Project On-line Deliverables: D01.0
Requirements and Constraints Report


The Requirements and Constraints Report aims to develop a comprehensive overview of the information requirements for the project, the data availability versus information requirements, and the institutional and technical constraints to be expected in the practical implementation of the project work plan.

The report compiles the technical, institutional, and data requirements and associated constraints for the project. Finally, the report formulaties the basic conceptual and technical guidelines and blueprint for the subsequent work packages.

The report develops and describes the basic framework for the analysis, and summarizes this in a checklists and questionnaire to be used by all partners for the initial information compilation stage.

On the basis of checklist and questionnaire, each partner compiles the necessary information locally, in cooperation with the end users from the project user group where feasible, and fill in the questionnaire. All partners responsible for methodological components have to define the data requirements for the respective tools the report.

The individual questionnaires are compiled by ESS, analysed, and summarised into the final Requirements and Constraints Report presented here.

Table of Contents

1. Introduction

The primary objective of SUTRA is to develop a consistent and comprehensive approach and planning methodology for the analysis of urban transportation problems, that helps to design strategies for sustainable cities. 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.

The cities collaborating and involved in SUTRA (Athens*, Berlin*, Buenos Aires, Gdansk, Genoa, Geneva, Lisbon, Milano*, Tel Aviv, Thessaloniki, Vienna*) differ widely in terms of culture, environmental conditions, size, economic structure, social composition and demography. Despite these differences they all face common challenges in their transportation system such as those relating to air quality, noise, traffic congestion, but also related issues such as economic competitiveness, mobility, employment, maintaining their deteriorating infrastructure and built environment while reducing social exclusion and promoting sustainable development.

From a technical perspective, the project aims to develop and apply:

  • An indicator based approach compatible with Agenda 21 (UNCED 1992) and the indicators for urban sustainability used in the Dobris Report (Stanner and Bordeau, 1995) and later EEA publications, for a baseline analysis, ranking and benchmarking (within the participating cities and across all of Europe) that will ultimately support a discrete multi-criteria selection mechanism.

  • Scenario analysis that uses:

    • Traffic equilibrium modeling to evaluate alternative transportation policies, including multi-modal systems, technological development, socio-economic development, and spatial and structural urban development (landuse scenarios) in general;

    • Air quality modeling to translate transportation scenarios and their resultant emissions into ambient air quality estimates and population exposure;

    • Economic analysis and energy systems analysis and modeling using well established modeling approaches such as MARKAL, to identify and evaluate cost effective transportation scenarios, consistent with the larger economic and technological framework.

    • The concepts of environmental impact assessment for the comprehensive evaluation of alternative transportation scenarios, using on a rule-based checklist approach to cover all other environmental effects beyond air pollution, such as noise, waste including the complete vehicles life cycle, space and resource requirements for the transportation infrastructure and its maintenance, and the effects of accidents.

    The scenarios, defined for each of the cities, will consider the current base line and a do-nothing alternative (naive projection of current trends) and a set of probable development strategies in terms of demographic, socio-economic, spatial and structural (landuse), and technological developments over the next decade and beyond (30 year horizon).

  • Comparative multi-criteria assessment. Based on the comparative evaluation of the scenarios, again using the sustainable cities indicators, a multi-criteria decision support mechanism will be used to identify preferred strategies and policies.

  • Citizen and stakeholder participation in urban decision making processes, but also the underlying awareness building end educational aspects will be supported by making the project results and findings available as a public information system on the Internet.

2. The analytical approach

SUTRA is designed to develop a consistent and comprehensive approach and planning methodology for the analysis of urban transportation problems, that helps to design strategies for sustainable cities. The approach of SUTRA is based on the innovative combination of a number of key concepts and methods into a very comprehensive analytical framework.

This framework is made manageable through a layered implementation, where within an overall comprehensive structure different aspects are investigated at different levels of detail, but aggregated to the appropriate level of integration for overall assessment and comparison.

This logical structure includes:

  • Transportation modeling, where the classical equilibrium approach is extended to address multi-modal transportation and innovative forms of urban transportation, but at the same time scenarios of alternative transportation demand, provides the primary representation of the transportation system;

  • The description of the transportation sector is related to an overall energy system optimisation model, that addresses other sources of emissions such as industry and domestic energy consumption within a consistent framework of assumptions;

  • Both the transportation and energy systems analysis provide input, through appropriate emission models, to a range of air quality simulation models that translate the emission into ambient air quality indicators.

  • In addition to these immediate environmental effects, an economic assessment provides additional, and policy-relevant, indicators;

  • To obtain a more comprehensive view of the environmental impacts of transportation, the air quality modeling is augmented by a rule-based environmental impact assessment methodology (Fedra et al., 1991) that addresses other impacts such as noise, public health issues, land use and non-renewable resource issues, the life cycle of vehicles and transportation infrastructure, and accidents;

  • The detailed assessment of the different tools is summarised into a number of high-level indicators of sustainable cities;

  • These indicators are evaluated:

    • with a benchmarking approach across the set of test cases and against other cities, world wide;

    • with a discrete multi-criteria optimisation approach to identify the most promising scenarios and strategies of sustainable urban transportation.

