Modelling Sustainable Urban Transport

Gdansk, Gdansk, 19.04.2319.04.23

PTV Planung Transport Verkehr AG D-76131 Karlsruhe www.ptv.de

Modelling Sustainable Urban Modelling Sustainable Urban TransportTransport

Josef JankoJosef Janko

2

OverviewOverview

Transport Modelling

• Background

• City Examples

Sustainable Transport

• Methods

• Examples

Implementation of Project Features

• Modelling of City Structure Changes

• Indicator Determination

• Input for the Emission Model TREM

3

OverviewOverview

Transport Modelling

• Background

• City Examples


• Methods

• Examples





4

Transport Modelling in the SUTRA EnvironmentTransport Modelling in the SUTRA Environment

City Infrastructure

Transport ModelEmission

Environment EconomyPublic Health

Energy

Supply:networks for

different modes

Demand:OD-matrices for different segments

Volumes, speeds, trip lengths, number of cold starts, ratio hot/ cold driving

Volumes,trip lengths, journey times

Volumes, trip

lengths,journey times

Volumes, trip lengths,

journey times

Volumes, link lengths, journey times

5

Software +

Digital NetworkDigital NetworkDataData

Structural Structural DataData

Traffic Behaviour

Data

Ple

ase

tu

rn o

ve

r !

Sp

ac

e f

or

furt

he

r tr

ips

ove

rlea

f !

first name

weekday Did you leave from hom e on this day?

yesno, because

Trip Sheet

Person #

P TV AG

At what TIM E did you start this trip?

For what REASON did you do this trip?

Please estim ate the TRIPDISTANCE as precisely as possible!

Please mark all used meansand enter the respective driving/walking time

If the starting point of the first trip was your home but another place then enterfull postal address here

not

Please enter correspondingnumber from the master sheet

work place

business/offic ia l

education/school

purchase/service

bring/fetch som eone

FIRST TRIP

time of departure

PURPO SE at tr ip destina tion

M E AN S O FTR AN SP O RT

walking only

bicycle

motorcycle/scooter

car as driver

car as passenger

public bus

time of arrival

distance (estimated)

m iles m (yd)

Full posta l ADDRESS

long-distance train

tram/underground

next trip: next colum n

W hat was your destination?

le isure

home

other

local/regional train

walking to vehicle

walking to destination

driving /walking tim e(m ins)

At what TIM E did youarrive?

By w hich MEA NS OF TRANSPORT did you reach your destination?

work place

business/offic ia l

education/school

purchase/service

bring/fetch som eone

SECOND TRIP

time of departure

M E AN S O FTR AN SP O RT

walking only

bicycle

motorcycle/scooter

car as driver

car as passenger

public bus

time of arrival

distance (estimated)

m iles m (yd)

(street, nr)

(postal code, city)

long-distance train

tram/underground

next trip: next colum n

le isure

home

other

local/regional train

walking to vehicle

walking to destination

driving /walking tim e(m ins)

PURPO SE at tr ip destina tion

Full posta l ADDRESS

Transport Model :Transport Model :

6

Trip GenerationActivity Model

Trip GenerationActivity Model

• Population segmentation

• Activity chains

• Population segmentation

• Activity chains

OD matrixmode 2

OD matrixmode 2

OD matrixmode 1

OD matrixmode 1

OD matrixmode n

OD matrixmode n

Trip DistributionGravitation Model

Trip DistributionGravitation Model

• Attractiveness data of zones

• Attractiveness data of zones

• impedancematrix

• servicequality

• impedancematrix

• servicequality

Mode ChoiceLOGIT Model

Mode ChoiceLOGIT Model

• mode attributematrix

• mode attributematrix

Structural and behavioural data

• specific mode preferences

• specific mode preferences

Transport Model : the Demand Side ...Transport Model : the Demand Side ...

