Microscopic modeling of passenger transport demand based on time use diaries

In several projects currently at the Institute for
Transport Research (IVF) in the German Aerospace
Center (DLR) in Helmholtz
Furthermore, these classical approaches are based
Community are processed, it is z. B. the aim of
estimating measures in scenarios for sustainable
transport development up to the year 2030.
Macroscopic path-based models, which are currently
used in traffic planning practice, are not sufficient to
determine the necessary traffic demand. They do
not offer the possibility of reliably estimating changes
in individual daily routines (cf. Federal Ministry for
Transport, Innovation and Technology 2005: 99). In
particular, changes in the travel behavior of people,
interaction within households and changes outside
the transport system (e.g. changes in shop opening
times) cannot be mapped in this type of model (cf.
Widmer, Axhausen 2001: 6).
mostly based on the so-called four-stage algorithm
of traffic modeling (see Figure 1).
1. Introduction
Fig. 1: The traditional four-stage traffic prediction algorithm (Source: Hilty et al., 1987, p. 67)
Microscopic modeling of passenger transport demand based on time use diaries
The growing volume of traffic and the resulting traffic
problems increasingly lead to the question of which
concepts can be used to meet future traffic demand.
Christian Varschen, Peter Wagner
Traffic models are important tools in the context of
traffic planning and traffic management.
A traffic modeling should depict the traffic. However,
not only the current situation should be presented.
Rather, the traffic must be analyzed to the extent
that forecasts are possible and potential influencing
factors that affect this development can be identified
(cf. Kutter 2003: 9). There are various approaches
to achieve this goal.
Stadt Region Land – Issue 81 – AMUS 2006 conference proceedings 63
Microscopic modeling of passenger traffic
Christian Varschen
question based on time use diaries
Peter Wagner
Machine Translated by Google

each person in the synthetic population determines
which activities they pursue in the period under
consideration. The data from the time budget
surveys of the Federal Statistical Office are used for
this (further information from the Federal Statistical
Office 2003). This is a representative survey in
cooperation with the Ministry of Family Affairs. The
previous version of the model worked with the data
from the survey
2.1 Procedure and data basis of TAPAS
2. The TAPAS model
activities taking into account the current spatial
position and the spatial attributes of potential
opportunities, the current status of the transport
network, institutional conditions and the characteristics
of individuals and households (cf. Arentze et al.
1997: II-J/3).
For the projects at the DLR-IVF, a model must on
the one hand be able to depict future traffic, on the
other hand it must be capable of scenarios or
sensitive to measures, ie it must be able to react to
the bundle of measures that used as a basis in the
various scenarios. The TAPAS ( Travel and Activity
Patterns Simulation) transport demand model
developed at DLR-IVF is used for this purpose. It
has a modular structure and forms the traffic in a
defined space - e.g. B. a city or a district - from. So
far
it was used for the city of Cologne. A more detailed
description of the model and comments on the
Cologne application can be found in the literature
(Hertkorn 2004).
This model stands between the categories presented
because – as will be explained – observed behavior
in relation to the activities is used. But since z. For
example, if people's search area is restricted, it is
also assumed here that people are not fully informed.
In the following, an overview of how TAPAS works
and the data required for it will be given before a
procedure for adding new activity categories is
presented in a second step.
In addition to forecasting future traffic, the application
of TAPAS provides valuable insights into the
responsiveness of certain population groups to
innovative drives or vehicles.
For this model, it is necessary to include a range of
data in order to enable a realistic depiction of
individuals.
Vehicles and fuels or mobility concepts are expected.
This includes spatial and structural data, time use
data and information about the use of transport. In
the schematic representation of the model (Figure
2), the originally flowing data are marked with light
gray oval boxes.
The activity-based traffic demand models can be
kate
64
gorize. One possibility is differentiation according to
the action models used to depict the decision-making
behavior of road users (cf. Federal Ministry for
Transport, Innovation and Technology 2005: 99f). A
distinction must then be made between utility
maximization models that are based on the homo
oe conomicus and models that back the decisions
with certain heuristics in order to enable people to
find suboptimal solutions according to their level of
information (cf. Timmermans 2001, 28ff.). Whereas
with the first models mostly an activity
Stadt Region Land – Issue 81 – AMUS 2006 conference proceedings
First, a synthetic population for the study area is
generated in the model.
In order to compensate for these weaknesses and
deficits, a microscopic activity-based approach is
pursued in the projects mentioned for the small-
scale consideration. Activity-based models
correspond to the current state of research (cf.
Federal Ministry for Transport, Innovation and
Technology 2005: 100). The basic idea of this
approach is that the chronological sequence of
journeys is the result of the sequence of people's
activities (Hertkorn 2004: 2). This raises the central
questions of what activities people do, when, where,
for how long and with whom, as well as what means
of transport, if one is used. Added to this is the Pla
For this purpose, on the basis of existing population
data, it is determined for each traffic cell how many
people live there, how many households they are
distributed over and what socio-demographic
characteristics they have. After that, for
set is assigned from observed sets, in the second
category the activities and locomotion are sequentially
expanded into activity programs.

