Available online at www.sciencedirect.com
Available online at www.sciencedirect.com
ScienceDirect
ScienceDirect
Transportation
Research
Procedia 00 (2019) 000–000
Available
online
at www.sciencedirect.com
Transportation Research Procedia 00 (2019) 000–000
ScienceDirect
www.elsevier.com/locate/procedia
www.elsevier.com/locate/procedia
Transportation Research Procedia 40 (2019) 178–185
www.elsevier.com/locate/procedia
13th International Scientific Conference on Sustainable, Modern and Safe Transport
13th
(TRANSCOM
International 2019),
Scientific
High
Conference
Tatras, Novy
on Sustainable,
Smokovec –Modern
Grand Hotel
and Safe
Bellevue,
Transport
(TRANSCOM 2019),Slovak
High Tatras,
Republic,
Novy
May
Smokovec
29-31, 2019
– Grand Hotel Bellevue,
Slovak Republic, May 29-31, 2019
Analysis in modal split
Analysis
in modal
split
a
a
Michal Cingel *, Ján Čelko , Marek Drličiaka
Michal Cingela*, Ján Čelkoa, Marek Drličiaka
Department od highway engineering, Faculty of civil engineering, University of Žilina, Univerzitná 1, 010 26 Žilina, Slovak Republic
Department od highway engineering, Faculty of civil engineering, University of Žilina, Univerzitná 1, 010 26 Žilina, Slovak Republic
a
a
Abstract
Abstract
The paper deals with the parameterization of the calculation of the modal split in the four-step model determining the traffic
prognosis.
model theory is the
process
of calculating
the modal
thefour-step
model ismodel
in common
use forthe
transport
The
paper Multiple
deals withLogit
the parameterization
of the
calculation
of the modal
splitsplit,
in the
determining
traffic
modelling. Multiple
Its advantage
that we
can choose
from more
independentthevariables.
The estimation
of in
Logit
function
is
prognosis.
Logitismodel
theory
is the process
of calculating
modal split,
the model is
common
useparameters
for transport
based on transport
and sociological
in Žilina
The Biogemevariables.
program The
will estimation
be used foroftheLogit
calculation.
modelling.
Its advantage
is that we survey
can choose
fromregion.
more independent
function parameters is
The primary
task isand
to create
a set ofsurvey
Logit in
function
parameters
for Žilina
The choice of a specific
based
on transport
sociological
Žilina performance
region. The Biogeme
program
will region
be usedconditions.
for the calculation.
transport
mode
of istransport
by function
the utilityperformance
function. The
function isfor
used
in a region
disaggregated
model
individual
groups
The
primary
task
to createisaexpressed
set of Logit
parameters
Žilina
conditions.
Thefor
choice
of a specific
of the population.
Groups isare
characterized
by their
behaviour
in the transport
The disaggregated
model involves
transport
mode of transport
expressed
by the utility
function.
The function
is used inprocess.
a disaggregated
model for individual
groups
simulating
the behaviour
of individuals
in time,by
space,
their subsequent
aggregation
into the
resulting
transportmodel
relations
of the
of
the population.
Groups
are characterized
theirand
behaviour
in the transport
process.
The
disaggregated
involves
territory. The
split of
will
be taken into
account
in and
the trip
transport modes:
- driver,
car - passenger,
simulating
themodal
behaviour
individuals
in time,
space,
theirdistribution
subsequentby
aggregation
into thecar
resulting
transport
relationspublic
of the
transport,The
cycling
andsplit
pedestrian
transport.
territory.
modal
will be taken
into account in the trip distribution by transport modes: car - driver, car - passenger, public
transport, cycling and pedestrian transport.
© 2019 The Authors. Published by Elsevier B.V.
© 2019 The Authors. Published by Elsevier B.V.
Peer-review
under responsibility
the scientific
of the 13th International Scientific Conference on Sustainable,
©
2019 The Authors.
Published byof
Elsevier
B.V. committee
Peer-review
under responsibility
of
the scientific
committee of the 13th International Scientific Conference on Sustainable,
Modern
and
Safe
Transport
(TRANSCOM
2019).
