Modelling Commuting Activities for the Simulation of
Demand Responsive Transport in Rural Areas
Sergei Dytckov, Fabian Lorig, Paul Davidsson, Johan Holmgren and Jan A. Persson
Malmö University, Department of Computer Science and Media Technology,
IoTaP Research Centre, K2 – The Swedish Knowledge Centre for Public Transport, Malmö, Sweden
Keywords: Demand Modelling, Gravity Model, Simulation, Case Study.
Abstract: For the provision of efficient and high-quality public transport services in rural areas with a low population
density, the introduction of Demand Responsive Transport (DRT) services is reasonable. The optimal design
of such services depends on various socio-demographical and environmental factors, which is why the use of
simulation is feasible to support planning and decision-making processes. A key challenge for sound
simulation results is the generation of realistic demand, i.e., requests for DRT journeys. In this paper, a method
for modelling and simulating commuting activities is presented, which is based on statistical real-world data.
It is applied to Sjöbo and Tomelilla, two rural municipalities in southern Sweden.
1 INTRODUCTION
In minor municipalities and rural areas with low
population density, it is challenging to provide high-
quality Public Transport (PT) services that are also
economically viable (Velaga et al., 2012). The small
number of passengers often result in unsatisfactorily
large distance to the nearest bus or train stations and
low service frequency, resulting in low service
utilization, making people turn towards private cars.
The need for private individual transport can be
reduced by implementing Demand Responsive
Transport (DRT), which has potential to improve the
convenience of and access to PT (Mulley et al., 2012).
By supplementing or replacing existing PT, DRT
enables passengers to dynamically request pick-ups at
specific locations, e.g., their homes, and be brought
either directly to their destination or to a suitable PT
stop, where they can continue their journeys.
In practice, DRT services have been introduced
and tested in different cities and countries (Pettersson,
2019). However, many services were discontinued
due to, e.g., poor scalability, integration issues, or
insufficient service utilization. Still, PT providers and
municipalities see DRT as a means to reduce PT costs
due to its demand-based deployment. Passengers, in
turn, may enjoy taxi-like accessibility of a bus. To
assess the effects of introduction of DRT service, a
simulation can be used. To make an assessment of a
specific DRT design on a specific area, we need to
model realistic traveller requests that correspond to
the specifics of real-world requests such as the
requested arrival time as well as pick-up and drop-off
locations. This paper presents a method for
generating artificial, synthetic, travel demand for the
population of a municipality. It is applied to model
DRT requests of commuters that use PT to reach their
workplace. The method’s feasibility to generate
artificial commuting activities is demonstrated using
the example of Sjöbo and Tomelilla, two
neighbouring rural municipalities with approximately
32 700 inhabitants, which are located in southern
Sweden.
In this paper we present a method for generating
artificial, synthetic, travel demand with realistic
service requests. The method is a mixture of the
classic four-stage approach and agent based
modelling, where 1) the amount of trips is predicted
on a zone basis, 2) trips are distributed between zones
with a gravity model, and 3) trip flows between zones
are disaggregated to individual trips and parameters
as time and exact location are assigned to them. The
traffic assignment and mode choice steps are
integrated in an agent-based simulation of a DRT
fleet. The method is applied to model DRT requests
of commuters that use PT to reach their workplace.
Dytckov, S., Lorig, F., Davidsson, P., Holmgren, J. and Persson, J.
Modelling Commuting Activities for the Simulation of Demand Responsive Transport in Rural Areas.
DOI: 10.5220/0009367000890097
In Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2020), pages 89-97
ISBN: 978-989-758-419-0
Copyright
c
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
89
2 DEMAND RESPONSIVE
TRANSPORT
An implementation of public DRT service may
provide better travel opportunities and may be
financially more viable comparing to regular PT,
especially in low population density areas. The
availability of technological advances such as
smartphones, mobile Internet, and vehicle positioning
systems, have further promoted the interest in such
transportation concepts (Pettersson, 2019). In this
section, we highlight current approaches for the
implementation of DRT and outline the potentials of
simulation for assessing different designs.
2.1 Approaches for DRT
DRT, sometimes also referred to as Flexible Transit
Service or Dial-a-Ride transit, is a dynamic
transportation service that is flexible in route or time,
geared to the needs of the travellers (Mageean &
Nelson, 2003). Examples of ongoing services are
ViaVan
1
, PickMeUp
2
, or ArrivaClick
3
.
