sutra [ s ustainable u rban tra nsportation ]

SUTRA [ Sustainable Urban TRAnsportation ]

Fifth framework programme of the European Community Environment and sustainable development

Final Meeting and Project Review

June 24th 2003, Gdansk

ARPAL Municipality of Genoa

THE GENOA CASE HISTORY

City centre: 276.278 inhabitants 28 km2 AreaUrban Territory: 603.896 inhabitants 239 km2 Area

Genoa lies in an extremely narrow, 30 km long coastal plain - highly dense urban territory

–TGenoa transport system - main characteristics

Public Transport154 million passengers in 2002 32 million Km covered785 Km bus network123 lines 2.462 stops

Modal share(6:30 a.m. - 9:30 a.m):

• Motocycles 15,1 %

• Cars 38,4 %

• Public Transport – railway 4,1 %

• Public Transport – road 42,4 %

Private Transport1402 km of streets networkcar 340.000motorcycle 95.000Taxi 869Bus 916LDV 35.000Highways (2002) : 75.058.628 vehicles in/out at the local (7) gates


Fifth framework programme of the European Community Environment and sustainable development ARPAL

VISUM: BASELINE SCENARIO

SUTRA DOMAIN

Private network

278 nodes;

668 links;

72 O/D zones.

(+ 9 external centroids)

Municipal Network

Provincial Network

Public network

52 PT lines;

390 stops;

72 O/D zones.

The Genoa case history



ARPAL

“24-hour” Private O/D matrix : derived from two peak-hours matrices (6:30 - 9:00 a.m. and 5:00 - 8:00 p.m.) and adapted on the base of reliable traffic flow daily profile:

24-hr Pr O/D matrix = 3,30 * AM-peak-hour + 3.15 * PM-peak-hour

“24-hour” Public O/D matrix: data not so precise as in previous case,

(as suggested by Public Transport Agency), the two peak-hours matrices

(6:30-9:00 a.m.; 5:00-8:00 p.m.) added and result multiplied by a factor 4:

24-hr Public O/D matrix = (AM-peak-hour + PM-peak-hour)*4

Few remarks about the 24-hours simulation1. 24-hours simulations



VISUM: Assignment and calibration 1. 24-hours simulations2. VISUM Assignment


Private Transport BASELINEVehicle-km 3,758,461Vehicle-hr 93,922Additional vehicle-hr due to congestion 30,542Vehicle-hr in jam 604

Baseline Indicators – 24-hours assignment

Public Transport BASELINEVehicle-km 54,091Vehicle-hr 12,621Passenger-km 1,617,404Passenger-hr 377,361Passenger-hr in overcrowded vehicles 29,462Congestion Rate 7.8%

ARPAL



ARPAL


1. 24-hours simulations2. VISUM Assignment3. TREM simulations

TREM simulations

Italian fleet composition(MEET) – yr

2000

Data available on Genoa, yr

2000

Genoa case

Gasoline Pc55.45

%Diesel Pc 5.20%GPL and Natural gas Pc 1.02%Gasoline LDV 0.93%Diesel LDV 2.53%HDV 0.27%Bus 0.27%Coaches 0.27%

Motorcycles34.05

%New Technologies 0.00%

Gasoline Passenger Cars(...)

27.3

7.620.26

(...)0

00

(...)New Technologies

N/DN/DN/DN/DN/DN/DN/D

1986-1991 (ECE 15-

04)

cc<1.41

1.41<cc<2.01cc>2.01

2006-2010 (EURO4)

cc<1.41

1.41<cc<2.01cc>2.01

Electric Vehicles

PassengersLDVPassengersLDV

Fuel Cell Vehicles

PassengersLDVUrban Bus

cc<1.41

1.41<cc<2.01

cc>2.01

cc<1.41

1.41<cc<2.01

cc>2.01

Passengers

LDVPassengers

LDV

PassengersLDV

Urban Bus

1972-1977 (ECE 15-

00/01)

2006-2010 (EURO4)

Electric Vehicles

Hybrid Electric Vehicles

Fuel CellVehicles

Thanks to additional data provided by UAV and from Genoa case specific data, it was possible to

UPDATE vehicular fleet for the baseline scenario

Genoa – Baseline scenario (CO emissions)




ARPAL

“Validation” of the TREM results:

spatially disaggregated traffic emission

through a comparison with “CORINAIR” emission coefficient:

spatially aggregated (top-down approach) traffic emission

Use of averaged emission coefficients developed by ANPA (Italian national Environmental Protection Agency) fortypical Italian urban driving cycles.