The analytical approach combines a number of specific features:

  • The conceptual framework is based on a comprehensive approach to sustainable urban transportation that uses a multi-criteria evaluation scheme and employs high-level policy relevant indicators as the organising conceptual framework;

  • SUTRA employs a unique mixture of quantitative and qualitative yet formal analysis methods, combining numerical modeling with qualitative reasoning through an expert system and high-level indicators of sustainability;

  • To make complete coverage of environmental impacts possible, the approach foresees a multi-layered analysis that combines air quality, energy analysis, economic assessment, and general environmental and strategic impact assessment based on checklists;

  • The use of indicators of sustainable development not only guarantees a rigorous conceptual framework for the analysis of each city, but also provides a powerful means for cross-comparison and benchmarking over a larger set of urban situations, and a powerful tool for communication;

  • The direct integration of transportation equilibrium modeling and environmental impact assessment (using a combination of advanced emission and air quality modeling) within the framework of the overall energy analysis guarantees consistent scenarios of urban development;

  • The use of a rule-based environmental impact assessment expert system allows to cover a broad and comprehensive range of impacts including those that are extremely difficult to approach in a quantitative way (such as social issues and questions of behaviour and perception) through symbolic reasoning.

  • The use of a multi-criteria evaluation methodology based on reference point optimisation makes it possible to rigorously and systematically compare a large set of indicators over a large set of cities and scenarios efficiently and effectively to derive policy relevant strategies that can directly be used in the planning and decision making process.

  • The involvement of a large set of cities of quite different size and locations with a considerable geographical spread provides a rich base for comparative analysis, which is further extended by adding a large set of other cities for the benchmarking exercises.

3. Institutional framework

Since the intended end-user of the SUTRA results are cities, the institutional framework in these cities, i.e., the structure of their city government and administration, but also other public or private institutions including NGOs must be analysed for an effective dissemination and eventual implementation of project results.

For the seven cities involved in SUTRA, this information is summarised below:

City framework
Buenos Aires available available
Gdansk   available
Geneva   available
Genova availble available
Lisbon available available
Tel Aviv available  
Thessaloniki available  

The following examples show schematic representations of the institutional structure for Municipality of Genoa and Libon, respectively.

The managers are responsible both for an efficient activity and for management of budget of each office, in proportion with their level of competences.

4. Data requirements

Like with any formal method, the success of SUTRA depends critically on the availability, and quality, of appropriate data and information.

The requirements and constraints analysis compiles the data requirements implied by the different analysis methods and models, and compares them with reports of data availability by the partners responsible for the city case studies. This will lead to one or more iterations to ensure that data requirements and data availability are in line with each other.

Please note that the actual data compilation, standardisation and pre-processing for use with the individual tools is the objective of Work Package 2, Data Compilation.

4.1 General data requirements

The second part of the work package WP01 addresses the general data requirements: we need to compile an overview of the data sources to be used for WP 02.

The primary objective is to prepare and structure the actual data compilation in WP 02 by identifying the possible sources for each data set, establish the necessary institutional contact where necessary, and document the data sources.

The general data requirements (for the model specific details, see below) include basic information on each city, and generic data such as emission coefficients or similar generic technical data from other institutions and the general literature.

The basic data requirements include:

City Specific Data:

  1. Maps and GIS data (topographic maps, land use, digitial elevation model) covering an area of approx. 100 by 100 km around each City (this domain will be needed for the meso-scale metoeorological models, as well as for the inclusion of commuter traffic for larger cities)

    Appropriate scales are between 1:250,000 to 1: 100,000 for the overall domain, and 1:25,000 to 1:10,000 for the detailed city area.
    Please verify that data are available in electronic formats (preferably in Arc/Info export format).

  2. Meteorological data: at least one, preferably several stations with at least one complete year of hourly data (or as close to that as at all possible);
    Data include wind speed and direction (anemometric and geostrophic where available), air temperture, cloud cover, precipitation, stability class (can be computed from other data) and mixing height.

  3. Census data (population structure (age, sex, income distribution) and spatial distribution.

  4. Basic socio-economic statistics, spatially diasaggregate where possible.

  5. Any major development plans for the city, public transportations, road works, major city and/or industrial development, land use change, etc.,

  6. General development policy documents for the cities, and in particular pland for transportation management (access restrictions, fees, etc.).

General Data

  1. Inidicators (definitions, comparison data from other cities)
  2. Emission coefficients (stationary traffic and all other economic activities)
  3. Cost factors, epidemiological data, life-cycle data, energy consumption, etc.