There exists a variety of design options when
planning DRT services. This includes, e.g., the
number of allowed pick-up and drop-off points,
routing options, booking time-windows, and vehicle
settings (Daniels & Mulley, 2012). While door-to-
door pick-up and drop-off are most convenient for
customers, they might result in increased planning
efforts, operational costs, route lengths, and travel
times. Likewise, DRT services can be either offered
only on the first- or last-mile to complement existing
PT lines or for entire trips. Finally, the size of the
time-window where customers are able to request
journeys must be defined. While early requests
simplify the planning of vehicles, it limits the
travellers’ flexibility and might affect the acceptance
of the service. It is challenging to identify an
individual and suitable design for a given target group
and environment (Sharmeen & Meurs 2018).
2.2 Simulation of Demand for DRT
Computer simulation is considered well-suited to
analyse and compare different DRT design options
prior to their implementation (Deflorio et al., 2002).
It allows, for instance, for the identification of
optimal zones, time-windows, or fleet size
(Quadrifoglio et al., 2008). Prior to the real-world
rollout of Kutsuplus pilot study in Helsinki, Finland,
1
https://www.viavan.com/
2
https://pickmeup.oxfordbus.co.uk/
between 2012 and 2015 (HSL, 2016), Jokinen et al.
(2011) simulated the DRT to assess its cost
effectiveness. The applied trip demand model was
based on a Poisson point process, which does not
consider local travel conditions, e.g., residences of
passengers or existing road- or PT networks (Hyytiä
et al., 2010).
Yu et al. (2016) and Ke et al. (2017) apply neural
networks for predicting passenger demand. A
drawback of this approach is the required size of the
training dataset. To overcome this, Ke et al. extract 1
000 000 random orders from an existing DRT service.
Hence, this approach is most feasible for improving
an existing service, while it is challenging to apply for
future services or services in different environments.
Another approach for the generation of artificial,
realistic, activities of persons include activity-based
demand generation (ABDG), where intelligent agents
proactively request journeys according to predefined
behaviours. Rieser et al. (2007) demonstrate the
combination of ABDG and traffic simulation using
the MATSim simulator. The feasibility of ABDG has
also been shown in other domains, e.g., smart homes
(Renoux & Klügl, 2018). Moreover, population
synthesis is a class of statistical approaches for
predicting decision-making, which can be also
applied to transport scenarios (Müller & Axhausen,
2010). Still, both machine learning and agent-based
approaches require detailed high-quality data on
individuals and motivations for a specific behaviour.
Deflorio (2011) outlines how the space-wise and
time-wise dispersion of travel demand in DRT
scenarios can be simulated. The author presents the
use of Monte-Carlo methods and assumes that the
number of requests per area is known and that
detailed data in the current use of PT is available.
Based on this, Deflorio derives probability
distributions for estimating commuting behaviour on
a zone-level.
We may conclude that simulation has been proven
suitable for investigating DRT design and utilization.
Unrealistic representation of demand, disconnected
from the real-world demand for DRT simulations, is,
however, a weak point of many simulations. We
argue that case-studies of realistic environment are
required to understand how to get more benefits from
DRT in a specific area.
3
https://www.arrivabus.co.uk/arrivaclick/
VEHITS 2020 - 6th International Conference on Vehicle Technology and Intelligent Transport Systems
90
3 CASE STUDY OF SJÖBO AND
TOMELILLA
In a case study, we simulated DRT usage, aiming to
identify how DRT can be implemented with
maximum benefit, in the two rural municipalities of
Sjöbo and Tomelilla. They are located in the Skåne
County in the southern Sweden. They are the home to
approximately 32 700 inhabitants, and they lie in the
rural part of Skåne with a distance of 40–75 km to
larger cities Kristianstad, Trelleborg, Lund, and
Malmö. As of 2019, Sjöbo and Tomelilla have no
regular, local, public transport, but they are connected
with 9 bus lines: 6 with connection to close-by
municipalities and 3 express lines to the above-
mentioned cities, yet, with only a small number of
stops in central Sjöbo and Tomelilla. Tomelilla also
has a railroad connection. As a part our simulation
study, we developed a method to generate a realistic
demand of DRT journeys.