Comparison at “elementary data”:• Selected vehicle class (e.g., pre-ECE, <1.4 l)• Selected emission (e.g., “hot”, “cold”)

TREM: result analysis 1. 24-hours simulations2. VISUM Assignment3. TREM simulations



ARPAL


1. 24-hours simulations2. VISUM Assignment3. TREM simulations

TREM: result analysis

SUTRA provided spatial disaggregation and increased accuracy compared with previously developed emission factor data sets



ARPAL


1. 24-hours simulations2. VISUM Assignment3. TREM simulations4. ISC simulations

ISC: result analysis

Position of measuring stations compared to concentration maps from city level models

CO (traffic related, chemically inert)

Validation of dispersion model results with available measurements



ARPAL






ARPAL


Analysis of behaviours of Lisbon and Genoa facing Common ScenariosImplementation on two strongly different urban shape and texture of the same future scanarios:

Genoa

Lisbon



ARPAL


Transportation system response to CSs:

0

1

2

3

Baseline SC1 SC2 SC3 SC4

0

1

2

3


0

0.2

0.4

0.6

0.8

1

1.2


2) Ratio between Lisbon and Genoa

The “linear” town reacts in a more rigid way to exogenous pushes than the distributed town, this reflects in time spent to cover a km of network

1) Variation in time spent to cover a kilometer

-In conditions of traffic congestion

-In traffic Jams

- Total



ARPAL


Traffic emissions forecasted in CSs:

CO GenoaLisbon Pm GenoaLisbon

CO2 GenoaLisbon SO2 GenoaLisbon

NOx GenoaLisbon VOC GenoaLisbon

Emission indicators have similar behaviour to transportation indicators, with specific patterns according to single pollutants

CO Lisbon

0.00

0.20

0.40

0.60

0.80

1.00

1.20

BASELINE CS1 CS2 CS3 CS4

CO Genoa

0.00

0.20

0.40

0.60

0.80

1.00

1.20


CO2 Lisbon

0.00

0.50

1.00

1.50

2.00

2.50

3.00


CO2 Genoa

0.00

0.50

1.00

1.50

2.00

2.50

3.00


NOx Lisbon

0.00

0.20

0.40

0.60

0.80

1.00

1.20


NOx Genoa

0.00

0.20

0.40

0.60

0.80

1.00

1.20


Pm Lisbon

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80


Pm Genoa

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60


SO2 Genoa

0.00

0.20

0.40

0.60

0.80

1.00

1.20


SO2 Lisbon

0.00

0.20

0.40

0.60

0.80

1.00

1.20


VOC Lisbon

0.00

0.20

0.40

0.60

0.80

1.00

1.20


VOC Genoa

0.00

0.20

0.40

0.60

0.80

1.00

1.20




ARPAL


Dispersion modeling in the two cities: City scale dispersion models:

Concentration map for CO on the whole urban area of Genoa



ARPAL


Dispersion modeling in the two cities: Urban Canyon models

Dispersion 3-D map on the Lisbon Hot Spot



ARPAL


Concentration of pollutants in CSs:

0 50 100 150 200 250 300 350 400 450

W est/East (m )

0

50

100

150

200

250

300

350

400

450

So

uth

/No

rth

(m

)

5 0

1 5 0

3 0 0

5 0 0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

7 0 0 0

C O(µg.m -3)

Ou

ro S

tre

et

Pra

ta S

tre

et

Betesga Street

Already compromised areas (Hot Spot) suffer dramatically strong variations in emission levels in their peak values

Average concentrations are not directly comparable to measured data and usually underestimated

CO GenoaLisbon

0.00

0.50

1.00

1.50

2.00

2.50

BASELINE CS1 CS2 CS3 CS40.00

0.50

1.00

1.50

2.00

2.50


NOx GenoaLisbon

0.00

0.50

1.00

1.50

2.00

2.50


0.50

1.00

1.50

2.00

2.50


Pm10 GenoaLisbon

0.00

0.50

1.00

1.50

2.00

2.50

3.00


0.50

1.00

1.50

2.00

2.50

3.00




ARPAL






ARPAL


Concentration of pollutants in CSs:Geometry of the AQ stations network is projected to measure hot spot data, therefore city level averages are generally underestimated

-Next refining will be analysis of values in a smaller monitored area



ARPAL


1. 24-hours simulations2. VISUM Assignment3. TREM simulations4. ISC simulations 5. VADIS simulations6. Scenario analysis7. Conclusions & Outlook

Conclusion and Outlook:

SUTRA has offered a unique opportunity to:• develop, calibrate and validate an “integrated transport & impact assessment modelling suite”; • compare the obtained results with previous experiences;• investigate future and city specific scenarios.

A particolar attention has been done to “put and keep the results in context”, being both Genoa partner (Municipal

Transport Office and Regional Environmental Agency) local operative Authorities

Among the main trade-offs of the SUTRA project: • implementation of an operative transport model (that became the local standard);• synergy bewteen transport and environmental Authorities, as well as other UE partners;• disaggregation and detailed studies of local “hot-spot” for the main transport related pollutants ( recent EU “air quality” directives)

sutra [ s ustainable u rban tra nsportation ]

Documents

hr public od matrix

hour public od matrix

peakhour pmpeak

peakhours matrices

hr pr od matrix

hour private od matrix

od zones

public transport road