7

OD matrixmode 2

OD matrixmode 2

OD matrixmode 1

OD matrixmode 1

OD matrixmode n

OD matrixmode n

• impedancematrix

• servicequality

• impedancematrix

• servicequality

Traffic AssignmentTraffic Assignment• modeattributematrix

• modeattributematrix

Net

wor

k D

escr

iptio

n

Dem

and

Mod

el

Traffic volumes, journey times, journey costsTraffic volumes, journey times, journey costs

Evaluation of schemesEvaluation of schemes

Transport Model : ... and the Supply SideTransport Model : ... and the Supply Side

8

OverviewOverview

Transport Modelling

• Background

• City Examples


• Methods

• Examples





9

Network Model GdanskNetwork Model Gdansk

10

Network Model Geneva (1)Network Model Geneva (1)

11

Network Model Geneva (2)Network Model Geneva (2)

12

Network Model Genoa (1)Network Model Genoa (1)

13

Network Model Genoa (2)Network Model Genoa (2)

14

Network Model Lisbon (1)Network Model Lisbon (1)

15

Network Model Lisbon (2)Network Model Lisbon (2)

16

Network Model Tel Aviv (1)Network Model Tel Aviv (1)

17

Network Model Tel Aviv (3)Network Model Tel Aviv (3)

18

Network Model Thessaloniki (1)Network Model Thessaloniki (1)

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Network Model Thessaloniki (2)Network Model Thessaloniki (2)

20

City Networks (1) : Model StatisticsCity Networks (1) : Model Statistics

Indicator Gdansk Genev

a

Genoa Lisbon Tel Aviv Thessaloniki

Network Size Zones 175 280 82 85 580 316

Nodes 2348 936 285 1124 3144 1386

Links 5546 2900 888 2940 11850 4084

PrT Assignment average speed [km/h] 73 50 42 16 65 43

PuT Assignment mean ride distance [km] 6.7 4.2 9.5 6.5 4.4

mean in-veh. speed [km/h] 25 20 27 21 13

21

City Networks (2) : Junction DensityCity Networks (2) : Junction Density

0

100

200

300

400

500

600

700

800

0 10000 20000 30000 40000 50000 60000 70000

Distance from the centre

Jun

ctio

ns

Gdansk

Geneva

Genoa

Lisboa

Thessaloniki

Tel Aviv

22

City Networks (3) : Junction Density - NormalisedCity Networks (3) : Junction Density - Normalised

Distance from the Centre

Jun

ctio

n D

ensi

ty

Gdansk

Geneva

Genoa

Lisboa

Thessaloniki

Tel Aviv

23

OverviewOverview

Transport Modelling

• Background

• City Examples


• Methods

• Examples





24

Sustainable TransportSustainable Transport

Objective: reduce the usage of private cars

Urban Planning:

• mixed land use

• high density land use to reduce trip lengths

Economic incentives to use desired transport modes in variable pressure:

• improvement of public transport (new or better systems)

• P+R

• HOV lanes

• bus lanes

• usage charges (parking, area, roads)

• removal of road space

25

Modelling Park + Ride - PrincipleModelling Park + Ride - Principle

Public Transport

Private TransportInitial

Potential

Final

26

Modelling Park + Ride - MethodModelling Park + Ride - Method

Define P&R interchange sites

• for each city centre zone add a virtual private transport link at the P+R site with

impedance = f(PuT price, PuT travel time, ...) and connect zone to the other end of the

virtual link

Determine P&R demand

• perform private transport assignment and determine proportion of demand using virtual

P&R links

Split P&R demand off private transport demand

• add a zone to each P&R site and connect to both private and public transport

• subtract P&R demand from private demand

• add back private transport leg (up to P&R zone)

• add public transport leg (from P&R zone) to PuT demand

Assign changed public and private transport demands separately

27

High Occupancy VehiclesHigh Occupancy Vehicles

Focus in SUTRA: car pools

Incentives for participating in a car pool:

• Availability of dedicated HOV lanes

• Reserved parking spaces at convenient locations

• Exemption from road pricing

Typical usage similar to Park & Ride

• car pool members start their trips separately,

• meet at an agreed place,

• share one vehicle for remaining leg

28

HOV - MethodHOV - Method

Approach similar to Park & Ride

Define HOV as a new transportation system

Add HOV incentives to network, e.g. additional lanes reserved to transportation system HOV and closed for car etc.