3.1 Available Data Sources
It is generally known that modelling activities are
highly dictated by the availability of data in a selected
region. To estimate the demand for DRT services,
data on living and working conditions of the
investigated municipalities are required. Statistics
Sweden
4
(SCB), the national statistics agency,
provides aggregated socio-demographical
characteristics in order to ensure the privacy of
individuals.
To derive more detailed information, data from
different registers must be combined. The Swedish
SAMS (Small Areas for Market Statistics) areas,
which divides municipalities and cities into areas with
an approximately equal number of inhabitants, is a
typical basis for aggregation publicly available data.
In our case study, the travel analysis zones are defined
by SAMS. There are 17 zones in Sjöbo and 20 zones
in Tomelilla. Other studies make use of these areas as
well, such as the investigation of travel habits (RVU,
2018). We base our study on RVU from year 2013 as
we have full access to the dataset. RVU contains
detailed travel information on 24 483 individuals in
Skåne including their travel origin and destinations,
specified on SAMS zone level, reasons for travel, and
travel times, as well as information on their
availability of other transport means, e.g., car or bike.
Geographical Sweden Data
5
(GSD) is a public
database that contains data on the position and
purpose of buildings, e.g., residential house,
4
http://www.statistikdatabasen.scb.se/
industrial facility, or public building. While
residential houses serve as home for commuters and
thus tend to be the origin of commuting activities,
industrial and public buildings are usually worksites
and thus the destination of commuters.
Figure 1: Transportation modeling process.
4 MODELLING COMMUTING
ACTIVITIES
To investigate and assess the suitability of different
DRT design choices, a model is required that allows
for the dynamic planning and scheduling of DRT
vehicle routes based on trips requested by travellers.
In this section, a method for the generation of
artificial commuting activities, i.e., work trips (home-
work-home sequences), is presented. We used a
stepwise approach on the transportation modelling
process for forecasting travel demand presented by
Johnston (2014) (cf. Figure 1). In our study, steps 3
and 4 are implemented in a simulation model.
Due to reasons of data privacy, it is difficult to
obtain detailed and reliable data on travellers’ habits,
routines, and commuting activities. Thus, the required
information must be deduced from other information
sources, such as interviews, surveys or census. In
these sources, data is often aggregated, e.g., for
defined regions or groups, to ensure anonymization
of the surveyed individuals, as it is the case in
Sweden, where travel surveys are aggregated to the
level of SAMS zones. The goal of the presented
method is to build a transport demand model, based
on statistical data, that allows for the estimation of an
origin-destination (O-D) matrix on the number of
commuting activities between multiple areas.
Moreover, the start and end location as well as the
time of each request need to be estimated in order to
enable realistic simulation of DRT use.
5
https://www.geodata.se/geodataportalen/
Modelling Commuting Activities for the Simulation of Demand Responsive Transport in Rural Areas
91
Figure 2: Trip generation method.
4.1 Trip Generation
The goal of the trip generation step is to estimate the
travel demand, i.e., the number of trips that start (have
origin O
i
) and end (have destination D
i
) in each of the
predefined zones i. We use the terms production and
attraction, which are used to describe traffic flows,
where households “produce”, and workplaces
“attract” trips or commuters. This step does not yet
connect of origins and destinations (where from and
where to trips are made). In the case of lacking
individual survey data, such information can be
deduced or predicted based on socio-demographic
and household data (De Dios Ortúzar & Willumsen,
2011). Due to limited availability of data, we limited
the application of presented methodology to one
purpose of journeys, which is commuting to work.
Moreover, we explore a target group, commuters, that
is not typically eligible to use DRT services. As
home-work-home sequences start and end at the same
location, they will be generated as one tour,
consisting of two distinct trips. The return trip home
is not counted towards production of a zone of a
workplace.
An approach that can be used for estimating the
relationship between attraction and production of
regions (zones) and socio-demographic data is linear
regression trip generation models (De Dios Ortúzar &
Willumsen, 2011). The benefit of regression is that it
is simple, it produces interpretable results, and works
sufficiently well with a medium sized dataset.
When using regression models to estimate travel
demand in terms of ingoing and outgoing work trips,
it might occur that the sum of all respective trips over
all zones is not equal. According to De Dios Ortúzar
and Willumsen (2011), the assumption can be made
that the overall sum of outgoing trips (T) is correct
because the amount and quality of data on housing is
typically better, and the number of ingoing trips is
adjusted accordingly by factor f for all destination
zones D
j
with
f
T
D
jj
.
