Using Time-Use Surveys in Multi Agent based Simulations of Human

Activity

Quentin Reynaud

, Yvon Haradji

, François Sempé

and Nicolas Sabouret

LIMSI, CNRS, Univ. Paris-Sud, Université Paris-Saclay, Bât 508, Campus Universitaire, 91405 Orsay, France

EDF RandD, EDF Lab Paris-Saclay, 7 Boulevard Gaspard Monge, 91120 Palaiseau, France

François Sempé AE, Paris, France

Keywords: Multi Agent based Simulation, Human Behavior Simulation, Time Use Survey.

Abstract: Human behavior simulations in multi agent systems often lack data to calibrate and qualify the

representativeness of the simulated behaviors. In this paper, we will show that massive investigations such as

time-use surveys allow us to obtain this type of data. At the present time, time-use surveys are mostly used to

validate the realism of human activity at a macroscopic level (population scale). In this paper, we present a

new method of human behavior generation that combines the use of time-use surveys to calibrate human

activities, with a multi agent system enabling simulated behaviors to gain reactivity, autonomy, coordination

and realism at a microscopic level (individual scale).

1 INTRODUCTION

During the last decades, computer simulations have

become indispensable tools in many research areas

such as energy, meteorology, geography, etc. (Law et

al., 1991). Yet, how one can calibrate models and

validate the realism of the produced activities remains

an open question (Rakha et al., 1996; Caillou and Gil-

Quijano 2012; Lacroix et al., 2013).

This is particularly true within the context of

human activities simulation, where one can find an

abundance of simulators focusing on different

aspects, such as facial expression realism (Pelachaud,

2009), crowd movement (Thalmann et al., 2007) or

the decisional process (Laird, 2012). Each of these

domains needs the development of adapted validation

methods.

Our research framework is human activity

simulation in order to study residential electricity

consumption (Amouroux et al., 2013). Many studies

deal with this type of human activity simulation in the

world of multi agent systems (MAS). Depending on

the simulation’s needs, the simulated human activities

can either be highly scripted (Ulicny and Thalmann,

2001; Sharma and Otunba, 2012) or result from the

behavior of more autonomous agents (Rao and

Georgeff, 1991; Traum et al., 2003; Shendarkar et al.,

2008). Other approaches try to combine the

advantages of both previous methods (Grosz and

Kraus, 1996; Tambe, 1997; Hubner and Sichman,

2007; Lanquepin et al., 2013).

In all these approaches, the issue arises: how to

validate the realism of the produced activities? As

many authors have shown, for example in (Gratch et

al., 2009; Darty et al., 2014), the notion of "realism"

can be viewed from several angles, depending on the

type of simulation: likelihood or frequency of

individual behavior consistent with each other over

time allowing to reproduce high-level indicators,

loyalty to the psychological level of decision making,

etc.

One way of measuring the realism of a human

behavior simulation, is to confront individuals with

their own activity simulation, following a

participatory simulation approach (Drogoul et al.,

2003; Haradji et al., 2012). However, this method

drastically lacks scalability. The amount of time

needed for the interviews and the case by case basis

of activity modelling make it impossible to simulate

a large number of individuals and measure the realism

of the simulation.

Is it possible to automate this process? In order to

do so, one needs data about the activities of a large

number of individuals, formatted in a model allowing

to objectively compare them. Yet, this data exists in

the time-use surveys (TUS)(Stinson, 1999). TUS are

daily surveys in which respondents must transcribe

Reynaud Q., Haradji Y., SempÃl’ F. and Sabouret N.

Using Time Use Surveys in Multi Agent based Simulations of Human Activity.

DOI: 10.5220/0006189100670077

In Proceedings of the 9th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2017), pages 67-77

ISBN: 978-989-758-219-6

their day as a series of episodes. These surveys exist

in many countries and are well standardized.

Statistical methods in the field of energy simulation

use these TUS in order to simulate human activity and

calibrate their models at a macroscopic level. The

realism of the activity produced is then measured in

terms of statistical proximity with the actual observed

behavior. In this article, we propose to use TUS to

produce realistic behavior following this statistical

definition.