Define HOV meeting places (zone + virtual link)

Adjust demand

• Determine HOV demand

• Split off HOV demand from private transport demand

• Add back individual legs to private transport demand

• Form HOV demand from shared legs

Reassign changed demand to network

29

Road User Charging (1)Road User Charging (1)

• traditional approach:route choice depends on travel time

• considering toll: route choice depends on travel time and costs

problem:drivers have different sensitivity to costs („value of time“)

30

Road User Charging (2) : Value of TimeRoad User Charging (2) : Value of Time

impedance CritR of a route R consists of a time component tR and a

cost component cR.

time and cost are connected through a VT [€ / h], [$ / h], ...

)VT/1(ct RR

)VT/1(ctRL RL

LL

CritR

31

Road User Charging (3) : MethodsRoad User Charging (3) : Methods

)VT/1(ct RR CritR

“traditional” toll assignment

constant VT for all users

mono-criterial

TRIBUT

randomly distributed VT

bi-criterial

32

OverviewOverview

Transport Modelling

• Background

• City Examples


• Methods

• Examples





33

Modelling of City Structure ChangesModelling of City Structure Changes

Conventional Demand Modelling

• based on structural data (residents, work places, educational facilities, shopping

facilities)

• based on behavioural data (homogenous groups, trip chains, OD groups)

SUTRA Demand Modelling

• „common“ „hypothetical“ scenarios

• dedicated demand modelling beyond the project scope

• derivation of the scenario demand from the analysis case.

34

Demand Modelling DetailsDemand Modelling Details

Trip generation

• change of the sum of all trips in the demand matrices based on population and mobility

rates

Mode share

• individual treatment of the demand matrices

Trip distribution

• sensitivity factor: small ... strong reduction of long distance trips

• form factor: change of the ratio of trips between the zones.

35

Common Scenarios DefinitionCommon Scenarios Definition

Young and Virtuous

Young and Vicious

Old and Virtuous

Old and Vicious

The city is growing and getting younger: The city is shrinking and getting older: 1 2 3 41. population growth p.a. including positive natural (+0.5% pa) and migratory balances (+1% pa); 1.5%

1. population growth p.a. including negative natural and migratory balances (each contributing -0.5% pa); -1.0% 1.5% 1.5% -1.0% -1.0%

2. youth share increases 0% 2. youth share decreases -5% 0.0% 0.0% -5.0% -5.0%3. working age share decreases -3% 3. working age share decreases -10% -3.0% -3.0% -10.0% -10.0%4. old age pensioners share increases 3% 4. old age pensioners share increases 15% 3.0% 3.0% 15.0% 15.0%

The city is changing fast towards a high-tech service-based one:

The city is changing slowly towards a high-tech service-based one:

1. Sector 3 employment share increases 20% 1. Sector 3 employment share increases 5% 20.0% 20.0% 5.0% 5.0%2. teleworking share equal to 50% 2. teleworking share equal to 15% 50.0% 50.0% 15.0% 15.0%3. Mobility rates increase 25% 3. Mobility rates increase 5% 25.0% 25.0% 5.0% 5.0%4. Goods vehicle transport adding to motorised private transport 15%

4. Goods vehicle transport adding to motorised private transport 25% 15.0% 15.0% 25.0% 25.0%

The city is moving fast towards improving transport efficiency:

The city is moving slowly towards improving transport efficiency:

1. passenger car occupancy rate increases 5% 1. passenger car occupancy rate decreases -1% 5.0% -1% 5% -1%2. passenger urban public transport share increases 15%

2. passenger urban public transport share variation of 0% 15.0% 0% 15% 0%

3. complete knowledge on traffic;network link capacity increase of 10%

3. partial and increasing knowledge on traffic;network link capacity increase of 5%

4. increases in penetration rate of HEV 13%, EV 7% and FCEV 7%.

4. increases in penetration rate of: HEV 7%, EV 4% and FCEV 3%.

