Preliminary Report
SUTRA : Sustainable Urban Transportation
for the City of Tomorrow
WP 03: Multi-modal Transportation Modelling
D03.1 Implementation Report
Second Draft
Karlsruhe, January 2002
PTV
Planung
Transport
Verkehr AG
Preliminary Report
SUTRA : Sustainable Urban Transportation
for the City of Tomorrow
WP 03: Multi-modal Transportation Modelling
D03.1 Implementation Report
Second Draft
Prepared for:
Commission of the European Communities
Research Directorate-General
Prepared by:
PTV Planung Transport Verkehr AG
Author:
Josef Janko
Karlsruhe, January 2002
PTV
Planung
Transport
Verkehr AG
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Executive Summary
Transportation problems are among the most pressing strategic development
problems in many cities, often a major constraint for long-term urban development
in general. In SUTRA these problems are addressed with a consistent and
comprehensive approach and planning methodology that helps to design strategies
for sustainable cities. This includes an integration of socio-economic, environmental
and technological concepts to improve forecasting, assessment and strategic policy
level decision support. It uses traffic equilibrium modelling to evaluate alternative
transportation policies, including multi-modal systems and their relations to land
use, technological development, socio-economic development, and spatial and
structural urban development in general.
To evaluate scenarios for sustainable urban transportation an existing transport
model software will be enabled to deal with multi-modal aspects like Park+Ride
traffic. Special attention will be given to the prioritisation of high-occupancy
vehicles and road user charging, but any other scheme of sustainable transportation may be considered, if it is of relevance for one of the demonstration sites in
the project.
As a number of decision support indicators will be estimated from dedicated models
(for emissions, air quality, public health, economic and energy system analysis)
relying on the output of the transport model, it has to be guaranteed, that their
relevant input data are made available. Specifications and interface requirements
have been defined between the developers of the models.
Using the modelling software local transport models will be developed for the seven
participating cities and applied to common and individual scenarios to reveal the
influences and the consequences of transport schemes to socio-economic and
environmental aspects of urban living conditions.
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Contents
Executive Summary ................................................................................. 3
Contents.................................................................................................... 4
1
2
3
4
Introduction ..................................................................................... 6
1.1
Objectives ................................................................................ 6
1.2
Description of work ................................................................... 6
1.3
Deliverables ............................................................................. 7
1.4
Milestones and expected results .............................................. 7
Transport Model: Background and Methods ................................. 8
2.1
Structure of Transport Models .................................................. 8
2.2
Modelling of Traffic Demand ................................................... 10
2.3
Modelling of Traffic Impacts.................................................... 10
Modelling of Sustainable Transport Modes ................................. 12
3.1
Park+Ride .............................................................................. 12
3.2
High-Occupancy Vehicles ...................................................... 15
3.3
Road User Charging............................................................... 16
3.4
Other Strategies of Sustainable Urban Transport ................... 17
Model Interface .............................................................................. 18
4.1
Input Data .............................................................................. 19
4.1.1 Demand data .......................................................................... 19
4.1.2 Supply data............................................................................. 19
4.2
Output data ............................................................................ 20
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4.2.1 Emission model interface ........................................................ 21
4.2.2 Energy model interface ........................................................... 21
4.2.3 Public health model interface .................................................. 22
4.2.4 Environmental impacts model interface .................................. 22
4.2.5 Economy impacts model interface .......................................... 22
4.3
Data Exchange....................................................................... 22
5
Conclusions and Outlook ............................................................. 23
6
References ..................................................................................... 25
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1
Introduction
1.1
Objectives
Workpackage 3 of SUTRA deals with multi-modal transportation modelling. The
objectives are:
 To adapt an existing traffic equilibrium simulation model for multi-modal traffic
including private and public mass transport systems, high-occupancy
vehicles, and other trip-reducing strategies, based on the requirements and
constraints analysis undertaken in workpackage 1.
 To develop the interfaces of the traffic simulation model to the related
analytical components: emission model and near-field street canyon
modelling (workpackage 4), air quality modelling (workpackage 5), general
environmental impact assessment (workpackage 6), energy analysis
(workpackage 7), economic assessment (workpackage 8), and public health
impact assessment (workpackage 9).