We will first present some methods of human

behavior simulation in the field of multi agent

systems, as well as statistical methods using TUS in

the world of energy simulation. We will show how

ensuring the realism of behavior at a macroscopic

level is not enough to ensure the validity of individual

behavior. Moreover, we will argue that combining the

use of TUS with agent-based modelling improves the

realism of the simulated individual behaviors. We

will continue by giving more details about our

method of human behavior generation, combining a

MAS model and a TUS-based model.

2 STATE OF THE ART: HUMAN

BEHAVIOR SIMULATION

2.1 In Multi Agent Systems

In the field of human behavior simulation, the multi

agent approach offers several modelling methods.

The choice between these methods depends on the

goals of the simulation.

Scripted behaviors planned in advance by the

modeller can be used, for example, when the expected

behaviors are well known and sufficiently well

formatted. Those are typically used in training

simulations where the purpose is to expose a learner

to a specific scenario (Sharma and Otunba, 2012), or

in the case of urban emergency situations (Ulicny and

Thalmann, 2001).

In such approaches, one tends to limit the

behavior autonomy in order to ensure that the

simulations will be conducted as desired. Conversely,

it may be useful to model much more autonomous

behaviors, for example within the context of

negotiation and team work (Traum et al., 2003). One

way to do so is the BDI model (Belief-Desire-

Intention) (Rao and Georgeff, 1991), widely used in

multi agent based simulations (MABS). In this model,

the agent’s goals and belief are modelled. Thus, its

behavior is a means used in order to achieve its goals.

These models are also particularly interesting in

large-scale simulations (Shendarkar et al., 2008),

since scripts are difficult to implement in highly

unpredictable and unstable environments.

On a wider angle, when one tries to model

collective behavior, it becomes necessary to use

prescribed coordination models (Lanquepin et al.,

2013), and to combine them with reasoning

mechanisms. Several ways to achieve that

combination exist. For example, (Hubner and

Sichman, 2007) proposes to build a system of

organizational constraints that each agent must

respect. Rather than constrain the behavior of

autonomous agents, one may seek to equip agents

with teamwork models to enable them to coordinate

themselves in an adaptive and flexible way (Tambe,

1997), or allow them to collectively plan their actions

(Grosz and Kraus, 1996).

Our work follows a combined approach with both

prescribed activity at a global scale and autonomous

decision making for action selection at a fine-grain

level.

2.2 Statistical Methods using Time-Use

Surveys

TUS are daily surveys in which respondents must

transcribe their day as a series of episodes. For

example in the French TUS used in our research

(INSEE 2010), respondents had to indicate which

activity they were currently doing, every 10 minutes

for the whole day.

The use of TUS in simulations is restricted to a

few applications only, including the simulation of

household energy consumption. These are not as

widely used as they could be, certainly due to the lack

of visibility of these studies outside the world of

statistics. Moreover, if TUS are so attractive in the

world of energy, it is because it has been shown that

modelling residential energy consumption cannot be

realistic without any consideration about human

activity (Hitchcock, 1993). To improve the realism of

simulated consumption load curves, it is necessary to

integrate statistically realistic human behavior

models.

It is possible to distinguish two main trends in the

use of TUS to simulate human activity: "top-down"

approaches and "bottom-up" ones.

2.2.1 “Top-down” Approaches

The TUS data can be used to compute a matrix which

determines, at every time of the day, the probability

for an individual to switch from an activity to another.

(Richardson et al., 2010; Widén et al., 2012) use

ICAART 2017 - 9th International Conference on Agents and Artiﬁcial Intelligence

Markov chains to model these probabilities for a

change in activity (a simple presence / absence in the

housing in the first case, and ten classical activities in

the second). Similarly, (Chiou, 2009) uses a bootstrap

method (DeGroot, 1986) to extract the structure of the

daily behavior in US households.

These approaches are traditionally limited by

three factors (Yamaguchi and Shimoda, 2015). First,

they need to have the raw data of the TUS, but this

data isn’t always freely available, depending on the

laws of the country. Second, they lack accuracy about

the duration of the different activities. Because these

approaches focus on transitions between activities,

the length of each one may be less strictly simulated.

Finally, the coordination between members of the

same household is difficult to consider. Indeed, the

matrix of activity switches is not intended to be

dependent on the environment (or other agents).

2.2.2 “Bottom-up” Approaches

The TUS data can also be used to determine the mean

duration and distribution of activities during the day.