For our case study, we build a regression model to
predict the number of trips produced and attracted by
the municipalities of Skåne. Then we apply the model
to predict the amount of trips in each SAMS zone of
the target municipalities. To produce sane non-
negative results, we used lasso regression with zero
intercept and strictly positive coefficients. We used
demographical (different statistics on population
size) and land use data (amount of buildings by type)
out of which data on the day population (considering
the workplace of individuals), night population
(considering the residence of individuals) was
significant and not strongly linearly correlated. The
dependent variable for fitting, the number of outgoing
journeys, is calculated based on SCB data on
commuters between municipalities and adjusted by
the average daily trip ratio extracted from the RVU
2013 survey (cf. Figure 2).
The resulting model is presented in Table 1. There
is not enough data in the RVU surveys to compare the
model with the trips between SAMS zones, so the
data were aggregated back to the level of
municipalities and the predicted aggregated data is
compared to the original data of production of
municipalities from SCB. R2
adj
=0.99988 with a mean
deviation in the amount of trips of 70 trips and
standard deviation of 155.
Likewise, the number of buildings that might
serve as workspace, the day population in the public
and economic sector, the number of inhabitants that
are between 25 and 44 years (age range according to
SCB division) are used to construct an attraction
regression model (cf. Table 2). R2
adj
=0.9998, where
the average deviation of predicted attraction
aggregated to municipalities from SCB data is 94
trips with standard deviation of 293.
The results of the production model show that
employees of private sector produce more trips than
of public sector. However, the situation is opposite in
relation to attraction. An explanation can be that the
number of industrial buildings explains attraction
better that the amount of registered people.
4.2 Trip Distribution
After estimating the total number of work trips that
have their origin or destination in a specific zone, the
next step is to generate a trip matrix, which contains
information on the number of trips (T
ij
) that occur
VEHITS 2020 - 6th International Conference on Vehicle Technology and Intelligent Transport Systems
92
Table 1: Production regression model.
Coefficient
Std.
Error
t-value
*
Employees Public
Sector (Night pop.)
0.5158 0.018 28.324
Employees Economic
Sector (Night pop.)
0.6993 0.010 68.610
Employees Economic
Sector (Day pop.)
0.0372 0.008 4.949
*
all P values are less than 0.001
between all possible pairs of considered zones as well
as within zones. A common approach for estimating
the number of trips between different zones is the use
of gravity models, which is based on the assumption
that a correlation exists between the number of
travellers between two zones and the number of trips
originating and ending in the respective zones. As
there is enough data to rely on both attraction and
production values, we may use the tri-proportional
fitting method:
T
ij
A
i
O
i
B
j
D
j
fc
ij
with two balancing factors A
i
and B
j
for ensuring
both the origin and destination constraints
A
i

1
B
j
D
j
fc
ij
j
,
B
j

1
A
i
O
i
fc
ij
j
,
and a deterrence function fc
ij
that fits a modelled
distribution of tours into the observed trip length
distribution in RVU. The process happens cyclically:
first origins O
i
and destinations D
j
are balanced with
balancing factors A
i
and B
j
, then the deterrence
function fc
ij
is adjusted to fit the resulting modelled
trip length into observed trip length distribution.
In our case study, for balancing production and
attraction rates, attraction rates are scaled to the level
of productions. The distance of a shortest-path car trip
between the SAMS zones is used to assess travel
costs. Length of trips within the zones is taken a as a
half of distance to a closest zone. Deterrence function
is balanced to represent distribution of trip lengths
observed in RVU. Number of trips is rounded to
nearest integer after each balancing operation.
The trip matrix does only contain aggregated
information on the number of journeys, not on
individual travels. For the modelling of realistic DRT
requests, this is not sufficiently detailed, as the
temporal and spatial occurrence of requests might
influence the success of different DRT design
options. The desired arrival time of each traveller
must be considered as well as information on the start
Table 2: Attraction regression model.
Coefficient
Std.
Error
t-value
*
Inhabitants with
Age between 25-44
0.2051 0.029 7.003
Number of Industrial
Buildings
0.9510 0.197 4.833
Employees Public
Sector (Day pop.)
0.6471 0.025 25.617
Employees Economic
Sector (Day pop.)