The city is densifying and mixing land uses:The city is sprawling and separating land uses:

1. The average trip length decreases by 20% 1. The average trip length increases by 20% -20% 20% -20% 20%

UPPER END LOWER END

36

City Specific Input DataCity Specific Input Data

original trip matrix

original distance matrix

parameter for the desired trip generation

• Population

• Mobility rates

parameter for the desired trip distribution

• Sensitivity factor

• Form factor

37

City Specific Input DataCity Specific Input Data

InputMatrix Files

Distance Matrix x:\sutra\wp12\city.ifwPublic Transport Matrix x:\sutra\wp12\cityPuT.fmaCar Matrix x:\sutra\wp12\cityPrT.fma

Population data 2000total 1 000 000

Age groups 0-17 65+sector 3/office sector 3/tele sector 2 unemployed

population shares 19% 30% 5% 20% 10% 16%mobility rates 3.1 3.2 2.5 3 3.1 2.5

Car occupancy rate 1.3

Transport means Ped/Bic Pub PriMode shares 30% 20% 50%

Land Use : distance changesSensitivity Factor [a] [10 ... 0 ... -1] -1.00 8.00 -1.00 8.00Form Factor [b] [0.1 ... 1 ... 4] 0.10 3.00 0.10 3.00

18-64

38

Land Use Density (scenario examples)Land Use Density (scenario examples)

0

50

100

150

200

250

0 5000 10000 15000

Distance to the Castle

La

nd

Us

e In

ten

sit

y

39

Scenario Example - Modified Land Use IntensityScenario Example - Modified Land Use Intensity

0

50

100

150

200

0 1000 2000 3000 4000 5000 6000 7000

Distance from the Centre

Lan

d U

se In

ten

sity

Base CS1 CS2 CS3 CS4

Polynomisch (Base) Polynomisch (CS1) Polynomisch (CS2) Polynomisch (CS3) Polynomisch (CS4)

40

OverviewOverview

Transport Modelling

• Background

• City Examples


• Methods

• Examples





41

Indicator Summary (1)Indicator Summary (1)

Definition of indicators in Deliverable D08/A: Sustainability Indicators

Processing of transport model output to determine totals

... for Private Transport

• vehicle-km

• vehicle-hrs

• additional vehicle-hrs due to congestion

• vehicle-hrs in traffic jams

... for Public Transport

• passenger-km

• passenger-hrs

• passenger-hrs in overcrowded vehicles

... disaggregated by transport systems

42

Selection of a primary demand segment

• ... is required for the calculation of cold

flows

• ... has to be defined before reading the

network description to select only the

required data


43

Reading of link data

• journey times for private transport

• speeds for private transport

• disaggregated by link and transport

system

Aggregation to network level indicators

• vehicle-km, vehicle-hr

• additional vehicle-hr due to congestion

• vehicle-hr in jam

• passenger-km, passenger-hr

• passenger-hr in overcrowded vehicles


44

Indicator Summary : City ComparisonIndicator Summary : City ComparisonPassenger-hr

0

100

200

300

CS1 CS2 CS3 CS4

Passenger-km

0

100

200

300

CS1 CS2 CS3 CS4

Vehicle-hr

0

100

200

300

CS1 CS2 CS3 CS4

Vehicle-km

0

100

200

300

CS1 CS2 CS3 CS4

45

OverviewOverview

Transport Modelling

• Background

• City Examples


• Methods

• Examples





46

Cold Flows (1)Cold Flows (1)

„Cold Flows“ = Flows of vehicles operated under cold engine conditions

5 Pollutants

• CO2

• CO

• HC

• NOx

• FC

3 vehicle categories

• vehicles driven by petrol engine with catalyst

• vehicles driven by petrol engine without catalyst

• vehicles driven by Diesel engine

47


Each trip starts with a cold engine

Three Vehicle Categories in the Primary Demand Segment only (due to computation time)

All other Private Transport demand segments: driven by Diesel engines

Default values for vehicle shares in the Primary Demand Segment can be modified

48


Shares of vehicles in a link producing CO2

from a petrol engine

with catalyst under

cold conditions

49PTV Planung Transport Verkehr AG D-76131 Karlsruhe www.ptv.de

Thank you!Thank you!

[email protected]@ptv.dewww.ptv.dewww.ptv.de

Documents

Modelling Sustainable Urban Transport