With this information, the "bottom-up" approaches

are able to build schedules iteratively, selecting

activities one after the other, according to a

probabilistic distribution. When the new activity is

selected, the duration is also calculated with the same

method. Thus, with each new selection of behavior, it

is possible to take the current situation into account.

(Tanimoto et al., 2008) show that this approach

does not require us to have access to the raw data of

the surveys. The only information needed is:

• the mean duration (and the associated standard

deviations) of each activity

• the percentage of individuals who adopt a

specific activity at a given time (this percentage

is called PB)

The timetables are built iteratively from an initial

activity by randomly choosing the next activity from

PB, when the previous activity ends.

(Wilke et al., 2013) improve the method by

initially taking into account the periods of presence /

absence of individuals in their housing. From the

perspective of the simulation of energy consumption,

the presence or absence of any individual in the

habitat fundamentally alters the housing consumption

profile. Therefore, it seems appropriate to use it as a

"framework". When a person enters the house, the

model determines the first activity to be selected as

well as its duration, and so on until he leaves. Then

the model directly “jumps” to the next attendance

period.

Another method, developed by (Yamaguchi and

Shimoda, 2015) also uses a strict structure of

activities, but through behaviors called “routine”.

Thus, activities such as sleep, work and study, along

with those related to meals and hygiene are initially

placed in the timetable. Other behaviors are then

selected in order to fill gaps in the schedule. The

originality of the method is to give prior attention to

activities structuring the schedule. Through these

routine activities, it is possible to take into account an

early coordination between members of the same

household.

All these statistical methods (top-down and

bottom-up) aim to replicate realistic human activities

at a macroscopic level. The simulated activities are

intended to match with the observed ones only at a

macroscopic scale. However, this approach does not

focus on the simulation of realistic individual

behavior. There is indeed no need to simulate a

collection of individually realistic behaviors to

simulate a realistic aggregate behaviour (Thalmann et

al., 2007). In a way, the simulated individual

behaviors are not taken into account, but they are only

"emerging" from the targeted aggregated behaviors.

This is the reason why we are going to combine the

high-level TUS-based approach with the MABS.

2.3 The Interest of Coupling MAS and

Statistical Methods

One goal of simulation in energy consumption is to

provide predictions of the future evolution of load

curves as new practices appear in the household (e.g.

new electronic devices or low-consumption

appliances), or the projection in fictional situations

(e.g. to assess the impact on the load curve of a major

event such as a sport competition or weather change).

Understanding such evolutions requires to generate

individually realistic behavior over time, able to

respond and adapt to environmental changes.

However, statistical methods offer limited

information on this regard.

2.3.1 The Limits of Statistical Methods

Statistical methods are not trying to simulate

autonomous or even reactive individuals. Statistical

methods aim at reproducing observed behavior in a

reference situation (the situation corresponding to the

TUS). They cannot be applied to unknown situations,

in which there is no statistical data to match.

Statistical approaches also do not aim at generating

individually realistic behavior over time, able to

respond and adapt to environmental changes.

Using Time Use Surveys in Multi Agent based Simulations of Human Activity

Similarly, none of the above statistical methods

completely deal with the issue of agents’

coordination, since it is not essential to ensure the

realism of the activities at a macroscopic level. In the

best case, a limited coordination is restricted to a few

"routine" behaviors (eat, wash, etc.). However, many

other behaviors require coordination (from a family

walk to helping a child to get dressed, or even

choosing the set temperature for the housing). That is

why, in order to simulate energy consumption as well

as other applications, having a coordination model

between simulated individuals is a necessity.

2.3.2 The Benefits of the Agent Centred

Approach

Unlike statistical methods, the agent centred

approaches use models centred on the simulated

individuals. Therefore, they aim for a validity at the

individual level. In MABS, the “emerging behaviors”

are the collective ones, as they are not explicitly

described in the model, but arise from interactions

between individual agents (Drogoul and Ferber,

1992).

In our work, we are interested in realism at a

macroscopic level (population scale) and we need the

TUS data to calibrate the simulated activities.

However, we are also interested in validity at a

microscopic level (individual scale) for prediction

and simulation of fictional situations. We want to be

able to simulate reactive, autonomous and coopera-

tive behaviors over time. That is why we developed

another method that combines the advantages of both

statistical and agent-based approaches. Our method is

in this sense a “mixed” MAS method, using reactive

and autonomous agents, whose behavior is calibrated

from statistical models derived from TUS.

