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SUTRA Final Review

D13 - Multi Criteria AnalysisD13 - Multi Criteria Analysis

Gdansk, Poland23rd-24th June 2003

Presented by the Ministry of the Environment, Israel

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WP 13: Multi-Criteria Analysis

• OBJECTIVESOBJECTIVES• DEVELOPMENT OF RULES AND DESCRIPTORSDEVELOPMENT OF RULES AND DESCRIPTORS• MULTI CRITERIA ANALYSIS – METHODOLOGYMULTI CRITERIA ANALYSIS – METHODOLOGY• MCA OPTIMISATION EXCERCISEMCA OPTIMISATION EXCERCISE• RESULTSRESULTS

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WP 13: Multi-Criteria Analysis - Objectives

• The primary objectives of WP 13 “Scenario Comparison and Multi-criteria Analysis” is:

– the comparative analysis of the set of scenarios for each city using sustainable city indicators as defined in WP 8 and 10,

– the multi-criteria comparative analysis and selection of a non-dominated set of alternatives and

– the identification of the most promising scenario or small set of candidate scenarios from each test site.

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WP 13: Multi-Criteria Analysis - Rules Based Analysis

• The objective of a rule-based expert system is to reduce the multidimensionality of the information and to collapse all the data into one dimension so that the different scenarios can be analysed and compared in the same terms.

 

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WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology

• Classification of indicators (and derived indicators) into categories which define a “sustainable city” and “sustainable transportation”.

 

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Economic Performance

Social Performance

Environmental Quality

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WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology

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SUSTAINABLE TRANSPORT PERFORMANCE

ENVI RONMENTAL QUALI TY Concept Sub-concept I ndicator Units Source

Total passenger emission in a year [tons] TREM Passenger transport emission per capita in a year [tons/capita] TREM Passenger transport emission per pass-km in a year [tons/pass-km] TREM NO x emissions Percentage of private transport emission over total passenger transport emission in a year [%] TREM Total passenger emission in a year [tons] TREM Passenger transport emission per capita in a year [tons/capita] TREM Passenger transport emission per pass-km in a year [tons/pass-km] TREM CO2 emissions Percentage of private transport emission over total passenger transport emission in a year [%] TREM Total passenger emission in a year [tons] TREM Passenger transport emission per capita in a year [tons/capita] TREM Passenger transport emission per pass-km in a year [tons/pass-km] TREM VOC emissions Percentage of private transport emission over total passenger transport emission in a year [%] TREM Total passenger emission in a year [tons] TREM Passenger transport emission per capita in a year [tons/capita] TREM Passenger transport emission per pass-km in a year [tons/pass-km] TREM CO emissions Percentage of private transport emission over total passenger transport emission in a year [%] TREM Total passenger emission in a year [tons] TREM Passenger transport emission per capita in a year [tons/capita] TREM Passenger transport emission per pass-km in a year [tons/pass-km] TREM

Emissions pressure

PM10 emissions Percentage of private transport emission over total passenger transport emission in a year [%] TREM Peak concentration [g/m3] VADIS/OFIS Average annual concentration [g/m3] ESS Atmospheric [NOx] Above max. threshold [%] ESS Peak concentration [g/m3] VADIS/OFIS Average annual concentration [g/m3] ESS Atmospheric [CO] Above max. threshold [%] ESS Peak concentration [g/m3] VADIS/OFIS Average annual concentration [g/m3] ESS

Air quality

Atmospheric [PM10] Above max. threshold [%] ESS

Grouping of Indicators to summarise data.

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WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology

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SOCI AL TRANSPORTATI ON PERFORMANCES

Mortality Number of deaths in a year per capita [number] GDANSK Number of deaths in a year per pass-km [number] GDANSK Percentage of total costs [%] FEEM Morbidity Number of days lost in a year per capita [number] GDANSK

Transport rel. illness

Percentage of total costs [%] FEEM Crowding: hours per capita spent on overcrowded public transports in a year.

[hours/capita] VISUM Stressing factor

Traffic jams: hours per capita spent yearly in traffic jams hours/capita] VISUM PM10: Number of inhabitants under exposure [number] OFIS Nox: Number of inhabitants under exposure [number] OFIS

Health risks

Pop. Pollution exposure

O3: Number of inhabitants under exposure [number] OFIS Number of inhabitants [number] CP Percentage of population under 18 [%] CP

City dynamism

Percentage of population over 64 [%] CP Area [km2] CP Average distance PrT [km] VISUM

Transports requirements

Urban sprawl Average distance PuT [km] VISUM Total passenger transport demand per year [pkm/year] VISUM

Passenger demand Public passenger transport demand per year [pkm/year] VISUM

Transport intensity

Traveling distance Average distance traveled each year per person [pkm/capita] VISUM Total number of accidents with personal injuries in a year per capita