0.5360 0.026 20.234
*
all P values are less than 0.001
and endpoint of the trip that is as accurate as possible.
Each trip t
i
T is represented by a tuple
<o
i
, d
i
, t
i
>, with origin (o
i
), destination (d
i
), and
desired arrival time (t
i
).
GSD land use datasets are used to randomly
assign each traveller with a residential building of its
origin zone, from where the work trip is requested and
a workplace with company or public buildings, to
which the journey is requested. The point in time for
which the traveller decides to request a DRT journey
is determined based on the RVU survey. Probability
distribution fitting can be used to derive adequate
probability distributions of travel start or end times as
well as on the working hours based on data acquired
by the survey (cf. Figure 3). Through this, realistic
travel and work behaviour of an arbitrary number of
inhabitants can be modelled.
To validate the generated commuting trips, they
can be compared to real-world survey data. To this
end, attraction and production rates estimated by the
regression models can be compared to SCB data on
commuters per municipality and the resulting O-D
matrix can be validated against data from the RVU
survey.
In Table 3, the commuting tours of the generated
O-D matrix are compared against data from SCB. As
SCB data on commuters is not available for the
considered year, datasets from two surrounding
studies have been linearly interpolated. A deviation
between SCB and RVU data can be observed, which
might be due to the origin of the data. While RVU
data was acquired directly by surveying travellers,
SCB commuting data might be based on estimations
from other surveyed data, e.g., residence and
workplace of individuals. Hence, we assume that
RVU data we used as basis for our regression model
is more representative and scale the interpolated SCB
data according to RVU data with a derived factor of
0.65 such that they can be compared to the data we
simulated.
It can be observed that the simulated number of
outgoing, incoming, and intra-zonal tours deviates
Modelling Commuting Activities for the Simulation of Demand Responsive Transport in Rural Areas
93
between 3 and 20% from data that is provided by
SCB. It is also important to notice that SCB data only
exists on a municipality level, while the simulated
data from all zones within the municipality was
cumulated for the comparison.
4.3 Modal Choice
As a result of the previous two steps, an O-D matrix
is generated, which estimates the flows of commuters
between the investigated zones. In the classical
transportation forecasting model, the selected travel
mode is determined by statistical choice models, e.g.,
direct demand models.
To allow for more sophisticated and individual
decision-making, the use of agent-based modelling
(ABM) is suitable (DeAngelis & Diaz, 2019). Instead
of forecasting the use of PT and DRT based on, for
instance, travel time diversion curves or by means of
utility functions, travellers in ABM proactively and
individually select the travel mode that seems most
suitable considering their current personal situation,
environment, and desires. This is done through agent
function F, which determines a decision on whether
or not to accept an offered DRT trip (action
A={accept, reject}) from information that is provided
on the offered journey (e.g., estimated departure,
Figure 3: Probability distribution of a) work and home trip
start times b) work length distribution.
Table 3: Comparison of simulated O-D matrix and SCB
commuter data.
Simulated
Data
Interpolated
SCB Data
Dev. in
Percent
Sjöbo
- outgoing
- incoming
- within
2 955
1 633
2 957
3 050
1 296
2 680
-3.2%
+20.6%
+9.3%
Tomelilla
- outgoing
- incoming
- within
1 803
1 434
2 242
1.863
1 173
2 044
-3.3%
+18.2%
+8.8%
travel, or arrival time) as well as on the traveler’s
individual circumstances (perceptions P; e.g., access
to car, age, or income).
F
i
: P* A
In transportation, econometric utility-based
models are the dominate modelling approach, where
the most widely used is multinomial logit model
(McFadden, 1973). There are other approaches, such
as Machine learning based methods (Chen et al.,
2017, Daisik et al., 2017). They have a potential to
produce good results, but they require large dataset
for training. In this case study, we do not possess data
to produce a complex model for choice of DRT.
To implement an initial decision-making of the
traveller, we compare a planned DRT travel time (d
t
)
against the assumed travel time by car (c
t
) and a factor
of maximum acceptable deviation (dev) from this
travel time is suitable, e.g.,
F
i
d
t

accept,ifd
t
c
t
∙dev
c
reject,else.