3 OUR PROPOSITION

3.1 General Principle

This section presents our model of human behavior

generation. It is based on the combination of a

bottom-up approach to TUS with an existing and

already validated agent model: SMACH. We will

first present our agent model, then the TUS we

applied to calibrate the model. Afterwards, we will

discuss two specific issues encountered. First, which

data to collect in the TUS in order to increase the

validity of the simulated activities at a microscopic

scale. Second, how to generate both routine activities

and activity variability over time. We will then give

more details about the activity generation algorithm,

and two possible methodologies in order to validate

the model.

3.2 Description of the SMACH Agent

Model

SMACH is a simulation platform of human activity

inside the housings. Its ability to simulate individual

behaviors similar to real ones has already been

validated (Amouroux et al., 2014). In this platform,

each individual is modelled as an agent with goals

(tasks to perform), knowledge (about the other

individuals and the environment) and preferences

(either in terms of comfort or behavior). The agents

are able to exchange information, coordinate with

each other to perform specific tasks, plan their days

(agents can have preferences about when to perform

a specific task) and their weeks (for example, agents

can have preferences about the number of times per

week they wish to use a washing machine).

All the information needed to launch a simulation

are called the “scenario”. It contains a description of

the housing (type, surface, insulation, etc.), weather

conditions, household type and individuals. In a

scenario, each individual has a set of tasks to perform.

These tasks have the following features:

• Duration. Each task has a minimum and a

maximum duration. When an agent performs a

task, its priority increases compared to the other

ones, in order to prevent constant activity

swapping. The priority of this task decreases

after reaching the minimum duration, and it is

reduced to its minimum value after reaching the

maximum duration.

• Rhythm. Each task can be assigned a number of

repetitions per day or per week. For example: the

task “sleep” takes place once a day, the task “use

the washing machine” takes place three times a

week.

• Preferential period (PP). Each task may be

associated with a PP indicating the periods

during the day (or the week) that are preferred for

carrying out the task. These periods may be more

or less strict, that is to say that agents may or may

not be allowed to achieve the task outside the PP.

The PP changes the priority of the task,

positively during the period, and negatively

outside.

• Community. This indicates whether this task is

rather accomplished alone or in groups.

Example: “having meals” is rather a collective

task while “brushing teeth” is rather an

individual one. The realization by an agent B of

ICAART 2017 - 9th International Conference on Agents and Artiﬁcial Intelligence

a collective task also achievable by an agent A,

gives a bonus to the task’s priority for the agent

• Location. Each task is associated with a location,

inside or outside the housing.

These features illustrate an important advantage

of using an agent centred model. It becomes

unnecessary to generate a scripted schedule minute

per minute. Preferential periods are sufficient for

autonomous agents to determine themselves the

sequencing of their tasks from a list. In addition, the

agents themselves manage their coordination through

internal models (in this case, the community feature).

3.3 The TUS Used

Our proposal is to use TUS data to set the parameters

of the MAS simulation (task information for each

individuals of the household). While our method is

generic from the perspective of the TUS used (the

data of any TUS can be used because they all follow

a common methodology), in was applied to a specific

TUS: the French 2009-2010 “enquête emploi du

temps”.

3.3.1 Description

This TUS was conducted by the INSEE institute in

2009-2010 (INSEE 2010). It interrogated 12,000

households, in which one or two people per

household filled one or two timetables. A timetable

consists of 144 time slots of 10 minutes each, from

21:00 until 23:50 the day after. For each time slot, the

respondent must indicate what activity he is currently

performing. In this TUS, more than 18,500 people

filled around 27,000 timetables. There are 140

activities identified in the survey.

This subdivision is too precise for the goals of our

simulation. Indeed, dealing with very precise

activities have two negative consequences. First, it

unnecessarily increases the complexity of the model:

the activities "reading" and "reading a newspaper" of

the TUS can be simply modelled as a single activity

"reading" in the simulation. Second, the more

different activities are in the survey, the less

repetitions each of them receives. That leads to under-

represented activities in the survey, and therefore

insignificant ones (for example activities like "going

on strike" or "receive medical care from a

professional at home" are not observed enough in the

survey to be significant).