4.2 Model data requirements

Within WP 01, the data availability for each of the model components listed below was analysed; actual compilation of the data is the task of WP 02, which however, runs in parallel.

The individual models and assessment tools include:

4.2.1Energy modelingUGE
4.2.2Multi-modal transportation modelPTV
4.2.3Meso-scale photochemical modelAUT
4.2.4Emission model, street canyon modelingUAV
4.2.5Rule-based impact assessmentESS
4.2.6Spatial Impact AssessmentESS
4.2.7Epidemiology, accidentsUBG

4.2.1 Energy modeling

In order to run a MARKAL model, several types of data are needed. They are typically classified as:

  • Imported energy prices
  • Demand data
  • Residual Capacities
  • Techno-economic data
  • Input/output coefficients, availability and lifetime of technologies
  • Pollutants emissions associated with technologies

Imported energy prices:
The energy system dynamics is strongly influenced by the evolution of world prices of imported energy carriers (oil, gas, coal etc.). It is important to feed the model with one (or several) scenario(s) of evolution of imported energy prices over the planning horizon. There is indeed a high level of uncertainty, as energy prices fluctuate widely. Rather than estimating a price "averaged" on several scenarios, a far better approach consists in identifying several likely scenarios, with different price evolutions. One can then either perform a distinct MARKAL run for each scenario, or even better, combine all scenarios in a stochastic programming approach.

Demand data:  
Useful demands of energy services drive the whole MARKAL model. Thus these must be calculated with extreme attention.

    Useful Demands and Installed Capacities:   To satisfy these demands, MARKAL has to "install" capacities in various technologies. For Conversion and Process Technologies, the capacities are generally described in GW and Oil- equivalent tons respectively. These capacities are then transformed into correspondent energy flows (in TJ or PJ)

    Capacity units for Final Energy Use technologies:   It is common, in the case of Demand device technologies, to use the same unit for installed capacities and useful demands. The relevant unit for transportation demands is typically the number of kilometers per day or year, or the number of passenger-kilometers or ton-kilometers per day or year. For heating and electrical devices, the demands are generally expressed in energy units (PJ per year)

Residual capacities:
Residual capacity for each technology refers to the amount of previously installed capacity that is available prior to the beginning of the planning period. This data should be collected in the most accurate way, as it does not only depict the current energy situation, but will also have a major impact on investments at the earliest stages of the planning period. Residual capacities are expressed in the same units as demands.

Techno-economic data:

  • Investment costs. Each technology implies an investment cost, which, in the case of transportation technologies, can be roughly estimated by the purchase price of a vehicle divided by the mileage it will "deliver" during its lifetime.
  • Fixed operation and maintenance costs. Each vehicle represents a given operational and maintenance cost, proportional to mileage. In general, its order of magnitude is about 10 times less than investment cost.
  • Variable operation and maintenance cost. This cost applies only to transformation technologies (conversion and process) and is proportional to the amount of energy produced, not the installed capacity.

Input/output and technical data:

    Input/output coefficients. They describe the necessary input for each unit of output produced. They are generally expressed in PJ/PJ , except for transport where they are in PJ/10^6 km.
  • Date of availability. This applies to new technologies, such as fuel cell cars for example, only to be available in the future.
  • Lifetime. It is generally estimated to be 10 years for vehicles, unless otherwise stated, 30-40 year for transformation technologies,…

Pollutants and emissions:

    Type. (e.g. NOx, VOC, SO2, CO, PM10…)
  • Emission coefficients ( tons of pollutant emitted by unit of energy produced for conversion and process technologies, in PJ or Oil-equivalent tons of pollutant emitted by unit of energy consumed, km or PJ)
  • Bounds (Limits on tons emitted per period and pollutant).

4.2.2 Multi-modal transportation model

SUTRA will use the VISUM transportation moldeing system from ptv. VISUM is a versatile software product for com- puter-aided transport planning. It combines all relevant aspects of private and public transport planning in one comprehensive transportation model.

Transportation Model Data Requirements

The transportation model VISUM determines the impacts of existing or planned transport supply which can encompass both the private transport network and the public transport line network (including timetables). The transport planner is supported in developing a supply design, in analysing the supply, and in evaluating network variants. A transport model in VISUM consists of supply data and demand data. The following paragraphs can only give an overview of these data. A complete description can be found in the software documentation.

Supply data

Transport supply data are represented in a network model. The integrated network model distinguishes between "private transport" and "public transport" mode. By combining different means of transportation and modes, the planner can define different transport systems. Private transport systems depend on permissible speed and link capacity. Public transport systems are bound to a timetable. Basically the network model includes the following network objects which can be modified interactively:

  • Nodes: private transport intersections or public transport stops.
  • Zones: origin and destination of travel demand.
  • Links: speeds and capacities for private transport, travel times for public transport.
  • Turning relations: turning penalties for private transport, points and turning places for public transport.
  • Lines: line name, line variant, line route and timetable. Furthermore the network may contain,
  • operational information on public transport vehicles and operators to be managed in the network model,
  • census points on links for evaluation of traffic counts (by direction), and
  • user-defined areas (representing e.g. a district or a county), for which private transport and public transport indicators can be determined precisely based on a polygon describing the area΄s boundaries.