Peer-review
under
responsibility
of
the
scientific
committee
of the 13th International Scientific Conference on Sustainable,
Modern and Safe Transport (TRANSCOM 2019).
Modern and Safe Transport (TRANSCOM 2019).
Keywords: transportation planning, traffic model, biogeme, four-step model , modal split
Keywords: transportation planning, traffic model, biogeme, four-step model , modal split
1. Introduction
1. Introduction
Complicated transport problems are currently addressed mainly by modelling the transportation and transport
Complicated
transport
areis currently
addressed
mainly
modelling
the transportation
and transport
processes
in the given
area.problems
Modelling
a process that
helps to
better by
determine
the problem
and its solution
options.
processes
given
Modelling
a process
that helps
to better
determine
the with
problem
andproblems
its solution
options.
It
is used in the
every
areaarea.
associated
withishuman
creativity.
Every
model
has to deal
many
to meet
the
It is usedcriteria.
in everyThese
area are
associated
human
model
has toand
deal
many problems to meet the
rigorous
always with
different
andcreativity.
depend onEvery
the type
of model
itswith
character.
rigorous criteria. These are always different and depend on the type of model and its character.
* Corresponding author. Tel.: 041 513 5930;
address: michal.cingel@fstav.uniza.sk
*E-mail
Corresponding
author. Tel.: 041 513 5930;
E-mail address: michal.cingel@fstav.uniza.sk
2352-1465 © 2018 The Authors. Published by Elsevier B.V.
Peer-review©under
responsibility
of the scientific
committee
2352-1465
2018 The
Authors. Published
by Elsevier
B.V. of the 13th International Scientific Conference on Sustainable, Moder n and
Safe
Transport
(TRANSCOM
2019).
Peer-review
under
responsibility
of the scientific committee of the 13th International Scientific Conference on Sustainable, Moder n and
Safe Transport (TRANSCOM 2019).
2352-1465  2019 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the 13th International Scientific Conference on Sustainable, Modern and
Safe Transport (TRANSCOM 2019).
10.1016/j.trpro.2019.07.028
Michal Cingel et al. / Transportation Research Procedia 40 (2019) 178–185
Cingel et al.,/ Transportation Research Procedia 00 (2019) 000–000
2
179
The need transport models use has been already proved as useful in various projects around the world. Initially,
they are using simpler models. Over time, they have developed into models based on population divided into groups
with the same behaviour in the transport process. They are called disaggregated or individual models. Their
requirements for exact computing technology and the detailed data are its biggest disadvantages. On the other hand,
it is well known that transport models are very precise. There is only one way to obtain the necessary data and that is
a survey. The best quality information of transport relations are accessible through surveys conducted directly at home,
well known as traffic - sociological survey.
Mentioned models illustrate the consequences of various measures and proposals in the transport sector. They are
[1]:
•
•
•
•
•
reproduce and understand the transport process
predicting changes for future situations
prediction of changes in the transport process of the system changes
the possible scenarios of development, the working of the different views
assessment of proposed variants of the transport solution
2. Four – step process
Traffic prognosis has gradually evolved from the simplest observations by using the growth coefficients. These
coefficients took into account in a simplified form the projected population growth and job opportunities, while the
territory was not yet part of the transport districts and the purposes of the trips was not considered. Gradually, separate
models for individual and public transport have been developed, with the potential of traffic, traffic flow and network
loading. But calculations were still made manually – not use the computers, and their range was considerably limited
[1].
Generally, the transport model includes four steps:
•
•
•
•
Trip generation (volumes of the source, destination, and transit traffic)
Trip distribution (routing traffic flows)
Breakdown transport connections by transport used (modal split - distribution of the transport work)
Trip distribution to routes and sections of transport networks
Trip generation - calculation of forecast volumes of transport:
Trip generation is often defined as the total number of routes that were generated by households in the zone.
Generally, the trips made are divided into domestic (if the origin or destination is the address of the household) and
the way out of the house (if source and destination do not address of the household) [2].