So, we operate a transformation to change the 140

activities in the TUS to only 30 activities in our model

(more consistent with our simulation’s goals), such

as: “to sleep”; “to work”; “hygiene”; “to watch TV”;

“to wash the laundry”; “to wash the dishes”; “to cook

lunch”, etc.

Please note that this transformation of activities

from the survey is optional, and depends on the

simulation’s goals. In this case, we are interested in

household electrical consumption, so the activities are

mainly inside the housing, and energy driven.

3.3.2 Individual Types

In a similar manner as the statistical methods

presented in the state of the art, we do not try to

characterize the behavior of individuals in general.

We define a typology of individuals, which allows us

to categorize the timetables based on the individual

characteristics of the person who completed them.

The more we have access to specific individual

characteristics, the more we are able to identify a

typology of specific individuals, and the more the

schedule will be representative of those types.

However, the fewer timetables associated with a type

of individual, the less representative the schedules

are. The goal is therefore to build the most

discriminating individual typology possible, while

avoiding under-represented types. We decided to

retain the following individual criteria:

• Sex

• Age

• Professional activity (student, working person,

unemployed, pensioner)

There are no timetables filled by children under

10 years. So we decided to build categories with 10

years age range, starting from 10 years old (up to 60),

and a last category for those over 60 years. Since all

combinations of these three criteria (sex, age,

activity) are not possible (no woman under 20 years

old is a pensioner, for example), we get 27 different

types. The smallest group has around 170 timetables,

while the biggest has over 4,000 ones.

Please note that the TUS data do not allow us to

model children under 10 years old.

3.3.3 Type of Day

Another consideration needs to be taken into account:

the type of day. Indeed, for the same individual, the

activities carried out on weekdays and on Sundays are

rarely similar. But the type of day is also dependent

on the individual’s type. The usual Monday activities

from a working person and from a pensioner could be

highly heterogeneous. We use the following

typology:

Using Time Use Surveys in Multi Agent based Simulations of Human Activity

• “working day” or “weekday” (“working day” for

active and students, and “weekday” for

unemployed and pensioners)

• rest day

Please note that, during the survey, timetables

filling days have been deliberately divided equally

between weekdays and weekend. So, we have an

almost equal distribution for each type of individual,

between rest days and working day (51.4% of

weekdays).

Please also note that this segmentation of

individuals and types of day depends on the

simulation’s goals.

3.4 Data Collected from TUS

3.4.1 Comparison between Statistical

Macroscopic Results and Individual

Schedules

By studying the TUS’s timetables individually, it

appears that the statistical results at a macroscopic

level do not give much information about behaviors

at an individual level. A simple example: the "sleep"

activity (see figure 1).

These curves show the percentage of individuals

of the four categories "working person", "student",

"unemployed" and "pensioners" performing the

"sleep" activity during each step of the day. From

these curves, one can easily draw firm statistical

conclusions such as "on average, students get up later

than pensioners, who are those who go to bed the

earliest”. These results are very interesting and fairly

simple to reproduce statistically.

However, these results conceal a substantial part

of the individual behavior variability. Even if an

average of 95% of the pensioners is sleeping between

2 AM and 4 AM, it is not necessarily the same 95%

at 2 AM and 4 AM. The study of individual timetables

shows that activities interruptions are very common.

Thus, in 10% of the timetables, the sleeping activity

was reported several times during the day (and up to

7 times), or no time at all. This example illustrates the

gap between aggregated and individual behavior.

Modelling the human sleeping activity as an activity

carried out only once per night for a duration of 8

hours may be enough to match the behavior

distribution at a macroscopic level. But this does not

model individual realistic sleeping behaviors in the

sense that in many cases, the simulated behaviors may

not match the actual observed ones because of their

number of repetitions per day, or their mean duration

(people wake up during the night, sleep several times

a day, etc.).

Yet the sleeping activity is really easy to model: it

is an activity adopted every day by almost everyone,

with a simple schedule. For an activity much more

difficult to model such as "being on the phone", one

can easily imagine the difference there may be

between the aggregate and individual behaviors.

