=Paper=
{{Paper
|id=Vol-1778/AmILP_1
|storemode=property
|title=User Modelling Languages for AmI: A Case Study on Road Traffic
|pdfUrl=https://ceur-ws.org/Vol-1778/AmILP_1.pdf
|volume=Vol-1778
|authors=Alberto Fernández-Isabel,Rubén Fuentes-Fernández
}}
==User Modelling Languages for AmI: A Case Study on Road Traffic==
https://ceur-ws.org/Vol-1778/AmILP_1.pdf
User Modelling Languages for AmI:
A Case Study on Road Traffic
Alberto Fernández-Isabel and Rubén Fuentes Fernández1
Abstract. Traffic is an important phenomenon in modern societies. The work presented in this paper pursues reducing this effort by
Its complexity and the difficulties to control the actual settings where providing base models for people acting in traffic phenomena. These
it happens have made of simulation a key tool for its study. This models are part of a wider effort to build a general framework for
approach requires suitable models to capture all its relevant aspects traffic simulation based on ABM, so they are designed looking for
and their mutual influences. Among these aspects, people are the key reusability with different theories and contexts. For this purpose, the
one. However, there is a limited understanding of people attitudes basis of the models is a classification of people with three dimen-
and behaviours and their effect on traffic. Thus, simulation has here sions: their role in traffic, traits, and current state.
an important component of exploration of hypotheses. Our research People role in traffic depends on their mean of transport and their
contributes to this line of work with a set of general and extensible relation with it People are classified in drivers, passengers of vehicles
agent-based models about people in traffic. These models integrate and pedestrians. All of them can be modelled at the individual level
existing research from Social Sciences and simulation. The agent or as groups moving together.
paradigm supports the explicit specification of processes of infor- People traits represent features of people that are permanent for the
mation management, decision-making, action execution, and interac- travels considered in the simulation. They include physical attributes
tion both with people and the environment. Such approach facilitates of the body, such as age, gender or disabilities. There are also atti-
model reuse, and linkage between different elements relevant for the tudes to capture personality and mental features. For instance, people
studies. The paper illustrates the application of these models with a can be more or less aggressive and have personal problems that in-
case study that shows how to integrate in them a well-known model crease their stress. Moreover, people get more traffic experience over
for drivers attitudes. time. This empowers them with additional knowledge and skills to
face traffic situations through their learning.
The current state captures dynamic features that depend on the
1 INTRODUCTION specific context and moment. For instance, they indicate if drivers
attention is low because there are distracting passengers, or if traffic
Life in modern societies is highly mediated by traffic. Every day, conditions bother the driver.
millions of persons move on foot and by private vehicles or public The suitability of these models is illustrated with a case study that
transport. These flows are organized according to certain social rules, specifies existent work on traffic simulation using the proposed prim-
but also depend on individual attitudes and behaviours, unexpected itives. It considers the simulation in [13] about drivers attitudes and
events happening in the environment, and their mutual influences. their influence on group behaviour.
Given the difficulties to carry out these studies with real settings, The rest of the paper is organized as follows. Section 2 intro-
models have emerged as a key tool to study traffic. duces the agent modelling language used in our approach. Section
There are several approaches to model traffic [16]. Analytical 3 presents the models for people in traffic with that language, and
models rely on a strong abstraction of the individual components de- grounds them in available research about traffic. Section 4 compares
scribed mainly with mathematical formulas [8]. They are useful to these models with those in [13] regarding the phenomenon called
consider phenomena with large populations, but have limitations re- dominance at junctions. These results are further compared with re-
garding the specification of procedural and non-linear behaviors, and lated work in Section 5. Finally, Section 6 discusses some conclu-
heterogeneous populations. As an alternative, simulation facilitates sions and future work.
the specification of these kinds of behavior and population, but it
is not usually intuitive the correspondence between the actual sys-
tem and its computational representation. Agent-Based Modelling 2 AGENT-BASED MODELLING LANGUAGE
(ABM) [8] addresses this problem using agents as its core modelling
primitive. Agents are intentional abstractions conceptualized in terms This work specifies their models using the modelling language of the
of elements such as knowledge, goals or capabilities. They are able INGENIAS methodology [14]. The key concept of INGENIAS is the
to interact with other agents and their environment. These features agent.