This factor describes the relative delay that the
traveller is willing to accept in favour of DRT
transportation regarding the length of a direct trip. c
is a constant value that is added to the previously
determined deviation time. Both delays can either be
individual values for each traveller i, e.g., depending
on its access to other means of transportation, or equal
for all travelers. Also, the delay can refer to the entire
work trip, from home to workplace, or only on the
DRT leg, e.g., from home to the first PT stop.
4.4 Traffic Assignment
Finally, as a last step of the methodology, the choice
of the route takes place (Patriksson, 2015). This step
considers overload and congestions that might occur
if all travellers chose the same route for their journey.
In the presented case study, the focus lies on DRT and
PT in a low-density area. Thus, it can be assumed that
PT services will not deviate from their planned route
VEHITS 2020 - 6th International Conference on Vehicle Technology and Intelligent Transport Systems
94
Figure 4: Distribution of start points of DRT journeys a) potential travellers and b) subset of simulated requests that were
accepted (green), rejected (red), and ignored due to its closeness to PT stops (black).
and that significant delays due to increased traffic
density will not occur. Still, the number of DRT
vehicles as well as their capacity are limited.
Accordingly, trip request put a load on the DRT
service resulting in increased waiting and travel times
for travellers due to detours to serve multiple clients.
The trips with the large detours will likely be rejected
by travellers, which prevents overload of the DRT
service itself in a self-regulatory way.
An important assignment task that must be solved
and optimized in DRT scenarios is the dynamic
planning and scheduling of the DRT vehicles. Each
of the travellers’ requests must be allocated to one of
the vehicles and both pick-up and drop-off must be
scheduled in accordance with maximum deviation
restriction. This results in a vehicle routing problem
with paired pickups and deliveries, i.e., the dial-a-
ride problem, with the goal of minimizing the total
costs of transportation
minc
ij
x
ij
k
i,j∈Vk∈K
according to (Parragh et al., 2008), with a set of
vehicles K and set V of available edges leading from
node i to node j. c

is defined as the costs to pass edge
(i,j) and x
ij
{0,1} being 1 only if edge (i,j) is used
by vehicle k. The costs might consider both the time
and distance of the journey.
This optimization problem is dynamic, as request
may come in a real-time. When a traveller requests a
trip, a state of the service (position of vehicles,
planned routes) are determined by previous travel
requests. The resulting characteristics of a trip (travel
6
https://www.opentripplanner.org/
and waiting times) can be compared to the
expectations of the requesting traveller to evaluate
whether the person will use DRT to make a trip.
Simulation of DRT service and traveller behaviour
allows to realistically estimate operational
characteristics of a service. Furthermore, data is
generated that allows for the assessment and
comparison of the performance and utility of different
DRT designs.
5 RESULTS
To simulate the request of DRT journeys by
commuters for Sjöbo and Tomelilla and to estimate
the travel times for each requested journey, two tools
are combined. First, OpenTripPlanner
6
is used to
identify suitable multi-modal trips based on a road
network (OpenStreetMap
7
) and PT timetables. To
provide efficient DRT services, requests can be
grouped and served together, so that pick-ups and
drop-offs of other potential passengers must be
considered when estimating travel times. To this end,
we used jsprit
8
, a vehicle routing problem solver, to
find optimal routes for a set of requests.
We assume that individuals are allowed to request
DRT services for trips with a distance of more than
two kilometres. It may be a direct trip by DRT within
the borders of the two municipalities or a connecting
trip by DRT to a PT stop if a person commutes out of
or into the studied municipalities. Moreover, we
assume that travellers are not willing to accept DRT
legs with an estimated travel time of more than 1.5
7
https://www.openstreetmap.org/
8
https://jsprit.github.io/
Modelling Commuting Activities for the Simulation of Demand Responsive Transport in Rural Areas
95
times the travel time of using direct car transport plus
15 minutes, i.e., dev
i
= 1.5 and c
i
= 15. For example,
a car trip of 15 minutes will only we replaced by a
DRT trip if this trip does not take longer than 38
minutes. The simulated requests are shown in Figure
4, where each blue point represents the start point of
a work trip.
The simulation was run with 30 vehicles (minibus
with 8 seats) and for the morning of a workday, i.e.,
outward journeys. DRT trips could be successfully
offered to 990 travellers and the rejection rate was
27%. In average, there were 42.6 travellers per
vehicle and the total distance driven by all vehicle
was 18 444 km (average of 614.8 km per vehicle). 47
trips were not served as they were too short (2 km).