3.4.2 Enhance the Information Extracted

from the TUS

The information typically used by statistical methods

are the mean durations (and the associated standard

deviations) of each activity, as well as the percentage

of individuals who adopt a specific activity at any

given time. This data is sufficient to generate realistic

activities at a macroscopic level, but lacks realism at

a microscopic level, because it does not take into

account the number of times each activity is actually

repeated each day. That is why we extract more

information from the TUS, in order to match more

precise activity features at a macroscopic level.

Figure 1: The "sleep activity" over time.

ICAART 2017 - 9th International Conference on Agents and Artiﬁcial Intelligence

Let (, ) be the number of repetitions of the

activity a, in the timetable t.

Let 



(,,)be the probability distribution of

(, ) for the activity a, for all individuals of type i

and for all days of type d.

For each 



, let 

()

be the collection of couples

(starting time, finishing time), from every episode of

the activity in 



Our model requires to extract from the TUS all





(,,)and all 

()

for every activity a, every

individual type i, and every type of day d.

Please note that the usual data used by statistical

methods (mean duration/standard deviation and

percentage of individuals adopting a specific activity

at a given time step) are included in this data.

If this new information is taken into account

during the process of macroscopic matching between

observed activities and simulated ones, we believe

that the simulated individual activities are much

closer to the observed ones, and thus more realistic.

That is why we will build in our agent model the

parameters for task description based on this

information. However we must also deal with activity

variability over time.

3.5 Activity Variability over Time

3.5.1 The Absence of Information on the

Variability of Activity in the TUS

Another difficulty is the absence in the TUS of

information about the variability of activity over time.

For the same individual, no more than two days of

their life are given, and these days are never

consecutive (usually a weekday and weekend day).

From one or two timetables of an individual, it is

impossible to know if the times, durations and

frequencies indicated for each activities are rather

usual or exceptional.

Example: Let T

and T

be two timetables

collected in the TUS (respectively a Wednesday and

a Saturday) for an individual A. In T

, only 4 hours of

sleep are indicated, and 10 hours in T

. What does it

say about the average sleep durations of A? It is

impossible to know if these values are representative

of the mean duration of sleep on weekdays and

weekend or not. A could be sleeping 4 hours per night

during week nights, or this Wednesday he might have

attended a party and slept less than usual.

This problem does not appear when one attempts

to generate a schedule for only one day, because in

this case, it doesn’t matter if the activities observed in

the TUS are usual or not; one just has to reproduce

them. But when one wants to generate a schedule for

weeks or months, one needs to know if the activities

are usual or not, because only the usual activities

should be repeated day after day.

In order to generate individual realistic human

activity over time, one has to generate routine

activities which will regularly be the same, but also

variations around these routines (Feldman and

Pentland 2003; Haradji et al. 2012).

This means that generating the schedule for a

week with copies of the same day over and over is

impossible (no activity variability). On the other

hand, generating the schedule for a week with totally

different days each day does not bring more

consistency over time (no routine activity).

3.5.2 Weekly “Routine” Schedule

Our proposal to solve this problem is to generate the

information that is lacking. To do so, we will make

one strong hypothesis by considering that the most

atypical behaviors at a macroscopic level (the least

represented ones in the TUS) are also atypical ones at

a microscopic level, that is to say that they are only

special cases, specific exceptions, and cannot be the

routine behaviors of any individual. That is a simplify

model of the reality. It is possible that some humans

exhibit routine behaviors that are atypical: for

example, some humans do sleep on average 2 hours

per night. But since these cases are very uncommon,

we do not take them into account.

Weekly “routine” schedules will be based only on

regular behaviors. All the other behaviors (unusual

ones) found in the TUS will allow us to feed the

variability around routine behaviors.

For each simulated individual we will create a

weekly routine schedule based on all regular

behaviors on the timetables corresponding to his type.

The unusual behaviors of these timetables will

indicate how the behavior of the agent will change

around that routine.

In this way, the mean duration and standard

deviation of each activity are kept realistic at a

macroscopic scale, but the individual behavior gains

consistency over time.

3.6 Details of the Algorithm

We will now present the algorithm we use to generate

schedules. Please note that this algorithm is generic to

the extent that it works regardless of the typology of

individuals, the days and activities or the TUS

plugged in. The individual timetables are not required

as long as the data presented in the section 3.4.2 are

available.