𝐷𝐷𝑖𝑖 = 𝑎𝑎 + 𝑏𝑏1 𝑋𝑋1 + 𝑏𝑏2 𝑋𝑋2 + ⋯ + 𝑏𝑏𝑛𝑛 𝑋𝑋𝑛𝑛 ,
(1)
𝐷𝐷𝑖𝑖 - dependent variable (transport volume of the i-th district),
𝑎𝑎 – constant of the regression formula,
𝑏𝑏1 , 𝑏𝑏2 , … 𝑏𝑏𝑛𝑛 – partial regression coefficients,
𝑋𝑋1 , 𝑋𝑋2 , … 𝑋𝑋𝑛𝑛 – independent variable structural variables with a decisive influence on the volume of transport [1].
With demanding requirements, the regression analysis method is replaced by more modern methods. Method of
specific momentum is the most applicable. However, this requires more detailed input into the calculation. The basic
principle of the specific momentum method is the disaggregation of the whole set into the characteristic groups of the
paths according to their purpose, the purpose of the journey illustrating activity at the destination of the path, which
is the reason why the journey is taking place [2].
Michal Cingel et al. / Transportation Research Procedia 40 (2019) 178–185
Cingel et al. / Transportation Research Procedia 00 (2019) 000–000
180
3
Trip distribution (routing traffic flows):
The role of transport relations division is working up to the size of each transport relations between the two districts,
the trip starting and ending the journey, so creating a complete matrix of relations for all n districts, which is designed
territory divided. Therefore, it is possible to build a general hypothesis that transport relations between the two districts
i and j depend on [2]:
•
•
•
•
•
availability in source area i (volume of origin transport)
attractiveness in destination j (volume of destination transport)
distance origin and destination
competition of other goals
the number of opportunities between the origin and destination i and j for the journey [1]
The basic condition of all procedures is a requirement that the combined volume of origin source and destination
transport were the same and that is equal to the total volume of traffic of the area, thus:
∑𝑖𝑖 𝐷𝐷𝐷𝐷𝑖𝑖 = ∑𝑗𝑗 𝐷𝐷𝐷𝐷𝑗𝑗 = 𝐷𝐷𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡
(2)
Other marginal conditions are the requirement that the sum of all trips from the district i to the other districts j be
equal to the volume of the origin transport of the precinct i and also that the sum of all the trips to the district j from
all the districts i is equal to the volume of the destination transport of the district j, thus [1]
∑𝑗𝑗 𝐷𝐷𝑖𝑖𝑖𝑖 = 𝐷𝐷𝐷𝐷𝑖𝑖
a
∑𝑖𝑖 𝐷𝐷𝑖𝑖𝑖𝑖 = 𝐷𝐷𝐷𝐷𝑗𝑗
(3)
Load allocation to routes and sections of transport networks:
At the final stage of modelling must already be an available ready model of the road network, which is ''plugged
in'' connectors with zones [2].
Fig. 1. Connecting the road network with connectors with selected ranges [2]
The following methods are used to calculate the volume distribution on the transport network itself:
• Shortest route method - method is based on the assumption that the transported person chooses the connection
between the origin and destination is always the shortest path.
4
Michal Cingel et al. / Transportation Research Procedia 40 (2019) 178–185
Cingel et al.,/ Transportation Research Procedia 00 (2019) 000–000
181
• two or more route allocation method - developed for cities. This method analyzes of directional surveys showed
that 30% of passengers used a variety of reasons other than the shortest path.
• limited capacity method - two working procedures are basically used, with only the shortest or two or more
shortest path again.
In the first instance, the gradual loading of the network from the matrix of transport relations, by dividing, for
example, the half-value of each relationship in the first step, by adjusting the intensities thus obtained with the
allowable intensity, adjusts the times of the sections and nodes. Next, for example, 25% of the volume to the new
most suitable routes are decomposed, followed by their modification and additional distribution of traffic load on the
remainder.
In the second process, all network traffic relationships are decomposed. From the percentage of intensity and
allowable intensities thus obtained, the travel times in the segments and nodes are adjusted, and the iterative process
repeats this process until a load distribution that corresponds to the permissible communications intensities is achieved.
The result, however, may also be a need to revise the proposed perspective communication network.