1.2
Description of work
The workpackage will enable the modelling of inter-modal trip chains, park+ride
traffic (including optimal choice of locations for mode interchanges), road user
charging, and prioritisation of high-occupancy vehicles (HOV), in terms of their
potential to lower the economic and environmental costs of mobility. Workpackage
3 will build on a widely used transport planning software that uses a modeintegrated network and demand model. For the full range of policy options the
model will be extended with methods for inter-modal routing, Park+Ride, and HOV
prioritisation. This will include public transport features where necessary. Interfaces
will help transfer the results of the transport model to the economic and
environmental models of the other project partners. This kind of model chaining has
already been successfully demonstrated in an ESPRIT project where this model
also played the role of the transport model in a transportation/environment context.
While the workpackage is designed for an initial 12 months period, it is expected
that modifications of the model will be an ongoing, though low-level activity
throughout the whole duration of the project based on feedback provided by the city
partners.
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1.3
Deliverables
The work undertaken in WP03 will be documented in three deliverables:
 D03.1 Multi-modal transportation modelling (Implementation Report)
 D03.2 Transportation Model prototype
 D03.3 Transportation model: User Manual and example test data sets
1.4
Milestones and expected results
This workpackage will contribute to Milestone 2, which is due at PM 12. It is a
description of the availability of the first model versions for the individual application
sites.
As final result of workpackage 3 it is expected, that the methods developed in
SUTRA are available as normal features in the transport modelling software
package VISUM.
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2
Transport Model: Background and Methods
2.1
Structure of Transport Models
Transport models consist in general of two main components (Figure 2.1):

the data base as numerical input

the software with the methods and algorithms of the transport planning process
Transport Model
Data base
Structural data
Behavioural data
Network data
Figure 2.1 :
Software
Demand model
Assignment model
Transport Model Components
The design and application of a transport model is determined by the
interdependency between algorithms and methods on one hand and the availability
of data on the other hand.
The application of the transport planning algorithms is generally structured into four
separate processes:
 Trip generation
 Trip distribution
 Modal split
 Trip assignment
Figure 2.2 shows the interactions between these four elements. The first three
elements, trip generation, trip distribution, and model split define the demand
model. The fourth element is the supply side defined by assignment of trips to the
public transport and highway networks. A key feature of the model is that demand
is inextricably linked to supply in an iterative process in which the performance of
the transport networks resulting from the „loading“ of demand in the assignments is
cycled back into further runs of modal choice, distribution and possibly generation.
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Demand Model
Structural and Behavioural data
Population, behavioural
homogenous person groups
Activity chains
Structural data,
attractiveness of zones
Trip Generation
(Activity Model)
Activity related
impedance matrices
Trip Distribution
(Trip Chains)
Service Quality
Mode Choice
(LOGIT Model)
Mode attribute
matrices
Matrix Mode 1
e.g. passenger
cars
Trip costs PrT
Trip costs PuT
Matrix Mode 2
e.g. road freight
transport
Matrix Mode n
e.g. public
transport
Traffic Assignment Private Transport
Traffic Assignment Public Transport
Network description
Assignment Model
Figure 2.2 :
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Transport Model Interactions
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2.2
Modelling of Traffic Demand
There are two different categories of demand models, analytical and synthetic
models. As their main difference can be noted, that analytical models describe the
trip generation, while synthetic models try to explain demand:
 Analytical models use structural data like numbers of inhabitants, employees,
workplaces, schoolplaces and generation rates to estimate origins and
destinations of trips in zones for a particular time period.
 Synthetic models use beside structural data also behavioural data to model
activity chains of so-called behaviourally homogenous person groups and to
derive from these the origins and destinations of trips. As in synthetic models
trip chains for a whole day are generated the trip distribution and the mode
choice have to be linked to the trip generation [2].
2.3
Modelling of Traffic Impacts
Transport systems have impacts in different forms on system operators, on
individual users, and on the general public.
The road network is usually operated by the state, federal states or municipalities
and increasingly by private investors. These "operators" of the road network have
to decide on investments for the construction and maintenance of road
infrastructure. The Public transport operators are transport companies or transport
associations. To offer a public transport service, Public transport operators develop
line networks and timetables from which the user can then choose connections.
Users of infrastructure for private transport are mostly car drivers and their
passengers, but also non-motorised travellers such as cyclists and pedestrians.
Users of public transport are public transport passengers. Important indicators for
evaluating transport systems are journey times and travelling expenses. To
evaluate a public transport system, additional indicators such as availability and
frequency of service, number and quality of transfers, and seat availability have to
be considered.