Which opportunity is specifically chosen to carry out
an activity is determined in the model based on
users who logged three days' activities, but which no
longer had to be consecutive. Furthermore, in
1991/1992 231 assets were
collected, but in 2001/2002 every ten minutes. In
the second study, the Per
On the basis of the ziodemographic and
socioeconomic characteristics that were collected in
the time budget study, a probability distribution is
obtained with which a group of people chooses a
specific activity pattern. Concrete activity programs
are then assigned to the agents of the synthetic
population according to this probability distribution.
activity codes, in the study from 2001/2002, however,
281. In the old study, va
from 1991/1992, in which 7,200 households took
part. Each person in the household has
Every five minutes, she notes which activities she
has carried out on two consecutive days.
Activities relating to unpaid work are differentiated in
great detail, so the focus of the new study is in the
area of further education and professional
qualifications; the activities are broken down
accordingly. The activity chains module is therefore
being revised with regard to the calculation of
transport demand for the base year.
Using a combination of sequence and cluster
analyses, this data is classified into groups of similar
activity patterns. About an additional clustering of
people about their so
Fig. 2: Flowchart of the TAPAS model (source: own illustration)
After determining the distribution of people and
Stadt Region Land – Issue 81 – AMUS 2006 conference proceedings 65
Households on the traffic cells and on the Mon
The data of the second federal German time budget
study has been available since autumn 2005, in
which 12,600 people took part between 2001 and 2002
dell activity chains to be simulated, it is determined
for each activity of a person at which location it takes
place. To do this, the model requires corresponding
data on the spatial location of potential opportunities
(places where activities can be pursued) and their
capacity. In addition, a travel time matrix for the area
under consideration is required in this module.
5,400 households participated. This led to changes
in the design, which were made at the suggestion of
EUROSTAT in order to enable a comparison with
time budget studies in other European countries. So
in 1991/1992 the activities were held every five
minutes

66
After the locations and means of transport have
been selected, the travel times required are assigned
with the help of the travel time matrix. This can lead
to inconsistencies in the daily routine.
For example, a planned visit to the theater after work
could result in increased travel times in the evening
Fig. 3: Successive choice of mode of transport in TAPAS
(Quelle: Hertkorn, 2004, S.89)
commuter traffic are endangered since the start of
an event is relatively fixed. In order to ensure a
certain variability in daily routines, the episodes of a
daily schedule are weighted. This weight represents
the cost of shifting the start and end times of an
activity within a daily schedule. This makes it possible
to adjust episodes and travel times to one another
using a balancing procedure. If the balance is
unsuccessful, either new destinations and modes of
transport are chosen until a balance is possible, or if
a maximum number of new attempts is reached, a
new scheme is used.
The means of transport themselves are selected
using a CHAID decision tree (Chi-Squared Automatic
Interaction Detection), which is based on a subset
of the data from the Germany-wide survey “Mobility
in Germany 2002” (cf. infas, DIW 2004, infas, DIW
2003). .
based on the model of intervening opportunities ,
which assumes that a certain alternative will be
rejected with a certain probability. The possible
opportunities for this are currently sorted according
to their travel time and assigned an attractiveness
weight based on ancillary conditions (e.g. occupancy).
As a result, potential means of transport must already
be considered at this point. In order to arrive at
meaningful daily plans, the episodes of a daily
routine are also hierarchized. Reference point and
thus episodes that have the highest hierarchical level
are those that take place at home (see Figure 3).
The locations for episodes of the next hierarchical
level (e.g. work) are then selected, as well as the
corresponding means of transport to get there.
The result of this module of destination and mode
of transport selection are consistent daily plans of
the synthetic population. The source-destination
relationships for each person and each activity that
causes a journey are therefore in these daily plans.
They form the transfer value for a traffic flow
simulation to be carried out externally. With the help
of this simulation, corresponding indicators can then
be used, e.g. B. distances or edge loads are
determined. (FEEDBACK)
This process is repeated until a person's daily
schedule is completely full. This procedure has two
decisive advantages: On the one hand, an individual
means of transport is used for a complete tour. Since
the number of cars in the synthetic population is
related to the household, account is also taken of
the fact that several people cannot use a car for
different purposes at the same time.