3. Basic features for selecting the mode (MODE CHOICE)
Mode selection is characterized by random selection of independent variables. In practice, the random theory is
widely applied in various industries. The various uses are different in use and the type of distribution function. This
is always adjusted to be suitable for the case. The number of distribution functions is currently estimated in thousands
of orders [5].
Fig. 2. The use of different distribution functions for modelling (x-axis is derived from the index of the functions h, the y-axis is the probability)
[6]
Use the distribution function is a worldwide expanded, whether in the field of transport planning and economic
sectors. The substance is the correct procedure for estimating the necessary parameters. Depending on the number of
independent variables, we use the function:
• Probit model
• Logit model
• Nested model
These models are used to model the relationships between the dependent variable Y and one or more independent
X values. The dependent variable Y is a discrete (discontinuous) value that represents a selection from a set of mutually
exclusive selections. For example, an analyst can choose the model of choice under cost-effectiveness (from the set
Michal Cingel et al. / Transportation Research Procedia 40 (2019) 178–185
Cingel et al. / Transportation Research Procedia 00 (2019) 000–000
182
5
of classes of vehicles), by type of transport mode (walk, train, car, ...), the model can also be selected by a mutual
combination of individual terms [5].
3.1. Multinomial choice model
We assume that the analyst can define some set C for the studied problem. Then it includes all potential choices
for some population. We call C the universal, or master, choice set, and define J to be the number of elements in it.
Each member of the population has some subset of C as his or her choice set. For example, in a mode choice model,
C may consist of eight elements:
•
•
•
•
•
•
•
•
driving alone,
sharing a ride
taxi
motorcycle
bicycle
walking
transit bus
rail rapid transit
The general expression for the probability of choosing an alternative or element i (i= 1, 2, ….J) from a set of J
alternatives is:
Pr (i ) =
eVi
J
e
Vj
(4)
j =1
Pr (i ) is the probability of the decision-maker choosing alternative I,
V j is the systematic component of the utility of alternative j.
At this stage, we will assume that each individual's choice set can be specified by the analyst using some reasonable,
deterministic rules.
4. Traffic – sociological survey
Traffic models for precisely defined territorial units are designed to simplify the demanding handling of traffic
data. The creating, usable database requires at least once a perfectly map the territory in terms of transport relations
and their character. This type of data is obtained through a traffic-sociological survey.
The actual survey was conducted through questionnaires. The nature of the questions must exactly match our
requirements. Its implementation is very demanding and costly. The larger the area we investigate, the more families,
respectively habitants we must reach out. Every interviewee must be assigned to a predetermined population group.
The distribution of the population may not always be the same. However, further steps in transport modelling should
be respected. The software used to determine transport relations always has its input data requirements. Using
disaggregated model have the same basic procedure.
The most used software in our conditions is PTV – Vision, included a few sub-programs (Visum and VISSIM are
the most know). To determine the matrix of transport relations is intended VISUM-demand model. The software is
able to determine matrices for each mode of transport as well as an overall matrix. We can always introduce other
situations in the calculation.
The real problem arises when comparing program results with the model that was with transport matrices detected
directly from the survey. The data can often be misleading. To edit the model, a set of coefficients is to be created,
Michal Cingel et al. / Transportation Research Procedia 40 (2019) 178–185
Cingel et al.,/ Transportation Research Procedia 00 (2019) 000–000
6
183
which by gradual iteration is also applicable to models in other areas similar to its character. The used trafficsociological survey was conducted in the Zilina self-region. To acquisition optimal and credible sample, the number
of households prepared on the basis of data from the Slovak Statistical Office. The survey was then performed in
every village where the required number of households is greater than 10. A total of 6231 respondents from households
with 18,382 inhabitants. Currently, data from traffic - sociological survey are analyzed, for example, modal split by
district. Individual values are given in percent Fig.3).
Modal split
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
BUS
Bike
Public Transport
Motorcycle
Car - driver
Car - passenger
Foot
Train
Fig. 3. Modal split by transport vehicle
5. Biogeme
The Biogeme package (biogeme.epfl.ch) is designed to estimate the parameters of various models using maximum
likelihood estimation. It is specially designed for discrete choice models. [3]
Biogeme is available in three versions:
• BisonBiogeme is designed to estimate the parameters of predetermined discrete choice models such as logit,
binary probit, nested logit, cross-nested logit, multivariate extreme value models, discrete and continuous
mixtures of multivariate extreme value models, models with nonlinear utility functions, models designed for
panel data, and heteroscedastic models. It is based on a formal and simple language for model specification.