[number/capita] GDANSK

Total number of accidents with personal injuries in a year per pass-km

[number/pass-km GDANSK Transport safety

Percentage of total costs [%] FEEM

ECONOMICAL PERFORMANCE Penetration rates of EV in car fleet composition [%] FEEM/VISUM Penetration rates of HEV in car fleet composition [%] FEEM/VISUM New technology

penetration Penetration rates of fuel cell electric vehicles in car fleet composition

[%] FEEM/VISUM

Private transport

Urban average private car occupancy rate [number] FEEM/VISUM

Transport efficiency

Use efficiency of trans. systems

Public transport

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WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology

• Derived Indicators are then developed, which aim to maximise efficiencies. Two examples are:

Transportation IntensityTransportation Intensity emissions efficiencyemissions efficiency

• Each set of derived indicator is based on a number of lower indicators.

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WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology

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SOCI AL TRANSPORTATI ON PERFORMANCES

Mortality Number of deaths in a year per capita [number] GDANSK Number of deaths in a year per pass-km [number] GDANSK Percentage of total costs [%] FEEM Morbidity Number of days lost in a year per capita [number] GDANSK

Transport rel. illness

Percentage of total costs [%] FEEM Crowding: hours per capita spent on overcrowded public transports in a year.

[hours/capita] VISUM Stressing factor

Traffic jams: hours per capita spent yearly in traffic jams hours/capita] VISUM PM10: Number of inhabitants under exposure [number] OFIS Nox: Number of inhabitants under exposure [number] OFIS

Health risks

Pop. Pollution exposure

O3: Number of inhabitants under exposure [number] OFIS Number of inhabitants [number] CP Percentage of population under 18 [%] CP

City dynamism

Percentage of population over 64 [%] CP Area [km2] CP Average distance PrT [km] VISUM

Transports requirements

Urban sprawl Average distance PuT [km] VISUM Total passenger transport demand per year [pkm/year] VISUM

Passenger demand Public passenger transport demand per year [pkm/year] VISUM

Transport intensity

Traveling distance Average distance traveled each year per person [pkm/capita] VISUM Total number of accidents with personal injuries in a year per capita

[number/capita] GDANSK

Total number of accidents with personal injuries in a year per pass-km

[number/pass-km GDANSK Transport safety

Percentage of total costs [%] FEEM

ECONOMICAL PERFORMANCE Penetration rates of EV in car fleet composition [%] FEEM/VISUM Penetration rates of HEV in car fleet composition [%] FEEM/VISUM New technology

penetration Penetration rates of fuel cell electric vehicles in car fleet composition

[%] FEEM/VISUM

Private transport

Urban average private car occupancy rate [number] FEEM/VISUM

Transport efficiency

Use efficiency of trans. systems

Public transport

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WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology

• The individual indicators are qualitatively classified, to enable statistical analysis.•Three ranges for each indicator is set, and standard deviation is calculated.

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TOTAL PASSENGER TRANSPORTATION DEMAND

Classification LOW MEDIUM HIGH

Value ranges [ < (mean – 1STD)]

[(mean – 1 STD) – (mean + 1 STD)]

[ > (mean + 1STD) ]

TOTAL PASSENGER TRANSPORTATION DEMAND (pkm/year)

City partner Data (E+09) Mean (E+09) Standard deviation (E+09)

Gdansk 1.88

Genoa 2.23

Lisbon 11

Tel Aviv 0.013

Thessaloniki 0.93

3.2 4.41

Example of qualitative ranges for indicator “total passenger transportation”.

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WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology

• Once the ranges have been established, a matrix for every primary indicator (transport intensity) is developed to show all possible combinations of alternatives. • From such a matrix we can identify the combinations which represent the most efficient options and thoseindicators that we would want to minimise/maximise.• Each combination is represented by a respective rule. TTPD = TOTAL PASSENGER TRANSPORTATION DEMAND

PPDT= PUBLIC PASSENGER TRANSPORT DEMANDADTP = AVERAGE DISTANCE TRAVELLED

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TTPD = H TTPD = M TTPD = L

PPDT ADTP

H M L H M L H M L

H H* H* M H H M M M M

M H* H* M H M L M L L

L M M M M L L M L L

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WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology

Representation of list of I}ndicators: Lisbon Example

 