Demand data

Transport demand data have to be generated separately from the traffic assignment model. The most important method is the application of a traffic demand model as for instance VISEM to produce the required data. Demand data must be made available in a matrix form describing travel demand

  • For every relation between zones in the network model
  • For the considered time period, e.g. a complete day or a peak hour
  • For all considered demand segments, i.e. vehicle trips in private transport, and passenger trips in public transport


The ptv vision demo CD includes a comprehensive example for the application of the transportation model. It is derived from a transport study for the city of Halle, Germany (app. 260 000 inhabitants). The example includes demand data and supply data, so that every possible analysis can be performed.

Document Authors:   K.Noekel, J.Janko, PTV

4.2.3 Meso-scale photochemical model

For photochemical modeling, SUTRA will use the OFIS model.

Input data for OFIS is needed for :

  • For background boundary layer concentrations, regional scale model results for meteorological quantities and pollutant concentrations. Such data is obtained from emission databases and models like EMEP (European Monitoring and Evaluation Programme) or LOTOS (cost ~7500 Euro/year), OR from appropriate measuring stations and following the necessary assumptions.
  • Emissions and meteorology for an area 150x150 km2 around the city (see laso OFIS power point presentation).

For the emission data in the area of 150x150 km2 around the city, firstly all the emissions must be provided in distinct categories depending on the type of their sources (industrial, road transport, natural etc.), the type of the pollutants (NOx, SO2 etc.), their distance from the cities (urban, suburban, rural) and the height of their sources (surface, elevated). Furthermore, the diurnal (monthly, daily, hourly) profile of the emissions must also be provided. Landuse information for every city is obtained by AUT from GLOBAL LAND COVER CHARACTERIZATION database, so no input is needed from partners.

City specific data

(Note: that the city must be divided into an urban and suburban area (given in XxY area or radius in Km units).

Filename: city.txt

Format:   name of the city, city longitude, city latitude, city population, urban city area, suburban city area, country abbreviation (ex.GR for Greece).


'ATHINAI', 23.74, 37.98, 3027331.00, urban area, suburban area, 'GR'

Neighbouring cities data

in order to locate the cities < 100 KM FROM THE CITY.

Filename: neigh_city.txt

Format:   y, name of the city, city longitude, city latitude, total city area, city population, country abbreviation

where y = an arbitrary city ID number, y= 1…..n for n neighbouring cities.


1, ATHINAI, 23.75, 37.99, 284.6,3072922, GR

EMISSIONS data for an area 150x150 km2 around the city

For each pollutant (CO, SO2, NOx, CH4, nmVOC):

TOTAL yearly emissions (TONS/YEAR) per SNAP group activity (see below), per area - urban suburban, rural (up to 150x150 Km2), neighbouring (<100KM)- MUST BE SPECIFIED.

(NOTE: you must specify whether the rural area emissions include the neighbouring city emissions, or not, data should be provided for at least the 11 main SNAP3 group categories):

Main SNAP3 group categorisation
Filenames:   total.urban.yr.txt, total.suburban.yr.txt, total.rural.yr.txt, total.neigh.yr_y.txt,

where y from neigh_city.txt, see above, yr = base year (year data is based on)

Format:   SNAP_GROUP, SNAP_SUBGROUP, SNAP_ACTIVITY, SO2 emissions [TONS/YEAR], Nox emissions [TONS/YEAR], CO emissions [TONS/YEAR], CH4 emissions [TONS/YEAR], nmVOC emissions [TONS/YEAR]

1 1 1 1.00 1.00 1.00 1.00 1.00
Diurnal variation per SNAP group activity

The diurnal variation per snap group activity. (NOTE: data should be provided for at least the 11 main SNAP3 group categories):

Filenames:   month.yr.txt, day.yr.txt, hour.txt

where yr = base year (year data is based on)

Example: (month.yr.txt):
SNAP 1 2 3 ... 7 8 ...
Month P.Power Comm. Ind.comb. ... Road Tr. Other ...
1 10.0% 8.3% 10.0% … 8.3% 8.3% …
2 10.0% 8.3% 10.0% … 8.3% 8.3% …
3 10.0% 8.3% 10.0% … 8.3% 8.3% …
4 8.3% 8.3% 8.3% … 8.3% 8.3% …
5 8.3% 8.3% 8.3% … 8.3% 8.3% …
6 5.0% 8.3% 5.0% … 8.3% 8.3% …
7 5.0% 8.3% 5.0% … 8.3% 8.3% …
8 5.0% 8.3% 5.0% … 8.3% 8.3% …
9 8.3% 8.3% 8.3% … 8.3% 8.3% …
10 10.0% 8.3% 10.0% … 8.3% 8.3% …
11 10.0% 8.3% 10.0% … 8.3% 8.3% …
12 10.0% 8.3% 10.0% … 8.3% 8.3% …

