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

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

, PickMeUp

, or ArrivaClick

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,

https://www.viavan.com/

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.

https://www.arrivabus.co.uk/arrivaclick/

VEHITS 2020 - 6th International Conference on Vehicle Technology and Intelligent Transport Systems

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

(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

(GSD) is a public

database that contains data on the position and

purpose of buildings, e.g., residential house,

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.

https://www.geodata.se/geodataportalen/

Modelling Commuting Activities for the Simulation of Demand Responsive Transport in Rural Areas

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

) and end (have destination D

) 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

with

f

∑



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

) that occur

VEHITS 2020 - 6th International Conference on Vehicle Technology and Intelligent Transport Systems

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:

A

fc



with two balancing factors A

and B

for ensuring

both the origin and destination constraints



∑

fc







∑

fc





,

and a deterrence function fc

 that fits a modelled

distribution of tours into the observed trip length

distribution in RVU. The process happens cyclically:

first origins O

and destinations D

are balanced with

balancing factors A

and B

, then the deterrence

function fc

 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

 T is represented by a tuple

, d

, t

>, with origin (o

), destination (d

), and

desired arrival time (t

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

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).

: 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

)

against the assumed travel time by car (c

) and a factor

of maximum acceptable deviation (dev) from this

travel time is suitable, e.g.,



t





accept,ifd

t

c

∙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

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

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

 {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

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

is used to

identify suitable multi-modal trips based on a road

network (OpenStreetMap

) 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

, 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

https://www.openstreetmap.org/

https://jsprit.github.io/

Modelling Commuting Activities for the Simulation of Demand Responsive Transport in Rural Areas

times the travel time of using direct car transport plus

15 minutes, i.e., dev

= 1.5 and c

= 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.

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Modelling Commuting Activities for the Simulation of Demand Responsive Transport in Rural Areas