• • PythonBiogeme is designed for general purpose parametric models. The specification of the model and of the
likelihood function is based on an extension of the Python programming language. A series of discrete choice
models are precoded for easy use. The package is written in C++ and is standalone.
• • PandasBiogeme is a Python package that must be imported into a Python code. It relies on the Pandas package
for data manipulation. This is the standard analysis mode that using is rapidly increased. The syntax for the
model specification is almost exactly the same as PythonBiogeme.
184
Michal Cingel et al. / Transportation Research Procedia 40 (2019) 178–185
Cingel et al. / Transportation Research Procedia 00 (2019) 000–000
7
The next part of analysis will be given expression and parameterized utility function. As an example, the basic
function for modal split between car, PuT (public transport) and foot.
V1 = ASC_PuT + B_TIME * PuT_TT + B_COST * PuT_COST + B_DIS * PuT_DS
V2 = ASC_ FOOT+ B_TIME * FOOT_TT + B_COST * FOOT_COST + B_DIS * FOOT_DIS
V3 = ASC_CAR + B_TIME * CAR_TT + B_COST * CAR_CO + B_DIS * CAR_DIS
Fig. 4. Program Biogeme – example
6. Conclusion
Traffic increasing is currently the primary problem of big cities, respectively need for the relocation of persons and
goods. Traffic growth shows the need to address traffic problems. Despite the difficult and arduous process of
obtaining input data becomes quality modelling of transport relations suitable investment for the future.
The paper deals with the development of so-called logit parameters for the Žilina Region. The foundations
necessary to obtain parameters are based on high-quality substrates, in our case, the data from the transportsociological survey conducted in the Žilina region.
At present, the next logit parameters are analysed for different modes of transport that will be useful for the
transportation planning process. The correct modal split calculation will positive impact to the value of the design
parameters (for example the ratio of heavy vehicles) [7].
8
Cingel et al.,/ Transportation Research Procedia 00 (2019) 000–000
Michal Cingel et al. / Transportation Research Procedia 40 (2019) 178–185
185
Acknowledgements
This contribution is the result of the project Centre of excellence for systems and services of intelligent transport
II., ITMS 26220120050supported by the Research & Development Operational Programme funded by the ERDF.
"Podporujeme výskumné aktivity na Slovensku/Projekt je spolufinancovaný zo zdrojov EÚ"
References
[1] KUŠNIEROVÁ, J.-HOLLAREK,T.: Methodology of modeling and prognosis of the Transport process. (in Slovak: Metódy modelovania a
prognózovania prepravného a dopravného procesu). Žilina: EDIS, 2000. 166 s. ISBN 80-7100-673-4.
[2] Ján ČELKO et al (a kolektív). Transportation Planning (in Slovak: Dopravné plánovanie). Žilina: EDIS, 2015. 265 s. ISBN 978-80-5541112-5.
[3] PandasBiogeme: a short introduction, Michal Bierlaire, December 19,2018 http://transp-or.epfl.ch/documents/technicalReports/Bier18.pdf,
[4] PTV_VISION Visem Karlsruhe, 2002. 294 s
[5] Ben-Akiva M., Bierlaire M.: Discrete Choice Methods and Their Applications To Short Term Travel Decisions
[6] http://www.philender.com/courses/categorical/notes3/probit1.html
[7] REMIŠOVÁ, E., DECKÝ, M., KOVÁČ, M.: The influence of the asphalt mixture composition on the pavement surface texture and noise
emissions production. In: 14th International multidisciplinary scientific conference SGEM 2014. Geoconference on Energy and clean
Technologies. Conference Proceedings Volume II, Section Air pollution and Climate hange.17-26, June, 2014 Albena, Bulgaria, p. 583-590,
ISBN 978-619-7105-16-2, ISSN 1314-2704, DOI: 10.5593/sgem2014B42