facilitate describing people behaviour with agents. However, mak- An agent is an intentional entity that follows its own agenda char-
ing realistic models still demands a high effort to integrate different acterized by goals. In order to achieve these goals, the agent is able
theories and give the needed information. to carry out certain tasks. An agent can trigger a task when it pursues
an unsatisfied goal that the task is potentially able to fulfil, and all
1 University Complutense of Madrid, Spain, email: afernandezisabel, the elements required by the task are available. These elements are
ruben@fdi.ucm.es usually pieces of information known by the agent or events coming
from the environment. As the result of the execution of the task, the The group of physical attributes currently comprehends gender
agent acts on the environment and produces or modifies information. and age. The gender attribute classifies people into male or female.
Agents act on and perceive the environment through external ap- The age attribute uses four levels, young, adult-young, mature and el-
plications. These are the sources of the events and tasks use their der. The age levels are different for drivers and pedestrians, as people
methods to affect the environment. can walk before they can drive and the required capabilities for both
A final element relevant for the models presented in this paper is activities are different. These attributes mainly affect perception and
the AInherits relationships. This is an inheritance relationship that al- reaction parameters, such as sight distance and time to maneuver.
lows defining a type of agent as an extension or constraint of another Other group of traits is the attitudes. Models consider in it a traf-
type of agent. Thus, it highlights the common features of different fic profile and relationship problems. The traffic profile is based on
types of agent and saves modelling effort. an extension of the selfish principle in [13], classifying drivers as
aggressive, normal or moderate, and pedestrians as reckless, normal
3 TAXONOMY OF PEOPLE IN TRAFFIC or prudent. This classification differentiates, for instance, between
drivers who always drive below or at speed limits, or on the contrary
Traffic is the organized way of moving people using different means usually break them. The relationship problems acknowledge this as a
of transport. This people have as their main goal to arrive fast and classical source of anxiety and distractions in traffic situations, mak-
safely to their destinations [7, 9]. They can achieve this goal through ing more likely suffering an accident or taking greater risks [18].
alternative sequences of actions as long as they meet some con- The last group of traits is the traffic experience. It classifies indi-
straints. First, people use different means of transport, and can con- viduals regarding their traffic learning with values between 0 and 5,
trol them or be a passenger. This makes suitable only some routes being 5 the maximum experience.
and implies certain rules. Our work only considers those means shar-
ing spaces in our cities and roads, e.g. on foot or by car. Second, the
sequence also depends on the physical and mental characteristics of 3.3 CURRENT STATE OF A PERSON
people and their current state. However, models cannot consider all
The traffic and personal conditions change during travels, and this
the known people features and processes. This would be unsuitable
affects people behaviour. For instance, drivers caught in a traffic jam
regarding efficiency and abstraction, and even incorrect given our
can start relaxed, but their frustration and impatience will rise as
limited understanding of the phenomena. The taxonomy presented
they waste more time stuck, which can cause risky situations in their
proposes a number of features based on literature, mainly coming
nearby environment. The models consider these dynamic features of
from Social Sciences, widely accepted as relevant for traffic studies.
behaviour with the attributes belonging to the current state of the
Next subsections present in details all these aspects.
person agent. Figure 2 shows these attributes classified in physical
state and mood, depending on when they affect physical action and
3.1 ROLE AND MEAN OF TRANSPORT perception or thinking and attitudes respectively.
The behaviour of a person in traffic is first limited by his/her mean The physical state influences the perception of the environment.
of transport. Although passengers influence traffic, e.g. distracting Individuals do not receive objective information from the environ-
drivers, these models focus on people controlling their mean of trans- ment, as this is really mediated by their own senses and depends
port. on external conditions [15]. The personal conditions are represented
The mean of transport requires certain processes to manage it, and with the values for this attribute, which are focused, drowsy, dis-
also makes possible some processes. For instance, a person can know tracted and drugged. The influence of the external conditions is rep-
how to brake a car, but needs driving one to perform the action. At the resented using the environment entity.
same time, different means of transport obey different rules. These The environment entity has attributes for the weather conditions
can be both explicit, e.g. traffic regulations, and implicit, e.g. drivers and type of environment [18]. The first one takes values between
facilitating other drivers maneuvers. sunny, cloudy, rainy, heavy rain, windy, snowy, ice and foggy, while
The models represent this information with a hierarchy of agents the second one is classified as familiar, unknown, difficult, affordable
(see Figure 1). The basic agent is person, which incorporates the goal and straightforward. These attributes are linked to the physical state,
of arriving to a destination following a certain route and perception pointing out that they affect its value.
