Modelling Sustainable Urban Transport
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Transcript of 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
Sustainable Transport
• Methods
• Examples
Implementation of Project Features
• Modelling of City Structure Changes
• Indicator Determination
• Input for the Emission Model TREM
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
Sustainable Transport
• Methods
• Examples
Implementation of Project Features
• Modelling of City Structure Changes
• Indicator Determination
• Input for the Emission Model TREM
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)
19
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
Sustainable Transport
• Methods
• Examples
Implementation of Project Features
• Modelling of City Structure Changes
• Indicator Determination
• Input for the Emission Model TREM
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
Sustainable Transport
• Methods
• Examples
Implementation of Project Features
• Modelling of City Structure Changes
• Indicator Determination
• Input for the Emission Model TREM
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
Sustainable Transport
• Methods
• Examples
Implementation of Project Features
• Modelling of City Structure Changes
• Indicator Determination
• Input for the Emission Model TREM
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
Indicator Summary (2)Indicator Summary (2)
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
Indicator Summary (3)Indicator Summary (3)
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
Sustainable Transport
• Methods
• Examples
Implementation of Project Features
• Modelling of City Structure Changes
• Indicator Determination
• Input for the Emission Model TREM
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
Cold Flows (2)Cold Flows (2)
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
Cold Flows (3)Cold Flows (3)
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