On the right side of Figure 4, the simulated
requests are shown. Here, green dots represent
requests that were accepted by the travellers as the
estimated travel time was satisfactorily for them. Red
dots mark requests that were rejected or trips that
could not be routed by the planning and scheduling
engine. This might be due to unavailability of
adequate PT connections or capacities of DRT
vehicles. Black dots represent travellers that are
excluded from the system due to their closeness to PT
stops. In the simulation, this occurs in some parts of
the city centre.
6 CONCLUSIONS
In this paper, we presented an approach for the
modelling of realistic commuting activities, which
can be used for the assessment and comparison of
DRT designs. In contrast to many other existing
approaches, requests for DRT journeys are generated
on a level of individuals, such that individual pick-
ups and drop-offs at the home or workplace of the
commuters can be simulated. To show the feasibility
of the approach, a case study is presented of Sjöbo
and Tomelilla, two municipalities that are located in
the rural area of southern Sweden.
The case study presented in this paper is only a
first step towards showing the feasibility of the
generated requests. The results do not yet allow for
assessing the effectiveness or viability of a DRT
service. For this purpose, a more advanced simulation
study must be conducted in which different designs
are systematically compared. The intention of the
presented simulation experiments is to show the
feasibility of the approach. For the design of an
efficient DRT service, extensions of the presented
simulation are reasonable. There is a need in more
advanced mode choice model, that considers further
attributes of the travellers that influence their decision
towards the use of DRT services, e.g., climate
protection awareness, availability of car, or age.
Accordingly, the route scheduling should not only
focus on operational costs but also optimize quality
of the service for passengers. In this case study, road
congestions were not considered as we investigate
rural areas. However, in urban conditions, congestion
is an important factor. Still, the presented case study
indicates where potential DRT requests occur and
when rush hours can be expected. Also, a first
estimation of the number of required vehicles can be
made based on the number of requests.
The generation of demand for DRT is a key
component of a simulation framework for
investigating the suitability of different DRT design
decisions. We plan to not only simulate commuters
that use DRT on their way to work but also other
types of travel, for instance, school, medical, or
leisure trips.
Our overall goal is to provide decision-makers
with a tool that can be used to explore and assess, how
to plan and execute an efficient and high-quality DRT
services. The development of a modelling and
simulation framework facilitates the conducting of
comprehensive simulation studies of DRT services.
This includes the consideration of specific local
conditions such as the distribution of inhabitants or
existing PT lines but also of different performance
measures on the utilization of vehicles or average
travel times. This allows for the thorough
investigation of different design decisions and
parameters, to identify whether and how they affect
efficiency, viability, and acceptance of DRT for
specific scenarios.
REFERENCES
Chen, X. M., Zahiri, M., & Zhang, S. (2017).
Understanding ridesplitting behavior of on-demand ride
services: An ensemble learning approach.
Transportation Research Part C: Emerging
Technologies, 76, 51-70.
Daniels, R., & Mulley, C. (2012). Flexible transport
services: Overcoming barriers to implementation in
low-density urban areas. Urban Policy and Research,
30(1), 59-76.
DeAngelis, D. L., & Diaz, S. G. (2019). Decision-Making
in Agent-Based Modeling: A Current Review and
Future Prospectus. Frontiers in Ecology and Evolution.
6:237.
De Dios Ortúzar, J., & Willumsen, L. G. (2011). Modelling
Transport. John Wiley & Sons.
Deflorio, F. P. (2011). Simulation of requests in demand
VEHITS 2020 - 6th International Conference on Vehicle Technology and Intelligent Transport Systems
96
responsive transport systems. IET intelligent transport
systems, 5(3), 159-167.
Deflorio, F. P., Dalla Chiara, B., & Murro, A. (2002).
Simulation and performance of DRTS in a realistic
environment. In Proceedings of the 13th Mini-Euro
Conference on Handling uncertainty in the analysis of
Traffic and Transportation systems and the 9th Meeting
of the Euro Working Group on Transportation
Intermodality, Sustainability and Intelligent transport
systems (pp. 622-628).
HSL (2016). Kutsuplus – Final Report. Helsinki Regional
Transport Authority. https://hsl.fi/sites/default/files/
uploads/8_2016_kutsuplus_finalreport_english.pdf
Hyytiä, E., Häme, L., Penttinen, A., & Sulonen, R. (2010).