Beside these direct individual aspects also impacts of travel demand on the general
public have to be considered:
 Pollution. Combustion engines and power plants produce emissions with
consequences to health and the environment.
 Energy consumption. Transport consumes energy
 Costs of accidents. Traffic accidents cause injuries and fatal casualties with
damage to the economy.
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Impacts of traffic can be derived from the output of the assignment model. The
principles and methods of traffic assignment have been developed over a long time
[3, 5]. State of the art are different variations of equilibrium methods. This means,
that the chosen routes for all the travels in a transport network meet the minimum of
an objective function, which cannot be improved by shifting trips between routes.
Examples for objective functions can be the total amount of time spent in the
network or minimum journey times for all the travels in the network. Assignment
methods allow basically estimates of transport related indicators like volumes or
journey times on roads. The above mentioned impacts go beyond the scope of
transport models and therefore have to be evaluated with separate models.
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3
Modelling of Sustainable Transport Modes
In this project work is focussed on sustainable transport modes. This particularly
deals with all forms of public transport and means of restricting private transport.
Special interest is directed to

Modelling of Park+Ride

Modelling of facilities for high-occupancy vehicles

Modelling of road user charging schemes
The following sections describe the way, in which the transport model can be
enabled to evaluate the effects of such measures.
3.1
Park+Ride
Figure 3.1 :
Example for a P+R system
Park+Ride describes travels in conurbations from the outskirts to the centre, which
are undertaken partly by private and partly by public transport. The first part in the
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less dense outer areas with usually less density of public transport is made by car.
The car is parked at a station or a stop of a bus or rail service, from where the
second part of the journey is continued to the destination in the centre of the city. In
this way one tries to combine the advantages and to avoid the disadvantages of
both transport systems. Figure 3.1 shows an example for Park+Ride in an urban
area with the private transport part of travel in red and dedicated P+R services in
blue. To the potential clients four facilities are available, which are linked to the city
centre with bus lines.
Modelling of Park+Ride trips requires a sequence of steps. The approach can be
outlined as follows:
 Definition of P+R sites
 Determination of P+R demand
 Split of P+R demand into private transport and public transport legs
 Assignment of P+R trips as parts of the demand segments for private and
public transport
The general modelling assumption is, that P+R trips are genuine private transport
travels, which can be shifted to public transport as a consequence of an improved
service.
In the network model a P+R site has to be represented as a zone and a link. Zones
are necessary as elements, where travels within a transport system start and end.
A change between private and public transport therefore requires a zone. In the
P+R links of the model two properties have to included, the capacity of the P+R
facility and the costs for using P+R. The P+R links, like all the other links in the
network model, have a limited capacity, which is here defined by the number of
parking places and number of changes during a day. This allows to include
different trip purposes with different durations of stay (commuters, shoppers). Also
costs for parking and bus fares can be defined in the impedance of these P+R links.
In a first step the impedances for the public transport legs of P+R trips have to be
defined with the Assignment model. This requires a selection of potential
destination zones of P+R trips by the modeller. Parameters for this determination
would be the destinations and journey times of the available public transport
services for the existing or potential P+R sites.
The destination zones have to be connected to the P+R sites. A first assignment
run is made in which the numbers of clients using the P+R sites are estimated for
the whole modelling area in competition to private transport on the whole journey.
Figure 3.2 depicts the transport model representation of a P+R facility with the real
public transport service and the modelled connections to some of the zones in the
city centre.
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Figure 3.2 :
Transport model representation of a P+R facility
A second assignment however is necessary to assign these travellers to the public
transport leg and to provide their correct return trip. As it is not mandatory in the
trip assignment that for both directions of a journey the same connection of a zone
is used, it has to be guaranteed, that P+R customers start their private transport
return trip from the same place where they left their car. Therefore the P+R return
trips have to be removed from the original demand matrix for the private transport
segment and have to be entered as new relations between the P+R site and their
final destination. In this same model run the P+R clients have to be assigned to the
public transport services between the P+R facilities and the city centre.
In detail this requires the following steps:
1. Preparation of the network model
 Definition of the the P+R sites as links and zones
 Definition of the public transport services for these sites.
 Estimation of potential P+R destinations through determination of isochrones
for the relevant public transport services
 Definiton of the relevant connectors between the P+R sites and the
destinations
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2. First assignment to estimate the demand matrix for the P+R clients; validation
against reference data.
3. Modification of travel demand
 Select link analyses for the P+R site links generate the matrices of P+R trips
from the origins to the final destinations in the city centre (original P+R trips).
