 894,435 1,100 1,386 2,034

The transportation scenarios were based on existing origin-destination (O/D)

matrices (Table 1.) for peak hours and/or daily average traffic. For the baseline

scenarios, the model was calibrated against available traffic counts where available

(Figure 1,2). The scenarios of change were constructed by scaling the base matrix to

represent demographic and land use change, modifying transportation demand and

average trip length as well as modal split accordingly. For the emission model,

VISUM produces vehicle frequencies and average speeds per link, as well as the

cold-start fraction of vehicles (Figure 3).

In addition to the emission data, estimates of crowding (hours spent yearly in an

overcrowded public transport) and traffic jams (hours spent in traffic jams) could be

obtained from the model results, as input for the economic analysis.

362.8391.4Provincial network1734.71546.8Municipal network

Simulation resultsMeasured dataAverage Values (veih/h)

362.8391.4Provincial network1734.71546.8Municipal network

Simulation resultsMeasured dataAverage Values (veih/h)

Correlation Coefficient: 0.82

Figure 1: Private traffic assignment, Genoa: VISUM model calibration results.


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TPB assignment statistics

Figure 2: VISUM model results: Genoa: public transport.

Figure 3: VISUM results, City of Thessaloniki: vehicles per link, per day.


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EMISSION MODEL

An emission model for road traffic (TREM) has been developed within SUTRA

project. The main purpose of this model is to estimate the quantity of pollutants

released to the atmosphere from vehicles. The MEET/COST methodology is adapted

to the project requirements taking in consideration input data availability from one

side, and user requirements on the other side. Estimation of traffic emission is based

on the results of the transportation model and local data sets including detailed

characteristic of fleet composition and driving conditions. Emission factors based on

average speed were considered as the best approach. Also, different technology

(engine type, model year) and engine capacity are distinguished in TREM model to

derive emission factors. The following pollutants are covered: CO, NOx, SO2, VOC,

CO2 and particulate matter.

In general terms, the estimation of transport-related emissions can be based on the

equation E = e * a, were E is the amount of emission, e is the emission factor per

unit of activity, and a is the amount of transport activity. This equation has to be

applied for each vehicle category, since the emission factors and the activity are

different.

The emission factor, e, is usually expressed in g.km-1 and primarily related to driving

conditions and vehicle type. The activity, a, is a product of the number of vehicles for

each of the categories and the distance travelled. The methodology used to calculate

emission factors is based on MEET/COST approach. Furthermore, to compile a

consistent model the following conditions were taken into consideration:

- Input data availability, and

- Intended use of modelling results.

Thus, emission factors based on average speed were considered as the best

approach due to the absence of more detail information relating to vehicle dynamic.

Different technology (engine type, model year) and engine capacity are distinguished

in TREM model to derive emission factors.

In this model version the calculation algorithm for the following pollutants emitted by

road traffic is implemented:

- Carbon monoxide (CO);


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- Nitrogen Oxides (NOx), given as NO2 equivalent;

- Volatile Organic Compounds (VOC), including methane,

- Carbon Dioxide (CO2);

- Sulphur Dioxide (SO2);

- Particulate matter (PM10).

Road traffic emission calculation is accessed as the sum of hot emission (i.e. under

stabilised engine operation), cold start (water temperature is below 70ºC) and

evaporative (from fuel evaporation) emissions.

To an accurate estimation of air pollutant emissions from road transport splitting of

vehicles by categories is required. In the current model, the following nine vehicle

categories are distinguished:

- Gasoline, Diesel and LPG (Liquefied petroleum gas) Passenger Cars;

- Gasoline and Diesel Light Duty Vehicles;

- Diesel Heavy Duty Vehicles;

- Urban Busses and Coaches;

- Motorcycles;

- New Technologies.

Additional attributes that can be considered for emission calculations include model

year, engine type, emission standards and engine capacity. In order to identify the

level of emission control, the years of introduction of the various amendments to EU

legislation is linked with the model years of vehicles within the fleet.

Transport activity is one of the principal input data to estimate road traffic emissions.

In SUTRA, it is estimated with the transportation model described above, distributed

over the entire transportation network, with detailed data for each segment. Transport


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activity is defined as: a = n * l, where: n is the number of vehicles for each of the

categories and l is the average distance travelled by the average vehicle of the

category over the time unit,

HOT SPOTS: STREET-CANYON MODELLING

Based on the emission estimated by TREM, a series of air quality (dispersion)

models translate these emissions for a range of meteorological conditions into

ambient environmental concentrations. A specific case for traffic generated pollution

is the street canyon, where the spatial confinement limits the dilution close to the

source.