Using Time Use Surveys in Multi Agent based Simulations of Human Activity

Table 1: Table presenting all notations of the algorithm.

(, )

number of repetitions of the activity

a, in the timetable t





(,,)

probabilistic distribution of N(a,t)

for all individuals of type i and for

all days of type d



()

all couples (starting time, finishing

time), from every episode in





(,,)

mean(

()

)

mean duration of the episodes from



()

stddev(

()

)

standard deviation of the episodes

from 

()

PP(

()

)

preferential period of the activity in



()

DRS(ind,d)

daily routine schedule of the

simulated individual ind, for the

days of type d

WRS(ind)

weekly routine schedule of the

simulated individual ind

DS(ind,rd)

actual daily schedules for the

individual ind, for the real day rd

3.6.1 Step 1: Calculation of Temporality

Information

Let A be the set of activities of the simulation

Let I be the set of individual types

Let D be the set of types of day

For every activity a in A, for every type of individual

i in I, and for every type of day d in D, we first

compute 



(,,) and 

()

(see section 3.4.2 for

definitions).

For every 

()

, we compute mean(

()

) and

stddev(

()

), the mean duration and standard

deviation of episodes from 

()

We then build PP(

()

), the preferential period of

the activity. The PP are calculated such as 75% of the

episodes from 

()

start after the starting time of PP,

and 75% of the episodes from 

()

finish before the

finishing time of PP.

3.6.2 Step 2: Determination of Daily Routine

Schedule

Let DRS(ind,d)={task

, task

, …, task

} be the daily

routine schedule of the simulated individual ind

(which individual type is i), for the type of day d. This

schedule is a collection of tasks (see section 3.2 for a

definition of task in the model)

For every activity a in A, let x(a) be a possible number

of repetition of a, chosen randomly in 



(,,).

We then create a task ta, corresponding to the activity

a, with the following properties:

• Minimum duration = mean(

()

) -

stddev(

()

)

• Maximum duration = mean(

()

) +

stddev(

()

)

• Preferential period: PP(

()

)

We add x(a) repetitions of the task ta in DRS(ind,d)

3.6.3 Step 3: Determination of Weekly

Routine Schedule

Let rd be any real day of the week (Monday,

Tuesday… Sunday).

Let WRS(ind)={DRS(ind,d

) , … , DRS(ind,d

) ,… ,

DRS(ind,d

)} be the weekly routine schedule of the

simulated individual ind (which individual type is i),

with d

, …, d

, … d

the n different type of day in D.

For every type of individual i in I, let Pdj(i,rd), (with

jϵ[0,n]) be the probabilities that the day rd be of type

for an individual of type i.

For every day of the week, we add in WRS(ind) the

corresponding DRS(ind,d) thanks to a random draw

in Pdj(i, rd), (with jϵ[0,n]).

3.6.4 Step 4: Determination of the Simulated

Schedules

Let ind be a simulated individual.

Let DS(ind,rd)={task

, … task

} be the actual daily

schedules for the individual ind, for the real day rd.

DS(ind,rd) is build as follows:

for every type of day d, we determine the daily routine

schedules DRS(ind,d) (step 2) and the weekly routine

schedule WRS(ind) (step 3).

Then, for every new simulated day sd (which type of

day is d), for every task ta in DRS(ind,d), we add ta’,

a copy of ta, in DS(ind,rd). But there is a probability

of 0.3 that we turn ta’ into an “unusual task” (see

section 3.5.2). This corresponds to the fact that,

statistically, only 68.2% of the episodes have a

duration situated in the interval [mean(

()

) -

stddev(

()

) ; mean(

()

) + stddev(

()

)].

However, at this point of the algorithm, every episode

is inside this interval. So, in order to keep a realistic

variability of these durations at a macroscopic level,

we have to “push” 1/3 of these durations outside the

interval.

Turing ta’ into an “unusual” task is done by the

following operation:

With a probability 0.3 (probability of unusual task),

select randomly one of the two possible alterations

(this choice is equiprobable):

a) Set minimum duration = mean(

()

) -

2*stddev(

()

)

ICAART 2017 - 9th International Conference on Agents and Artiﬁcial Intelligence

Set maximum duration = mean(

()

) -

stddev(

()

)

b) Set minimum duration = mean(

()

) +

stddev(

()

)

Set maximum duration = mean(

()

) +

2*stddev(

()

)

Please note that, statistically, around 4.5% of the

durations are outside the interval [mean(

()

2*stddev(

()

);mean(

()

)+2*stddev(

()

)]. We

do not deal with them (we consider those tasks as too

rare).