Example (day.yr.txt):
SNAP 1 2 3 … 7 8 …
Weekday P.Power Comm. Ind.comb. … Road Tr. Other …
Monday 16.0% 16.0% 14.3% … 17.0% 17.0% …
Tuesday 16.0% 16.0% 14.3% … 17.0% 17.0% …
Wednesday 16.0% 16.0% 14.3% … 17.0% 17.0% …
Thursday 16.0% 16.0% 14.3% … 17.0% 17.0% …
Friday 16.0% 16.0% 14.3% … 17.0% 17.0% …
Saturday 10.0% 10.0% 14.3% … 10.0% 10.0% …
Sunday 10.0% 10.0% 14.3% … 5.0% 5.0% …

Example: (hour.yr.txt):

SNAP 1 2 3 … 7 8 …
Time P.Power Comm. Ind.comb. … Road Tr. Other …
0 0.0% 0.0% 4.2% … 1.6% 0.0% …
1 0.0% 0.0% 4.2% … 1.3% 0.0% …
2 0.0% 0.0% 4.2% … 0.8% 0.0% …
3 0.0% 0.0% 4.2% … 0.5% 0.0% …
... ... ... ... ... ... ... ...
23 0.0% 0.0% 4.2% … 2.8% 0.0% …

Subdivision in Surface and Elevated emissions

Which emissions are surface, and which are elevated, must be specified.

Filename:   surface.elevated.txt

Format:   SNAP_GROUP, SNAP_SUBGROUP, SNAP_ACTIVITY, categorisation (S or E)



Subdivision of passenger cars category

  • diesel
  • catalytic
  • conventional
in % of total number of passenger cars or number of cars.

Filename:   division.txt

Format:   total number of cars, subdivision, percentage (or number of cars).

500000, diesel, 13.5 or 500000, diesel, 67500

With this data, LHTEE will then split nmVOC (non-methane VOC) into various categories (Ethane, Propane, HCHO etc.) and then create the urban area (surface) emission file and the urban point sources (elevated) emission file. Below please find a diagram of the process to be followed in order to create the emission inventory of each city.


For each day of the year, large scale pressure forcing (synoptic conditions) are needed. These data should include at least the prevailing wind speed and direction, temperature, temperature gradient and cloud coverage (e.g. at 850 hPa).

4.2.4 Emission model, street canyon modeling

The Transport Emission Model for Line Sources (TREM) was developed within SUTRA project. The main purpose of this model is to estimate the quantity of pollutants released to the atmosphere from vehicles. Estimation of traffic emission is based on the results of the traffic simulation model, local data sets including traffic counting, and emission factors for several vehicle types and driving patterns. 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 particulates. The emission data calculated by TREM are used as input for air quality modelling.

In the scope of SUTRA project, the VADIS model, a street canyon model based on a lagrangian near field approach, is being applied to estimate wind and concentration fields in specific areas of a city. This numerical tool calculates the dispersion of atmospheric pollutants around buildings using different information concerning the domain characteristics, obstacles, meteorology, air quality and emission data.

The model requires the following input data:

  • domain geometry, including obstacles vegetation/buildings) buildings (through node redefinition)
  • meteorology: wind and temperature profiles, ground temperature
  • emissions: passive pollutant rate, form/ size and location of sources
The latter data will be provided by the emission model, which in turn require vehicle specific emission coefficients and vehicle frequencies. The latter are supplied by the transportation model (see above). A more detailed description of needed data can be found in Annex A.

4.2.5 Rule-based impact assessment

4.2.6 Spatial Impact assessment

For the city-level modeling of air quality (SO2, NOx, TPS), some of the embedded models of the AirWare system will be used.

The models cover the entire city area (area covered by the transportation model network) and may range from 10 to more than 100 kilometers, at resolutions from 10 meters (near-field for traffic generated pollution) to 100 m to 1,000 meters, depending on city size. The models can be configured to directly produce the indicators for the multi-criteria assessment for both the base-line and the development scenarios, the former with the option of model calibration with air quality observation data. The current set of models includes:

  • Gaussian short-term and long-term models (ISC3/AERMOD) for one or 24 hours (multi-episode) simulations; and annual, seasonal results based on (hourly) weather frequency data, also adapted to very large number of sources; the model has been modified to simulate very larger numbers (>> 1,000) of sources (industrial, areal, or traffic segments) with a computationally efficient convolution approach.

  • A 3-D dynamic Eulerian model (TIMES/URBAN) for conservative pollutants or first-order decay and conversion (SO2=>SO4, NO=>NO2) for 24 hour episodes, combining all emission sources (industrial point sources, domestic area sources, land-use derived emissions, gridded traffic network emissions).