The VADIS model system, developed at the University of Aveiro (Borrego et al,

,2002; Borrego et al, 2000), was used to represent hot spots in the built up city

centres. VADIS is an integrated system, coupling a boundary layer flow module with

a Lagrangian dispersion module, developed and adapted in the context of SUTRA

Project to the simulation of urban air pollution in city centres. This model is prepared

to deal with unfavourable dispersion conditions, as thermal stability and low wind

speeds. These are typical conditions in the southern European countries that cannot

therefore be neglected in the study of pollutants dispersion. In this model the flow

field calculation is based on the resolution of the Reynolds averaged Navier-Stokes

equations, using a k-ε turbulence model. The concentration field estimation uses a

Lagrangian approach. VADIS has the capability to support multi-obstacle and multi-

source description as well as time varying flow fields and time varying emissions.

In the scope of SUTRA Project, VADIS was developed and adapted to the calculation

of urban air pollution due to traffic road emissions and to the estimation of local hot-

spot values, and it has being applied to specific areas of different city centres. This

study presents the methodology of application of this near-field model to a specific

area of Lisbon city centre, during a typical summer day. The CO concentration values

calculated by VADIS during the 18 hours simulation period showed a good


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comparison with air quality measured data made available in the city case studies

(Figure 4).

Figure 4: Wind and CO dispersion fields simulated with VADIS for 3 pm. of July 8, downtown Lisbon

(□, location of the air quality station) (University of Aveiro 2003).

REGIONAL PHOTOCHEMICAL MODELLING

At the other end of the spatial and temporal scale for air quality assessment, based

on the emission estimates from TREM plus additional data for industrial, domestic,

and biogenic sources derived from the scenario assumptions and the MARKAL

model runs where available, the OFIS model was used. OFIS, the Ozone Fine

Structure model was derived from well-tested full 3D models, and hence it retains all

elements necessary to achieve a realistic statistical evaluation of urban scale ozone

levels. The conceptual basis of OFIS is a 2D approach:

Background boundary layer concentrations are calculated with a

three-layer box model representing the local-to-regional

conditions in the surroundings of the city considered. This model

0 50 100 150 200 250 300 350 400 450

West/East (m)

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200

250

300

350

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Sou

th/N

orth

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100

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uses at input non-urban emission rates, meteorological data and

regional scale model results for pollutant concentrations (e.g.

EMEP (Cooperative Programme for Monitoring and Evaluation of

Long Range Air Pollutants in Europe) model results (Simpson,

1993, 1995).

Pollutant transport and transformation downwind of the city

(along the prevailing wind direction) is calculated with a three-

layer multibox model representing a substantially refined version

of MARS-1D (Moussiopoulos, 1990).

The distinction of three individual layers of time depending thickness allows

adequately describing the dynamics of the atmospheric boundary layer. At the

same time, vertical transport is taken properly into account by considering the

exchange between adjacent layers.

For prescribing the thickness of the three layers, a 1D version of MEMO (Non-

hydrostatic mesoscale model) is utilised: The vertical profiles of temperature,

mean wind speed and turbulent exchange coefficient as well as the mixing

height are calculated both for the city surroundings and the urban plume

assuming Monin-Obukhov similarity at the lower boundary. For the scenario

analysis, the modified emission scenarios were translated (under the

assumption of constant boundary conditions) into estimates of exceedances of

ozone standards and AOT, average over threshold concentrations (Figure 5).


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Figure 5: Number of days with 8-hours running average ozone concentration exceeding 120 g/m³ (IND

120), calculated by the OFIS model, for a 150x150 km² area surrounding Genoa and wind rose of

prevailing wind during the summer semester of 1999 (final initial reference scenario for Genoa;

University of Thessaloniki 2003).

CITY LEVEL MODELLING

To model the entire city but at a sufficiently high resolution to account the effects of

traffic (line sources) and their steep local gradients, the traffic impact simulation

module from AirWare (Fedra and Haurie 1999; Fedra 2000b, 2002;

http://www.ess.co.at/AIRWARE/) was used.

The model is based on the well known USEPA (United States Environmental

Protection Agency) ISC-3 model, used with a high-resolution (10m ) computational

kernel of 2.5 km size, that is scaled and convoluted along the entire traffic network

and its emission data generated by TREM. The model uses a mixing zone approach

to account for the near field concentration close to the street. While it doe not

consider the building obstacle simulated by VADIS it can cover the entire city and

transportation networks of several thousand segments fast enough for truly


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interactive use, both for individual meteorological scenarios as well as for seasonal

and annual average situations, solved in the frequency domain.

These features made it possible to implement the model on-line, for interactive use

through the Internet (Figures 6,7,8).

Figure 6: AirWare model results: Genoa City center, base-line scenario.


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Figure 7: AirWare model results: Genoa, city level, scenario 2: dynamic, rich and vicious city.