 These trips are removed from the private transport matrix.
 The P+R facilities are defined as destinations of the original P+R trips, and
these trips are added to the private transport demand.
 The P+R facilities are defined as origins of the original P+R trips, and these
trips are added to the public transport demand.
 The return trips have to be treated in the same way.
4. Second assignment with modified demand for the public and private transport
systems.
The whole procedure has to be calibrated in a possible iterative process, if the shift
from private to public transport is of a magnitude which has influence on the mode
choice decision: if the reduction of private traffic is so big, that congestion is
relieved, car usage might be encouraged again.
3.2
High-Occupancy Vehicles
The term „High-occupancy vehicle“ (HOV) can include buses, vanpools, carpools,
and other authorised vehicles. The focus of projects to promote HOV lies on
carpools; most facilities use a two person (2+) per vehicle carpool definition, but
some require three person (3+) carpools. Incentives for participating in a carpool
can be
 Availability of dedicated HOV lanes
 Parking spaces at convenient locations only for HOV
 Exemption from road user charging for HOV
In the transport model high-occupancy vehicles also are regarded as carpools.
They have to be defined as a transport system of their own. Dedicated HOV links
can be prepared in the network model which are blocked for other vehicles. Where
no particular HOV links are available, these vehicles join the other vehicles in
common links.
Demand for carpooling has to be determined in a demand model. These trips may
be defined in two categories:
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 The whole trip is done in a carpool. An estimated share of demand for private
travel has to be split from the private transport matrix and moved to an HOV
matrix.
 Carpool members start their trips separately, meet at an agreed place (e.g.
beginning of a HOV lane), and travel then together. In this case the treatment
of HOV demand is similar to the processing of P+R trips. The demand has to
be removed from the private transport matrices first and then added back
again for the private transport legs of the trips, while the HOV legs are part of
the HOV demand.
3.3
Road User Charging
Road user charging has been applied on motorways in a number of countries for a
long time. In cities or dense urban areas however road user charging has been not
been applied widely. Prominent exception is the city of Singapore, where a
charging scheme is effective now since 30 years. Few cities in Europe have
installed similar projects as case studies during the last years. Genoa, one of the
SUTRA city partners, prepares an area wide scheme for its centre.
Conventional methods to model road user charges apply a constant value of time
which can be included in a generalised cost function of a monocriterion assignment
method [4].
TRIBUT is a bicriterion traffic assignment method which equally considers travel
time and cost. The trip choice between different paths is modeled by defining the
value of time as a random variable with a distribution of the log-normal type, thus
considering that each trip has a specific willingness to pay toll for travel time
reduction. This approach offers a significantly better price elasticity than
monocriterion methods. Its most prominent features are randomly distributed
values of time, the principles of path search and path choice. Furthermore it
presents different aspects of the application in practice, in particular the definition of
different demand classes, the modelling of linear or non-linear pricing schemes and
the value of time estimation. A detailed description of the TRIBUT method can be
found in [1].
Within the transport model a wide range of scenarios can be tested:
 Charging for entering areas (city centre)
 Charging for use of single parts of the road network (bridges, motorways)
 Charging of particular users (goods vehicles, passenger cars with only one
person in it)
 Charging at peak hours
 Combinations of all these
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3.4
Other Strategies of Sustainable Urban Transport
Sustainable urban transport is not restricted to the application of policies described
above. Other strategies to reduce negative consequences of motorised private
transport can be modelled without any major limitations, e.g.
 Modifications of the road network including area wide traffic calming as well
as removal of local bottlenecks
 Traffic management schemes (including transport telematics)
 Increased use of environmentally friendly transport modes
 Introduction of new public transport systems
 Reductions of emissions and energy consumption caused by technological
development
 Travel demand reductions due to changes in land use
It must be stated however, that the quality of the results of the transport model
depends to a great extent on the precision of the input data, in particular the data
for travel demand.
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4
Model Interface
In the SUTRA context the transport model is the first in the chain of models to
evaluate city development scenarios. Figure 4.1 gives an overview of the data
exchange between the transport model and the other subsequent models.