3.6.5 Conclusion about the Algorithm

With this algorithm, the duration and standard

deviation of the simulated activities at a macroscopic

level are still respected, but the average number of

repetitions of each activity is respected too. In

addition, the simulated individuals exhibit both

routine activities and variation around them. We

believe that these features increase realism in the

individual schedules generated.

In this algorithm, we suppose that the tasks are

independent from each other, but it is possible to deal

with such dependencies during the random draw in





during step 2 (for example to take into account that

if an individual is cooking during the day, he is more

likely to eat at some point after that).

The tasks created by this algorithm are not

complete at this point. They lack community and

location information. This information cannot be

found in the TUS, it has to be added by the modeller.

So, for each activity of the simulation, the modeller

has to explicitly fill up the features “community” and

“location” (see section 3.2).

With this additional information, the tasks created

in this algorithm are complete (in the sense of our

simulator). The actual daily schedule DS(ind,rd)

created for every simulated individual ind and for

every day rd can be directly sent to the agents in the

simulator. Every day of the simulation, the agents will

receive their tasks, and they will decide how they are

going to perform them.

3.7 Validation Methodology

This model has been implemented and is currently

being tested with the data from the INSEE TUS. The

next step is to validate the result of the produced

simulation.

Several methods are possible. First, we would like

to verify that the autonomy of the agents does not

harm the macroscopic realism of the simulated

behaviors. Indeed, as we generate non-scripted

schedules, the agents have some degree of freedom to

reorganize them. That can be checked by launching a

large number of simulations and generate the TUS

timetables associated with each day of simulation.

Then we can compute the statistical results at a

macroscopic level from these new timetables as if

they were real ones, and finally compare them with

the real ones. This method is easy to perform (it only

relies on computation time), but like the validation of

the statistical methods, only verifies the realism of the

activities at a macroscopic scale.

A second verification is to compare real and

simulated activities at a microscopic level. However,

we cannot manually verify a large number of

produced behaviors. Our proposed approach is to

generate “new TUS timetables” as in the previous

method and to perform a classification process with

the real TUS timetables and the simulated ones.

Based on previous work (Darty et al. 2014), we claim

that if the obtained clusters are mixed (they contain

both real timetables and simulated ones), it means that

the simulated activities are indistinguishable from

real ones, based on the considered variables.

However, we still need to define the relevant

classification variables. That will allow us to state that

the individual simulated activities are “realistic”.

4 CONCLUSION AND

PERSPECTIVES

We have presented a multi agent model using

concepts coming from statistical methods of human

activity generation. In particular, thanks to data

collected in TUS, we are able to calibrate the

simulated behaviors. This model has two major

advantages. First, compared to traditional statistical

methods, it allows the generation of more realistic

individual behaviors, while keeping the same quality

of realism at a macroscopic level. Second, compared

to more traditional MABS, the use of TUS allows the

objective measurement of the realism of the simulated

activities. In addition, the international and generic

nature of TUS makes them usable in various

application domains.

The next step of our work is to implement this

model on data collected within our project to study

the realism of the behavior simulated.

This work leads to many perspectives. In the field

of energy simulation, adding reactivity and autonomy

to the simulated individuals allows the prediction of

long-term consumption, and the ability to take into

Using Time Use Surveys in Multi Agent based Simulations of Human Activity

account the impact of new types of consumption

(generalization of electric cars, self-production and

self-consumption of electricity, etc.). One also

becomes able to deal with major events (climatic,

social, etc.). Another research track currently

followed by our team is to study the impact of new

electrical tariff on consumption. How do consumers

react to a change in the price of electricity?

In the area of MABS, the widespread use of TUS

could bring a better understanding of the relationship

between the notions of realism and credibility (some

of the actual behaviors observed in the TUS seem

highly unlikely or even incomprehensible).

Furthermore, the worldwide nature of TUS can also

help modellers to introduce, in a consistent and

measurable way, some lesser explored aspects of

human activity simulation (such as the individual’s

culture or other local specificity).

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