Figure 8: AirWare population exposure estimate for Genova, scenario 2.


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HEALTH AND ACCIDENTS

Public health impacts of urban transportation include effects directly and indirectly

due to vehicular emissions; other indirect effects of the transportation system, e.g.,

related to stress such as from crowding and traffic jams; and traffic accidents.

Public-health effects can reach dramatic proportions, with several thousand

premature death attributed to traffic generated air pollution in European cities

(Kuenzli et al, 2000): 6% of mortality or a total of 40,000 cases are attributed in

France, Switzerland and Austria only, with an additional 25,000 new cases of

bronchitis annually, and up to 250,000 bronchitis episodes in children, and about 0.5

million bronchitis attacks, resulting in a total of 16,000,000 person-days of restricted

activities.

Traffic accidents are a growing problem. Urban areas have a higher percentage of

accidents (75 %) compared to rural areas.

Using the data generated by the SUTRA case study model cascade and estimating

exposure, estimates for fatalities and sick-hours were obtained with an expert system

based on fuzzy logic to account for the inherent uncertainty of epidemiological data

and estimates. The estimates for morbidity (expressed in sick days per year and

capita) varied only slightly between scenarios. Fatality estimates varied by a factor of

two between the most optimistic (CS3) and most pessimistic (CS1) scenarios.

However, it seems appropriate to express these results in terms of an index

measuring relative change (compared to the baseline scenario) rather than as highly

uncertain absolute numbers.

The objective of the health model is to build system for forecasts the level of mortality

and morbidity caused by pollution by transport. The proposed model relies on

knowledge-based solutions and the theories of fuzzy sets. A detailed design of fuzzy

models takes advantage of the experience of the emitted by the different kinds of

vehicles (toxic gases and particles) may cause several human diseases. Data from

the project participants have been utilized in tuning of the fuzzy model using

knowledge-based rules and membership functions. They include:


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• CO total emission [kg/h], methane, affecting: Angina - Affects pregnancies,

breathing and/or cardiac problems;

• NOx (Nitrogen oxides), Max. Conc. NOx [ug/h], average [ug/h], nonzero

average [ug/h], affecting: Bronchitis – Pneumonia, neoplastic diseases;

• Mortality caused by pollution due to transport [death in a year]

• Morbidity caused by pollution due to transport [working days lost in a year]

The membership function design has been based on clustering methods, where the

location of cluster centre of gravity is identified to adjust the membership functions

accordingly. starting point for a modelling procedure has been a mortality and

morbidity evaluation of the available data from the city case studies. As a first step,

appropriate formal models have been developed, then hierarchical and structural

models of the relation between the data describing pollution and mortality and

morbidity have been constructed. Next, the analytical (dynamic) integrated model has

been developed. In order to build a useful model, such elements of the fuzzy control

theory as the fuzzy rules of the Mamdani type have been utilized, which results in a

vector-matrix model with a fuzzy sub-system. The completeness and consistency of

the fuzzy-model rules have been verified. Fuzzy-dynamic state variables have been

introduced to describe the set of scenarios.

THE CITY CASE STUDIES

With only limited data available from Buenos Aires, the methodology was applied to

six cities (Gdansk, Geneva, Genoa, Lisbon, Tel Aviv, Thessaloniki; Table 2).

For each city, the scenarios simulated included the baseline scenario used for

calibration, the four common scenarios (see above) and a few city-specific scenarios

that represented local structural variations such as new bridges, tunnels, etc., i.e.,

changes to the transportation systems structure.


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Table 2: Case study city summaries

City Population Area Private cars Public transportation

Buenos Aires 12,000,000 800 2,000,000 50.000 taxis, 295 bus

lines, 5 subway lines, 6

metropolitan train lines

Gdansk 1,000,000 263 126,000 2.300 taxis, 250 buses,

250 street cars

Geneva 400,000 282 196.000 342 km of public

transport network

Genoa 650,000 240 cars: 431,000

trucks: 35,000

M.bikes: 57,000

870 buses, 1 metro line,

1 metropolitan train line

Lisbon 2,000,000 3,000 1,000,000 3000 taxis, 815 Bus, 60

trams, 235 subway

wagons

Tel Aviv 1,140,000 170 cars: 637,600

M.bikes: 50,100:

6,369 taxis, 7,597

buses, 6,254 minibuses

Thessaloniki 1,000,000 500,000 500 buses

MULTI-CRITERIA DSS (Decision Support System)

Each scenario represents a possible future for a city. This begs the question which of

these possible futures are the most desirable ones, and, even more important: how

to define desirable in the first place ? However, given the high dimensionality of the

problem: even in terms of the highly aggregate indicators, there are about 80

dimensions. One possible approach that was tested in SUTRA is a discrete multi-

criteria optimisation approach (Fedra and Haurie, 1999, Zhao aet al., 1985,


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Majchrazak, 1998). Given the small number of scenarios, the main purpose was to

analyse the sensitivity of any solution to the choice of criteria to be considered in the

optimisation.