Public Health
Environment
Volumes,
link lengths,
journey times
Emission
Volumes, speeds,
trip lengths,
number of cold
starts, ratio hot/
cold driving
Volumes,
trip lengths,
journey
times
Volumes,
trip lengths,
journey
times
Transport
Model
Demand:
OD-matrices
for different
segments
Economy
Volumes,
speeds
Energy
Supply:
networks for
different
modes
City Infrastructure
Figure 4.1 :
SUTRA Transport Model Interfaces
Expanding the point of view beyond SUTRA, a transport model also needs an input
data base for demand and supply modelling. It is assumed, that these data are
available from external sources and can be imported into the transport model.
The transport model software applied in SUTRA, VISUM, is part of a comprehensive software package for traffic planning and traffic engineering. It is running
under a Windows environment.
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4.1
Input Data
The transportation model determines the impacts of existing or planned transport
supply which can encompass both the private transport network and the public
transport network. 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 sections
give an overview of these data. A complete description can be found in the
software documentation.
4.1.1
Demand data
Transport demand data have to be generated separately from the traffic assignment
model. The most important method is the application of a dedicated traffic demand
model software to produce the required data. Demand data must be made
available in a matrix form describing travel demand, i.e. the number of trips
 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
In general, demand segments represent the different modes of transport, e.g.
passenger cars, bus, light or heavy rail.
4.1.2
Supply data
Transport supply data are represented in a network model. The integrated network
model distinguishes between "private transport" and "public transport" modes. By
combining different means of transportation and modes, the planner can define
different transport systems. Basically the network model includes the following
network objects which can be modified interactively:
 Transport systems, modes, demand segments
 Nodes: junctions with name, coordinates, type, and a public-transport-stopflag.
 Zones: name, type, centroid and boundary coordinates
 Global Zones: name, type, centroid and boundary coordinates
 Assignment of zones to global zones
 Link types: principal capacities and speeds, permission of transport systems
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 Links: nodes at the beginning and the end, capacities and speeds, permission
of transport systems, one-way-flag
 Major flows to reflect priority rules at junctions
 Connectors between zones and nodes: type, length, journey time, permitted
modes
 Turning standards:node type, turning relation, initial waiting time, capacity
 Turning relations: permitted transport systems, initial turning penalty, capacity.
 Public transport operators
 Public transport vehicle types: name, code, transport system, number of
seats, total capacity, number of vehicles, costs/km, costs/hr
 Lines: line name, variant, direction
 Sublines: line name, variant, direction, transport system, operator
 Lineroutes: line name, variant, direction, node, boarding and exiting flags,
arrival and departure times
 Timetables: line name, variant, direction, first departure, headway, last
departure, vehicle type
 Census points in links for evaluation of traffic counts
 Additional cost parameters for public transport links, stops, and operators
 user-defined areas (representing e.g. administrative units), for which
indicators can be determined.
Only a small part of these data is necessary to generate a functional network model
which is able to produce results: transport systems, nodes, zones, links,
connectors. Additional data help to manage the work efficiently and to achieve
more precise results.
Data can be retrieved from different sources and entered in different ways. The
principal method is the input of data through the GUI of VISUM. For very large
networks however it is easier to import the links and nodes from GIS databases.
Also public transport data may be imported from external databases of the
operators. Other objects like connectors can be generated algorithmically.
4.2
Output data
The basic output of assignment models is produced in the form of maps with
referenced indicators, e.g. link volumes, local speeds or chosen routes. In SUTRA
this however is a task of only minor importance. As a number of other models rely
on the output of the transport model it has to be ensured, that the necessary data
are available with the required precision.
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4.2.1
Emission model interface
Estimations of emission productions are carried out in the framework of this project
with TREM (Transport Emission Model for Line Sources). A description of this
model can be found in Deliverable D04.1 [6]. It requires as input from the transport
model the numbers of vehicles in each link of the network, and the information,
whether these vehicles are driven under hot or cold engine conditions. These
conditions are defined as so-called cold distances individually for different pollutants
and vehicle categories.
An interface has been developed, which outputs the required data in a table format
to an Excel spreadsheet. An example with three vehicle categories and five
pollutants is given below.
Percentage of Cold
Percentage of Cold
Engine Petrol Vehicles Engine Diesel Vehicles
for
for
LinkNr
25
109
110
114
115
116
Flow
CO2 CO
HC NOx
FC
CO2 CO
4891
43
33
14
33
30
4806
90
90
90
90
90
6726
90
90
57
90
90
HC
NOx
FC
Percentage of Cold
Engine Diesel Cat
Vehicles for
CO2 CO
HC
NOx
FC
40
90
90
90
90
25
50
90
90
90
9069
56
47
11
47
45
10239
59
47
36
47
50
2683
43
11
7
11
10
10239
76
49
37
49
50
2683
43
9
7
9
10
8526
67
49
30
49
50
8876
87
60
50
60
60
5
5
35
50
10
90
8897
86
59
49
59
60
10
10
30
40
10
90
7929
66
50
34
50
50
9
10
5
5
15
20
50
10
10
5
5
15
20
60
40
10
10
60
40
70
5
70
Treatment of additional vehicle categories and pollutants is possible.