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INDICATOR VALUE

Inhabitants 4193238 Population under 18 21.5 Population over 64 18 % employment in services 93 % employment on teleworking 50 Total passenger transport demand per capita 3567.715847 Total passenger transport demand per km2 5356348.344 Public passenger transport demand per capita 1990.354776 Public passenger transport demand per km2 2988195.8 CO2 Total passenger transport emission per km2 637.9634801 CO2 Passenger transport emission per capita 0.424929872 CO2 Passenger transport emission per pass-km 119.104 NOx Total passenger transport emission per km2 0.97386323 NOx Passenger transport emission per 1000 inh 0.648663428 NOx Passenger transport emission per pass-km 0.181815 VOC Total passenger transport emission per km2 1.296813462 VOC Passenger transport emission per 1000 inh 0.863771667 VOC Passenger transport emission per pass-km 0.242108 CO Total passenger transport emission per km2 6.530254207 CO Passenger transport emission per 1000 inh 4.349622153 CO Passenger transport emission per pass-km 1.21916 PM10 Total passenger transport emission per km2 0.077336198 PM10 Passenger transport emission per 10^6 inh 51.51150749 PM10 Passenger transport emission per pass-km 0.0144382 NOx Maximum concentration 982.22 NOx Average concentration 11.11 NOx Nonzero average 14.93 NOx Above maximal threshold 0.07 CO Maximum concentration 5080.23 CO Average concentration 81.77 CO Nonzero average 109.83 CO Above maximal threshold 0.07 O3 AOT (max) 1.01 O3 AOT (ave) 0.11 O3 E120 (domain) 0 Urban average car occupancy rate 1.47 % of public transport over total passenger transport 56.8 Penetration rates of Electric Vehicles 7 Penetration rates of Hybrid Electric Vehicles 13 Penetration rates of Fuel Cell Electric Vehicles 7 Time loss for congestion 133451 Number of deaths/yr/1000inh 3.132258262 Number of deaths/yr/p10^6km 0.877945036 Number of days lost/yr/capita 0.179425287 Time in overcrowded veh per pass*10^6km 0 Time spent in traffic jams per pass*10^6km 2.004842031

Transport Intensity 0.992970726 Emissions efficiency 0.939737155

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WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology

• Development of rules and descriptors into a following structures:

IF conditionAND/OR conditionTHEN conclusion

 

TTPD = TOTAL PASSENGER TRANSPORTATION DEMAND

PPDT= PUBLIC PASSENGER TRANSPORT DEMANDADTP = AVERAGE DISTANCE TRAVELLED

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RULE 0001

IF TPTD = = HIGH AND PPTD = = HIGH OR

PPTD = = MEDIUM AND ADTP = = HIGH OR

ADTP = = MEDIUM

THEN TRAN INT = = HIGH

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WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology

 

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Descriptor Operator ValueDensity ==, <,>,!=,……. highConclusion:

Descriptor Assignment ValueDensity = high

Total transportation passenger demand

A: TPTD

U: [pkm/year]

V: Low [ …]

V: Medium [ … ]

V: High [ … ]

R: 0001 / 0002 / 0003 / …

Q: What is the total transportation passenger demand measured in pkm in a period of one year?

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WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology

• Using the rules developed, analysis of city common scenarios (as defined by FEEM) is carried out to asses the performance of each scenario.

• This was carried out according to the following division of indicators:

transport demand, pollutant emissions, air quality, ozone concentration, stressing factors, human health, public/private transportation.  

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WP 13: Multi-Criteria Analysis Rules Based cont….Cross scenario comparison for transportation demand. Within the case studies, Tel Aviv is the city with the most efficient performance for scenario 3, whereas Lisbon shows the highest values for scenario 1 and 2 and Gdansk for scenarios 3 and 4.

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Gdansk Genoa Lisbon Tel Aviv Thessaloniki

Scenario 0 Total Tr [pkm/yr] 1879E+06 2236E+06 12891E+06 4080E+06 3587E+06 Public Tr [pkm/yr] 771E+06 590E+06 4449E+06 562E+06 598E+06 Average dist. [pkm/cap] 4116.11 3521.02 4805.4 1562.15 4010 Scenario 1 Total trans. [pkm/yr] 2039E+06 3218E+06 14690E+06 6292E+06 3283E+06 Public trans. [pkm/yr] 771E+06 1561E+06 8346E+06 1153E+06 1052E+06 Average dist. [pkm/cap] 3292.88 3241.21 3567.72 1541.29 2348.47 Scenario 2 Total trans. [pkm/yr] 3599E+06 5163E+06 21933E+06 17128E+06 5936E+06 Public trans. [pkm/yr] 771E+06 1211E+06 7283E+06 797E+06 960E+06 Average dist. [pkm/cap] 4939.33 5201.07 5230.74 4195.92 4246.28 Scenario 3 Total trans. [pkm/yr] 1635E+06 1426E+06 6918E+06 1919E+06 1461E+06 Public trans. [pkm/yr] 771E+06 1561E+06 3796E+06 471E+06 5071E+06 Average dist. [pkm/cap] 3292.88 2610.90 3486.23 993.24 2209.66 Scenario 4 Total trans. [pkm/yr] 2237E+06 2233E+06 10222E+06 6837E+06 2665E+06 Public trans. [pkm/yr] 771E+06 540E+06 3313E+06 360E+06 461E+06 Average dist. [pkm/cap] 4939.33 4087.46 5151.37 3539.56 4029.50

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WP 13: Multi-Criteria Analysis - MCA Methodology

• The objective of the multi-criteria analysis is to identify within the different scenarios, the city/scenario that has the most efficient performance and which maximises the pre-defined derived indicators.