EUROPEAN CITY BENCHMARKING

The comparative analysis of the scenarios, has been accompanied by a

benchmarking exercise where the promising scenarios are compared against a larger

set of cities to find patterns and trends from which policy implications for the

implementation of the optimal strategies can be identified. One of the main steps

towards the cross-comparison and benchmarking exercise, was the compilation of a

baseline data base obtained by merging the relevant collected data. The main data

sources, in addition to the projects own data on the case study cities, were:

• UITP (International Union (association) of Public Transport) Millennium Cities

Database for Sustainable Transport has compiled a database involving 100

cities worldwide, known as the “Millennium Cities Database”, in collaboration

with Professors Jeff Kenworthy and Felix Laube at Murdoch University (Vivier,

2001).

• Auto Oil Programme, that has been set up in order “to provide the technical

input to the Commission’s work on future vehicle and on fuel quality standards

and related measures” (SENCO, 1999).

• Citizens' Network Benchmarking Initiative (EC 1996)

• The Urban Audit is an initiative managed by the DG (Directorate General)

Regional Policy and EUROSTAT (Statistical Office of the European

Communities) , and its purpose is assessing the quality of life in European

cities.

• EMTA (Association of European Metropolitan Transport Authorities)

Barometer Of Public Transport In The European Metropolitan Areas

• Ecosistema Urbano 2003 di LEGAMBIENTE

The resulting data set combines 250 cities and a superset of 85 indicators. The large

scatter of the data makes it difficult to identify clear pattern. One of the most

surprising finding here was the poor quality and rather sparse data coverage: the


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matrix of about 200 cities and about 40 indicators is sparse. Nevertheless, the

combined data set is available on-line at http://www.ess.co.at/SUTRA/ (Figures 9,10).

Figure 9: Relationship of poulation density to average speed of transportation for private (car) and

public transport (buses), including stops across the 250 benchmarking cities subject to data

availability.

Figure 10: Web access to the city benchmarking data.


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DISCUSSION

The SUTRA project has generated two types of main results: methodological and

substantive. On the substantive level, the conclusion are complex, as is to be

expected in this complex domain. No single measure included in the scenario

analysis alone can make a major impact within the ranges of plausible rates of

change in the driving forces. Clearly, a well balanced set of integrated measure is

necessary to maintain and improve sustainable urban transportation. This set of

measures must be defined for each city considering its structural, socio-economic,

and technological constraints to find the best, cost-effective solution. At the same

time, the scenarios demonstrate that within the plausible limits of possible change,

most state and performance indicators vary by a factor of about 2. Thus, within, the

range of possible policy instruments there is room for considerable improvement, but

also the danger of dramatic deterioration.

From a methodological point of view, we consider the linkage of a set of very different

simulation tools within the common indicator framework and subsequent multi-criteria

evaluation a success. Despite their different structures, coverage, and resolution, the

cascading of the models worked well, leading to a complete and uninterrupted chain

of inference and data flow with explicit assumptions and clear linkages and interfaces

open to detailed analysis. With this conceptual meta model in operation, and

considering the potential of modern distributed grid computing, one can imagine that

the model cascade can be embedded in an optimisation approach, that can explore a

large number of scenarios e.g., in a evolutionary programming approach. Thus, the

complex and very large scale problem of urban planning and development can be

approached with a consistent formal approach, that can combine the power of the

optimisation paradigm with the details and resolution of simulation model.


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Acknowledgements

The research described in the paper was funded, in part, by the Commission of the

European Communities, Director-General for Research DG, under the Fifth

Framework Programme and the City of Tomorrow key action in the project SUTRA

(EVK4-CT-1999-00013).

The author acknowledges the contributions of the numerous project partners (a total

of twelve institutions from 11 countries were collaborating in SUTRA, see

http://www.ess.co.at/SUTRA/partners.html). The material presented here is largely

based on the set of final Deliverables from the SUTRA project. The full reports are

also available on-line for download (http://www.ess.co.at/SUTRA/DELIVERABLES/).

Authors of the individual respective reports are: Pietro Caratti, Dino Pinelli, Valentina

Tarzia: indicators and scenarios; J.Janko: traffic modelling; University of Aveiro:

emission and street canyon modelling; C..Naneris, K. Karatzas and

N.Moussiopoulos: ozone modelling; University of Geneva: energy modelling;

C..Orlowski: health modelling; S.Mink and J.Rose: scenario comparison;

U.Gasparino and O. Jabary Salamanca: benchmarking.

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