Simulation of a large scale dynamic pickup and delivery
problem. In Proceedings of the 3rd International ICST
Conference on Simulation Tools and Techniques (p.
77).
Johnston, R. A. (2004). The Urban Transportation Planning
Process. In Hanson, S. & Giuliano G. (Eds.), The
Geography of Urban Transportation (3
rd
ed., pp. 115–
140). The Guilford Press.
Jokinen, J. P., Sihvola, T., Hyytiä, E., & Sulonen, R. (2011).
Why urban mass demand responsive transport?. In
2011 IEEE Forum on Integrated and Sustainable
Transportation Systems (pp. 317-322).
Ke, J., Zheng, H., Yang, H., & Chen, X. M. (2017). Short-
term forecasting of passenger demand under on-
demand ride services: A spatio-temporal deep learning
approach. Transportation Research Part C: Emerging
Technologies, 85, 591-608.
Mageean, J., & Nelson, J. D. (2003). The evaluation of
demand responsive transport services in Europe.
Journal of Transport Geography, 11(4), 255-270.
McFadden, D. (1973). Conditional Logit Analysis of
Qualitative Choice Be. Frontiers in Econometrics, 105-
142.
Müller, K., & Axhausen, K. W. (2010). Population
synthesis for microsimulation: State of the art.
Arbeitsberichte Verkehrs- und Raumplanung, 638.
Mulley, C., Nelson, J., Teal, R., Wright, S., & Daniels, R.
(2012). Barriers to implementing flexible transport
services: An international comparison of the
experiences in Australia, Europe and USA. Research in
Transportation Business & Management, 3, 3–11.
Nam, D., Kim, H., Cho, J., & Jayakrishnan, R. (2017). A
model based on deep learning for predicting travel
mode choice. In Proceedings of the Transportation
Research Board 96th Annual Meeting Transportation
Research Board, Washington, DC, USA (pp. 8-12).
Papanikolaou, A., Basbas, S., Mintsis, G., & Taxiltaris, C.
(2017). A methodological framework for assessing the
success of Demand Responsive Transport services.
Transportation Research Procedia, 24, 393–400.
Parragh, S. N., Doerner, K. F., & Hartl, R. F. (2008). A
survey on pickup and delivery problems. Journal für
Betriebswirtschaft, 58(1), 21-51.
Patriksson, M. (2015). The Traffic Assignment Problem:
Models and Methods. Courier Dover Publications.
Pettersson, F. (2019). An international review of
experiences from on-demand public transport services.
K2 working papers 2019:5. http://lup.lub.lu.se/record/
e9e0079b-609f-4da9-a075-2a292d5aff03
Quadrifoglio, L., Dessouky, M. M., & Ordóñez, F. (2008).
A simulation study of demand responsive transit system
design. Transportation Research Part A: Policy and
Practice, 42(4), 718-737.
Renoux, J., & Klugl, F. (2018). Simulating daily activities
in a smart home for data generation. In Proceedings of
the 2018 Winter Simulation Conference (pp. 798-809).
IEEE.
Rieser, M., Nagel, K., Beuck, U., Balmer, M., &
Rümenapp, J. (2007). Agent-oriented coupling of
activity-based demand generation with multiagent
traffic simulation. Transportation Research Record,
2021(1), 10-17.
RVU (2018). Resvaneundersökning i Skåne 2018.
https://utveckling.skane.se/publikationer/rapporter-
analyser-och-prognoser/resvaneundersokning-i-skane/
Sharmeen, F., & Meurs, H. (2018). The Governance of
Demand-Responsive Transit Systems – A Multi-level
Perspective. In: The Governance of Smart
Transportation Systems (pp. 207-227). Springer, Cham.
Velaga, N. R., Beecroft, M., Nelson, J. D., Corsar, D., &
Edwards, P. (2012). Transport poverty meets the digital
divide: accessibility and connectivity in rural
communities. Journal of Transport Geography, 21,
102–112.
Yu, S., Shang, C., Yu, Y., Zhang, S., & Yu, W. (2016).
Prediction of bus passenger trip flow based on artificial
neural network. Advances in Mechanical Engineering,
8(10).
Zhao, Y., & Kockelman, K. M. (2002). The propagation of
uncertainty through travel demand models: an
exploratory analysis. The Annals of regional science,
36(1), 145-163.
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