  • A dynamic (24 hours) photochemical box model (USEAP PBM).
All models perform a population exposure analysis as a post-processing step.

Data requirements:

  • Transportation and emission model results (network based emissions)
  • Meteorology: hourly data for a reference year: wind speed, direction, temperature, stability class, mixing height;
  • Emission inventories (spatially distributed) for industrial and domestic sources, and/or gridded data from MARKAL for development scenarios;
  • Geographical data (background map) and topography (DEM);
  • Population distribution (spatially explicit) for exposure assessment;
  • Air Quality Observations (reference year) for calibration.

4.2.7 Epidemiology, accidents

This group of analytical functions and models will be used to analyse the relationships between urban transportation an public health, by developing basic assessment tools for health impacts

  • from air pollution generated by traffic;
  • the spread of contagious diseases within the public transportation system
  • of traffic generated accidents.
Epidemiological data:

We required long time series of incidence rate (for each city and for different neighbourghoods within the city) for the following diseases:

  • Respiratory infections (Influenza, Cold, Pneumonia, and other infectious diseases from respiratory system)
  • Measles
  • Pertussis (Whooping cold)
  • Tuberculosis

Other required variables

  • number of persons per travel (crowdeness in public transportaion units),
  • average time of travel from each neighboardhoods to the others
  • connectivity between neighboardhoods (O-D Matrix).
Traffic accidents:

Data Required for Accidents Analysis :

  1. Population and Housing Characteristics (by area):
    • Number of children 0 -1 year
    • Number of children 2- 5 years
    • Number of children 6-11 years
    • Number of children 12-17 years
    • Number of adults 18 - 64 years
    • Number of adults with more than 65 years
    • Median household income
    • % households with UBN
    • % households owning a motor vehicle
    • Existence of prevention of traffic injuries programs
    • Transportation to work by mode :
      • Cars (%)
      • Subway (%)
      • Bus (%)
      • Walkaing (%)
      • Bicycle (%)
      • Train (%)
      • Others (%)
  2. Number of non-fatal and fatal accidents:
    • Number of pedestrian
    • Number of bicycle collision with motor vehicle
    • Number of bicycle collision with other
    • Motor vehicle occupant:
    • Car driver Car passenger
    • Motorcycle driver Motorcycle passenger
    • Other traffic mode
    • Unspecified traffic
  3. For all injured person we need:
    • Age , Sex and Marital status
    • Education level
    • Income or unemployment
    • Blood alcohol concentration
    • Place, time and type of the accident
  4. Street Physic Characteristics (by area):
    • Speed limits
    • Volume and traffic composition
    • Lighting and signage
    • Street types (avenue, one way, two ways)

    4.3 Indicators

    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 to define the status quo as a base line.

    Indicators defined, for example, by UNCED/Agenda 21, UNEP, or the Dobris Report and the indicators for sustainable cities defined by the EEA will be 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.

    4.4 Urban development scenarios

    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, migration and urbanisation or landuse change, including urban growth and sprawl, centralisation of material services especially in retailing, development of suburban structures or the revitalisation of urban centers, 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, 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 (e.g., the Praxitel project in France). Other policies could consist in providing incentives for using reduced emission technologies for urban transport.

    For each city, a set of likely development scenarios will be identified using European policy and individual urban plans as the starting point. These scenarios will 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.

    5. Implementation Requirements

    The primary objective of SUTRA is to develop a consistent and comprehensive approach and planning methodology for the analysis of urban transportation problems, that helps to design strategies for sustainable cities. 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.

    To implement this methodology in indiviudal cities, and primarily the case study cities will require a number of steps:

    • Identification of the institutional framework concerned with transportatio, land use, and the environment;
    • Establishing institutional and personal contacts;
    • Creating an implementation strategy that makes adopting the SUTRA approach and methodologies easy, and cost efficient, for a given city.
    The requirements have both an institutional and a technical dimension addressed below.

    5.1 Institutional constraints

    In institutional terms, SUTRA will analyse the responsibilities for planning and operational decision making in the individual cities. Experience already shows that this is usually complex and distributed, i.e., no individual entity has the sole responsibility for all sectors concerned (land use, transportation, environment).

    This obviously gets even more complex if we include the energy system,economic development, or public health and transportation accidents in this framework.

    The institutional constraints include the distributed nature of responsibilities but also data holdings. Therefor, for a succsessful implementation SUTRA must identify the appropriate level of political control, or try to build operational networks and partnerships between several departments and groups in a city administration.

    From a requirements point of view, this translated into the need for direct contact with the respective city administrations. As a first step, the project analyses the organisational framework and a list of contacts in each city administration (see: 3. Institutional Framework.

    5.2 Technical constraints

    Among the technical constraints we have identified limitations to the tools and in particular the simulation and forecatsing models.