• The MCA also identifies the factors that leads this city to perform more efficient than the rest, for the purpose of extracting policy strategies.

• Optimisation is carried out via mutli criteria analysis to identify the city that performs the most efficiently and maximises pre defined indicators for transportation efficiency.

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WP 13: Multi-Criteria Analysis – MCA Optimisation

• The DSS software is used to carry out optimisation, which automatically calculates the efficient point which is closest to utopia.

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K.Fedra 2002

Decision SupportDecision SupportDecision Support

Reference point approach:Reference point approach:

nadirnadirnadir

utopiautopiautopia

A1A1

A2A2

A3A3

A4A4

betterbetter

efficient efficient pointpoint

criterion 1criterion 1

crite

rion 2

crite

rion 2 A5A5

dominateddominated

A6A6

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WP 13: Multi-Criteria Analysis – MCA Optimisation

• Optimisation of scenarios and cross scenario compasrions required gathering all indicator results (D12) and adding those derived indicators which we want to maximise/minimise.

• Each city then produces an input file per scenario.

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WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology

Representation of list of Indicators: Tel Aviv Example

 

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CITY NAME:Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4

Cod Indicator name Units Source (Core) Dynamic, rich, virtuous

Dynamic, rich, vicious

Virtuous pensioners

Vicious pensioners

City specific 1

1. Demography1.a Number of

inhabitantsnumber City

partners2,611,500 4,081,984 4,081,984 1,925,882 1,925,882 3,501,500

1.b Percentage of population under 18

% City partners

33 33 33.00 28.00 28.00

1.c Percentage of population over 64

% City partners

12 15 15.00 27.00 27.00

2. Land use2 Area [km2] km2 City

partners1,447,000 1,447,000 1,447,000 1,447,000 1,447,000 1,447,000

2.a Structural density ESS2.b Functional

distribution of urban functions

ESS

2.c Index of mixed use number ESS3. Economy3.a GDP per capita,

expressed in current Euro price in Purchasing Power Parities (PPP)

euros/capita

City partners

12,396

3.b Percentage of employment in services over total employment

% City partners

77.0 97.0 97.0 82.0 82.0

3.c Percentage of employment on teleworking over total employment

% City partners

0.0 50.0 50.0 15.0 15.0

4. Passenger trasnportation demand4.a Total Passenger

transport demand per year [pkm per year]

pkm/yr VISUM 9,860.0 15,204.5 41,391.9 4,636.7 16,523.9 16218.41

City specific 2

3,501,500

1,447,000

15880.88

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WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology

Representation of list of Indicators: Tel Aviv Example cont..

 

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4.b Public Passenger transport demand per year [pkm per year]

pkm/yr VISUM 1,360 2,787 1,926 1,139 871 1266.89

4.c Average distance travelled in each year per person [pkm per capita]

pkm/ capita

VISUM 0.0 0 0 0 0

5. Emissions 5.1 CO25.1.a Total passenger

transport emission in a year

tons/yr TREM

0.87 1.78 8.93 0.46 2.51 5.1.b Passenger transport

emission per capita in a year

tons/yr/capita

TREM

0.00 0.00 0.00 0.00 0.00 5.1.c Passenger transport

emission per pass-km in a year

tons/yr TREM

0.00009 0.00012 0.00022 0.00010 0.00015

994.05

Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4Cod Indicator name Units Source (Core) Dynamic, rich,

virtuousDynamic, rich, vicious

Virtuous pensioners

Vicious pensioners

City specific 1

City specific 2

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WP 13: Multi-Criteria Analysis – Results

• A set of indicators produced for each scenario.

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WP 13: Multi-Criteria Analysis – Results

• Definition of each indicator (range, normalisation, reference point), and location of reference point.

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WP 13: Multi-Criteria Analysis – Results

• The model allows a selection of indicators to be chosen for analysis of the optimum scenario.

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WP 13: Multi-Criteria Analysis – Results

• The efficiency point represents the best alternative giventhe constraints.

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WP 13: Multi-Criteria Analysis – Results

• Results of optimisation for CO2 emissions from passenger transportation vs. teleworking employment

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WP 13: Multi-Criteria Analysis – Results

• Representation of the complete set of indicator values which relate to the efficiency point/scenario.

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