Corresponding adjustments now make sense in two
ways. New behaviors due to shifts in the scope and
duration of activities can be derived relatively well.
For this purpose, changes in the use of time in the
last 10 years are analyzed using a comparative data
set from the 1991/1992 time use survey of the
Federal Statistical Office and then interpolated into
the future.
technologies happened: With the ubiquitous
on the online behavior of people, whereby the four
parameters can be determined
It is therefore necessary to extend this module in
order to be able to simulate future and other
behaviors. Several approaches are possible for this.
However, this assumption for the total population
would be unrealistic. Thus, with a high probability z.
B. the traffic behavior of older people
Usability of hardware and software have for
2.3 Conclusion
The overall behavior of the population will change in
the future due to changes in the population structure.
Assuming that time use stays the same in certain
population groups, no adjustment would be
necessary, since the individual in the model – as
mentioned above – selects activity patterns using a
probability matrix. If there were a change in the
structure of the population, a change would
automatically take place via this matrix
part of the employment activities during travel has
increased significantly in importance.
The traffic demand model presented was developed
to depict an actual state and to take appropriate
measures, e.g. g. in the infrastructure sector. The
results achieved here were very satisfactory
compared to other simulations and surveys (Hertkorn
2004, 110ff.). It therefore seems generally appropriate
to calculate future traffic demand. To do this,
however, all TAPAS modules must be revised and
adapted to the conditions expected in 2030. For
example, demographic aging must be taken into
account in the synthetic population, and when
assigning activity chains, the assumed change in
time budgets – as described – must be extrapolated
into the future, or new activity categories must be
added.
People are changing because those who are
currently younger are more likely to have a driver's
license than those who are currently older. However,
this also makes it more likely that this population
group will use cars more in the future. However,
completely new activity patterns can also develop,
as has been the case in recent years due to the
ordinance
In this model, the development for the actual state
is based on observed behavior.
As described above, this model uses an activity-
based approach based on the analysis of time-use
data.
In order to be able to map completely new activity
patterns, an additional extension is necessary. To
do this, the four parameters that describe each
activity in TAPAS must be estimated: proportion and
extent of use of the activity (new to TAPAS) and the
variability of the activity over time in terms of start
time and duration.
i.e. Each module is backed with representative,
current data to ensure the most realistic possible
Therefore, the activity patterns available are limited
to those contained in the time-use data, which is a
severe constraint for forecasting.
The corresponding data required for this is generated
from surveys. For example, the activity "Use of the
Internet" can be generated from your own surveys.
This activity can only be partially determined from
the time use survey. Activities relating to qualification
and online shopping are well presented there. It is
not possible to determine the extent or proportion of
information obtained via the Internet, since this was
then only included in generally designated computer
use activities. However, these do not necessarily
have to take place on the Internet. A survey was
now project-related
67
to. All parameters can be calculated directly from
this empirical (time use) data, the first two parameters
are mean values while the last two are derived from
the statistical one
2.2 Expansion of activity categories
Variation of the surveys result. In order to be able to
estimate all parameters, however, real time use data
are necessary, since otherwise the temporal
variability of the starting point could not be determined.
gen.
availability of information and communication