    An example is the photochemical model OFIS, described below:

    OFIS has been derived from full three-dimensional models, which means that numerical schemes and physical parameterisation are taken from well-tested models. However, OFIS is a reduced form model which means that some simplifications have to be made in order to reduce computing time and input requirements. In fact, one of the basic assumptions made in OFIS is that each city can be assumed having a perfect circular shape with a homogeneous suburban ring surrounding it. The city radius is derived from a database with a given urban area. A further assumption is made that 2/3 of the urban area can be assigned urban core and 1/3 of the area as suburban. Similar assumptions are made for deriving the emission strength of area and point sources etc. - which gives the model its unique easyness to handle and to apply for many (in fact all) European Union cities.

    However, in a specific case of coastal location with extreme orography like Genoa, which does not follow the assumption of a perfect circular city in conjunction with the not taking into account the scatterness of industrial emissions, the OFIS model concept finds its limitation and model results have to be interpreted with care. Nevertheless, comparison of model results with available measurements for numerous cities within the European Union allow to conclude that OFIS is an efficient tool for assessing ozone exposure and evaluating air pollution abatement strategies. It remains to be analysed, to what extend the deviation from the model assumptions in the case of Genoa lead to non-realistic predictions of ozone exposure.

    6. References

    Bahn, O., Haurie, Kypreos, A. S. and Vial, J.-P. (1998)
    Advanced mathematical programming modeling to assess the benefits of international CO2 abatement cooperation, Environmental Modeling and Assessment, Vol. 3, Nos 1 and 2, pp. 107-116, June 1998.
    Berger, C.,Fuller, D., Haurie, A., Loulou, R., Luthra, D., Waaub, J.-P., (1990)
    Modelling Energy Use in the Mineral Processing Industries of Ontario with MARKAL-Ontario, Energy, Vol. 15, no. 9, pp. 741-758, 1990.
    BergerC., Dubois, R. Haurie, Lessard, A. E., Loulou, R. and Waaub, J.-P. (1992)
    Canadian MARKAL: An Advanced Linear Programming System for Energy and Environmental Modelling, INFOR,Vol. 30, No. 3, pp. 222-239, 1992.
    Carraro, C., Haurie, A. [eds.] (1996)
    Operations Research and Environmental Management, Kluwer, 1996.
    Din A., A. Dubois, E. Fragniere, A. Haurie, R. Kanala, M. Sella (1998)
    Energy/environment models and GIS, Cahiers du CUEH, No 1, Universitι de Genθve, pp. 79-106, 1998.
    Fedra, K. (2000)
    Model-based Decision Support for Integrated Urban Air Quality Management. In: Air Quality Management (Advances in Air Pollution Series), pp 243-260, WIT Press.
    Fedra, K. (2000)
    Urban environmental management: monitoring, GIS and modeling. Computers, Environment, and Urban Systems 23(1999) 443-457.
    Fedra, K., Haurie, H. (1999)
    A decision support system for air quality management combining GIS and optimization techniques. Int. J. Environment and Pollution Vol.12, Nos.2/3, 1999 , 125-146.
    Fedra, K. (1999)
    Urban Environmental Management. Integrating Monitoring, GIS and Simulation Models. GIM International, 7/13, 28-31.
    Fedra, K., Karatzas, K. and Moussiopoulos, N. (1999)
    Integrated urban environmental management: Monitoring, simulation, decision support. In: N.Moussiopoulo [ed.]: Research in the Fields of Energy and the Environment. Selected Scientific Articles of the Laboratory of Heat Transfer and Environmental Engineering. pp. 101-109. Thessaloniki.
    Fedra, K., Greppin, H., Haurie, A., Hussy, C., Dao, Hy, and Kanala, R. (1996)
    GENIE: An Integrated Environmental Information and Decision Support System for Geneva. Part I: Air Quality. Arch.Sci.Geneve, Vol. 49, Fasc.3, pp 247-263.
    Fragniere, E. and Haurie, A. (1996)
    A stochastic programming model for energy/environment choices under uncertainty, Int. J. Environment and Pollution, Vol. 6, Nos. 4-6, pp. 587-603, 1996.
    Fragniere, E., Haurie, A. and Kanala, R. (1999)
    A GIS-based regional energy-environment policy model , Int. J. of Global Energy Issues, Vol. 12, Nos 1-6, pp. 159-167
    Haurie, A. and Loulou, R. (1997)
    Modeling Equilibrium and Risk under Global Environmental Constraints in Energy Models. In: W.A MARTIN AND B. TOLWINSKI [eds], Modeling Environmental Policy, Kluwer acdemic publishers, Amsterdam, 1997.
    Martins, J.M. (1998)
    Dispersao de poluentes na atmosfera em condioes de vento fraco, PhD thesis, Dep. Ambiente e Ordenamento, Universidade de Aveiro.
    Martins, J.M. and Borrego, C. (1998)
    Describing the dispersion of pollutants near buildings under low wind-speeds: real scale and numerical results. Proc. Envirosoft 98, WIT, Las Vegas.
    Moussiopoulos, N. (1999)
    The EUMAC Zooming Model, A tool for local-to-regional air quality studies, Meteor. Atmos. Phys. 57 (1995), pp. 115-133.
    Sahm P and Moussiopoulos N. (1998)
    The OFIS model: An efficient tool for assessing ozone exposure and evaluating air pollution abatement strategies, Proceedings of the EUROTRAC2 Symposium 1998.
    Moussiopoulos, N., Sahm, P., Tourlou, P. M., Friedrich,R., Simpson, D. and Lutz, M. (199?)
    Assessing ozone abatement strategies in terms of their effectiveness on the regional and urban scales, vol. 34 issue 27, pp. 4691-4699.
    Sahm, P. and N. Moussiopoulos, (1999)
    The OFIS model: A new approach in urban scale photochemical modelling, EUROTRAC Newsletter 21/99, EUROTRAC-ISS, Garmisch-Partenkirchen (1999), pp. 22-28.
    Stanners, D. and Bourdeau, P. (1995)
    Europe's Environmnet. The Dobris Asessment. 676 pp., EEA, Copenhagen. Office for Official Publications of the European Communities, Luxembourg.
    UNCED (1992)
    Agenda 21. Earth Summit, The United Nations Programme of Action from Rio. 294 pp., UN Department of Public Information, NY.