4.2.2
Energy model interface
In the MARKAL-LITE specification as input is required the total distance covered by
the vehicles of the different transport systems over the period of one year.
The transport model produces link volumes as constant values over the given
simulation period. The link volumes can be disaggregated into the different
demand segments. The required data can be calculated by first summing up the
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figures for the whole network and then expanding them to the desired time period.
These expansion factors have to be provided by the city partners.
4.2.3
Public health model interface
No written specification is given for the public health model input data requirements.
It is assumed that the required data can be derived from the parameters link
volumes, link lengths, and journey times.
4.2.4
Environmental impacts model interface
No specification is available for the environmental impacts model input data
requirements.
It is assumed that the required data (if any) can be derived from the parameters link
volumes, link lengths, and journey times.
4.2.5
Economy impacts model interface
No specification is available for the economy impacts model input data
requirements.
It is assumed that the required data (if any) can be derived from the parameters link
volumes, link lengths, and journey times.
4.3
Data Exchange
Processing of the results of traffic assignments usually cannot be done manually,
as one has to deal with parameters and indicators from thousands of links. Within
the Windows environment there are basically two methods available for a
communication between VISUM and other programs:
 Clipboard: VISUM can generate lists of indicators and assignment results.
The user can select the items which should be included in such a list. This list
can be stored as an ASCII file or copied into the clipboard and then imported
to other applications, e.g. spreadsheets or data base applications.
 COM interface: this feature allows the access to the data structures of the
transport model software with Visual Basic applications. The user can control
the application of the transport model software and extract and process data
according to his requirements.
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5
Conclusions and Outlook
Discussions with the city partners in the SUTRA project have shown, that the
transport model software is able to simulate the possible scenarios and to produce
the data required from the other models. Work on multi-modal transportation
modelling will continue in SUTRA with establishing demonstrators for the features
described in this deliverable in various test sites. These activities will be
documented in the deliverables
 D03.2 Transportation Model Prototype
 D03.3 Transportation Model: User Manual and Example Test Data Sets
Features of Test Sites
Buenos Aires
Gdansk
Area-wide road user charging, Public transport fare strategies
Geneva
Genoa
Road user charging in the city centre
Lisbon
Tel Aviv
Parking policies, Road user charging (Congestion Pricing)
Thessaloniki
Public Transport Improvements:
 Underwater vehicle tunnel which links the west entrance of the city with the
center and extension of it to the East
 Basic subway line in the center of the city
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 Tramway in the center of the city, east and west Thessaloniki.
 Suburban railway linking the industrial area of Thessaloniki with the west part
of the city.
Private transport Improvements:
 Upgrading of the major arteries
 Upgrading of the other types of roads (ring roads and suburban roads)
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6
References
[1]
Barbier-Saint-Hilaire, F., M. Friedrich, I. Hofsäß, W. Scherr : TRIBUT – a
Bicriterion Approach for Equilibrium Assignment. Traffic Engineering &
Control, April 2000.
[2]
Fellendorf, M., T. Haupt, U. Heidl, W. Scherr: ptv vision: Activity-Based
Demand Forecasting in Daily Practice. In: Activity-Based Approaches To
Travel Analysis, Elsevier, Oxford 1997, pp 55-72.
[3]
Lohse, D. : Traffic Assignment. In Schnabel, W., D. Lohse (ed.), Grundlagen
der Straßenverkehrstechnik und der Verkehrsplanung, Berlin (1997), vol. 2,
pp 317-325.
[4]
U.S. Department of Commerce, Bureau of Public Roads (ed.): Traffic
Assignment Manual. Washington, D.C. (1964)
[5]
Wardrop, J. G. : Some Theoretical Aspects of Road Traffic Research.
Proceedings of the Institute of Civil Engineers, London (1952), pp 335 ff
[6]
anon., Department of Environment and Planning, University of Aveiro [ed.] :
TREM – Transport Emission Model for Line Sources – Methodology. SUTRA
Deliverable D04.1
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