Modeling for traffic planning. Theoretical, empirical
and practical framework conditions. ECTL Working
Paper 21, Hamburg. http://www.vsl.tu-harburg.de/
vsl_2/Archiv/wp/wp21.pdf (07/2006)
infas, DIW (2004)
eitspapiere/1_EMVEM_bericht.pdf and
http://www.isv.tugraz.at/veroeffentlichungen/arb
68
change, or new patterns of activity emerge. You can
also use destination dialling
Widmer P.; Axhausen, K.W., (2001)
Possibilities exist, for example, with the activity patterns.
By a certain measure takes place a change between the
Mus
Mobility in Germany 2002 - Continuous survey of
traffic behavior. Project no.
Federal Ministry of Transport, Innovation and
Technology (2005) [ed.]
Timmermans, HJP (2001)
time use survey. Statistics from A to Z. http://
www.destatis.de/presse/deutsch/abisz/zei
questions. The close link with empirical data also
increases a high level of confidence
Data Needs, Data Collection and Data Quality
Requirements of Activity-Based Transport Models.
Presented at the International Confer ence on
Transport Survey Quality and Innova tion, 24-30 May
1997, Grainau, Germany. http://gulliver.trb.org/
publications/circulars/ec00
Hilty, LM ua (1998)
Cologne. http://elib.dlr.de/21014/01/fb_2004-
29_v2.pdf (07/2006)
Arentze, T.; Hofmann, F.; Kalfs, N.; Timmermans, H.
(1997)
In order to make the model scenario-capable and
measure-sensitive, it is necessary to include parameters
that calculate the influence of measures on traffic
behavior. dar
Federal Statistical Office (2003)
Nevertheless, the further development of the
model, the estimation of passenger transport demand
with special consideration of specific scientific and
political
On the other hand, the data requirements of the model
represent a limit of what is feasible
Hertkorn, G (2004)
reliability of forecasts.
Appropriate modifications are made so that people who
react to measures by choosing other goals for carrying
out their activities can be mapped. It is also necessary
to show the choice of means of transport and the degree
of motorization of the households.
Principles of Household Activity Scheduling Behavior.
In: Kutter, E., Timmermans, HJP, Jones, PM (eds.):
Expertise for the Mobiplan project, research working
paper F11, Institute for Urban Planning, RWTH
Aachen University. http://www.isb.rwth-aachen.de/
publikationen/F11-Expertisen_Mobiplan.pdf (07/2006)
Intelligent infrastructure. Final report EMVEM -
Evaluation methods of traffic telematics measures
Basic study. Graz University of Technology. http://
www.isv.tugraz.at/veroeffentlichungen/arb
70.0681/2001, Urban traffic research program of
the Federal Ministry of Transport, Building and
Housing. final report.
Kutter, E. (2003)
8/workshop_j.pdf (07/2006)
infas, DIW (2003)
Instruments for the ecological assessment and
design of traffic and logistics systems Final report of
the research project MOBILE. University of Hamburg
and FAW Ulm. http://mobile-www.informatik.uni-
hamburg.de/ MOBILE/Abschlussbericht/ Aufbau.html
(07/2006)
tbudgeterhebung.htm
The only limitation that this model currently has is the
small-scale representation. In the case of the complex
model system, this limitation is due on the one hand to
the computing power. A representation of Germany with
82 million individuals would currently be unrealistic.
literature
to create image. When adapting the model for calculating
future transport demand, forecasts – e.g. B. in relation
to population development - used.
Microscopic modeling of time-dependent traffic
demand and traffic flow patterns. German Aerospace
Center, Research Report 2004-29.
To ensure meaningful microscopic modelling, very small-
scale population and structural data would be necessary,
with structural data in particular posing a problem.
Activity-oriented passenger transport models
(preliminary study). Work report on traffic and spatial
planning, 70, Institute for traffic planning, transport
technology, road and railway construction (IVT), ETH
Zurich. https://www.ivt.ethz.ch/vpl/publications/
reports/index/edit/ab70.pdf (07/2006)
Mobility in Germany: Results report. Project no.
70.0736/2003, Federal Ministry of Transport, Building
and Housing.
keitspapiere/1_EMVEM_Anlagenband.pdf
(07/2006)

Dr. Peter Wagner
German Aerospace Center in the Helmholtz
Association (DLR)
Christian Varschen, MA
Rutherfordstr. 2, 12489 Berlin
Peter.Wagner@dlr.de
69
German Aerospace Center in the Helmholtz
Association (DLR)
Rutherfordstr. 2, 12489 Berlin
Christian.Varschen@dlr.de

70

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