    Appendix 1: Indicator definitions

    Appendix 2: Indicator Checklist/Questionnaire


    City-partners are asked to fill this table. This will be used to verify the availability of data for scenario generations and comparisons. The list has been compiled following EEA TERM list of indicators, integrated according to what described in previous pages. However, this list has not yet been co-ordinated with model data requirements. The list is provisional. Please feel free to add any indicator that you think to be relevant and for which you have data.

    Table columns:

    INDICATOR: name and short description of the indicator
    Unit of measurement used for this indicator
    Spatial dissag. spatial disaggregation or geographical/administrative resolution
    Sectoral dissag. sectoral disaggregation: /age /mode /income
    Period of data availability, temporal coverage
    Notes providing meta data for the indicator


    GROUP: Macroeconomic variables

    INDICATOR DESCRIPTION Unit Period Spatial Sectoral Notes
    GDP by sector (by area)          
    Employment by sector (by area)          
    Disposable income (by area)          
    Wage rates (by sector)          
    % of employment on teleworking (by area)          

    Group: Demographic variables

    INDICATOR Unit Period Spatial Sectoral Notes
    Number of households          
    Migration flows to and from the urban area          
    Population (by age class)          
    Land use and access to basic services          
    Residential building density          
    Retail and commercial centres (by area)          
    Leisure activities centres (by area)          
    Average passengers journeys time and length by mode          
    Average passengers journeys time and length by zone          
    Average passengers journeys time and length by purpose          
    Number of motor vehicles per household          
    % of persons with access to public transport (< 500M)          

    Transport demand and intensity

    INDICATOR DESCRIPTION Unit Period Spatial Sectoral Notes
    Passengers transport by mode (total          
    passengers, total pkm          
    passengers, total pkm per capita          
    passengers, total, pkm per GDP          

    Transport supply

    INDICATOR DESCRIPTION Unit Period Spatial Sectoral Notes
    Length of transport infrastructure by mode          
    Length of transport infrastructure by type (motorway, national road, municipal road, bicycle line)          

    Price signal

    INDICATOR DESCRIPTION Unit Period Spatial Sectoral Notes
    Expenditure for personal mobility per person per income group          

    Use of transport

    INDICATOR DESCRIPTION Unit Period Spatial Sectoral Notes
    Load factors for road freight transport          
    Average age of the vehicle fleet          
    Vehicle occupancy          


    Transport supply

    INDICATOR DESCRIPTION Unit Period Spatial Sectoral Notes
    Investment in transport infrastructure by mode          

    Price signals

    INDICATOR DESCRIPTION Unit Period Spatial Sectoral Notes
    Real passengers transport price by mode          
    Real freight transport price by mode          
    Taxes on fuels          
    Other charges (parking)          

    Efficient use of transport

    INDICATOR DESCRIPTION Unit Period Spatial Sectoral Notes
    Use of cleaner fuels (unleaded petrol; electric engines)          
    Number of alternative fuelled vehicles          
    Proportion of vehicle fleet meeting certain air emission standards (by mode)          
    Proportion of vehicle fleet meeting certain noise emission standards (by mode)          
    Speed limits          
    Movement restrictions          
    Technical standards          

    Appendix 3: VISUM data requirements

    Appendix 4: TREM fleet vehicle classes

    Appendix 5: VADIS input description

    Appendix 6: OFIS input description

© Copyright 1995-2018 by:   ESS   Environmental Software and Services GmbH AUSTRIA | print page