of obstacles and signals. According to the mean of transport, a person The mood considers that external factors influence people mental
is extended as a driver or a pedestrian. These agents have additional state [7]. This state affects aspects such as decision making or level of
goals, information and tasks to move by car or on foot respectively. attention to the environment. The specification of this attribute is fur-
In the case of the driver, there is a related vehicle. The vehicle is ther decomposed into the attributes impatience and self-confidence.
represented as an external application with, for instance, methods to The impatience represents the frustration of the person, perhaps be-
brake or to manage the steering wheel and events from the speed cause she/he is in a hurry or the traffic conditions are adverse. The
indicator. self-confidence represents the assurance of individuals on their own
knowledge, capabilities and skills. Both attributes take values be-
tween 0 and 5. Depending on the value, they can have a positive or
3.2 TRAITS OF PEOPLE INVOLVED IN
negative effect on the person processes. For instance, a person with
TRAFFIC
self-confidence 5 can make risky decisions that are inadequate for
The way of behaving in traffic also depends on the personal traits the perceived situation. In the case of pedestrians, the self-confidence
of each person. A well-known example is the differences in acci- also indicates how crowds influence individual trajectories [9].
dents regarding age and gender [12]. These traits are static for each As it happened with the physical state, the value of the self-
person, as they do not change during the travel, and thus in the sim- confidence is affected by other attributes. A familiar type of envi-
ulation. There are three groups of traits: physical attributes, attitudes ronment and good weather conditions increase the self-confidence.
and traffic experience. Moreover, people frequently move in groups, and this companion al-
Figure 1. Inheritance of driver and pedestrian agents.
Figure 2. Relationships between the elements of the taxonomy. Dotted lines represent that an attribute affects the calculation of other.
ters the self-confidence with comments or actions. The companions nication between driver agents is not suitable, as drivers are in their
attribute gathers this information. It considers the attitude of the com- own vehicles. The perceived agents could depend on new physical
panions with values in silence, little chatty, chatty and fun-loving, attributes related to sight.
and the number of individuals. This approach enables that the effects of the driver attitude in the
Note that this presentation has pointed out several mutual relation- simulation can dynamically change. Such effects are modulated by
ships between attributes. For instance, a bad physical state worsens attributes of the current state, which are influenced by traffic con-
the perception of the road, reducing the self-confidence. In the mod- ditions, e.g. surrounding vehicles, speed or time waiting. Therefore,
els, tasks managing the internal state of agents implement these mu- studies using these models provide behavior that is more realistic.
tual influences.
4 CASE STUDY
5 RELATED WORK
The attribute traffic profile presented in this paper is based on the
classification of drivers in [13]. This classification uses the selfish
principle, which assumes that any driver has a certain level of self- The current research must be framed within two main lines of work:
ishness when pursuing his or her goals. This level classifies drivers in studies on people and their behaviour regarding traffic, and traffic
moderate, normal and aggressive, determining their speed or prone- simulations. Both of them are sources of information to develop our
ness to make risky decisions. The main limitation of that work is that models and validate them.
two drivers of the same group do not differ in their behaviour, which Studies on actual people provide information on the relevant at-
is not a realistic approach. This case study considers how the models tributes regarding traffic and the actual processes involved in it.
proposed in our work cover the previous classification and facilitate These studies typically focus on obtaining data, statistics and rela-
its extension. tionships among some factors under scrutiny. For instance, they try
As previously mentioned, the traffic profile trivially supports the to identify aggressive behaviour and the reasons of their appearance
classification in [13]. Its effect over driving depends on the imple- [17]. Some commonly considered attributes in these studies are gen-
mentation of the tasks of the different agents. Note that since this der and age, as in [4, 12], presence of passengers in a vehicle, the
is an attribute of the person agent, which is the base type of all our weekday and the most troubled hours [4], the driving experience
agents, all the agents in our models include that attribute. of drivers [10], the physical state [18], and the mood [10]. There
The heterogeneity of behaviour for agents with the same traffic are also behavioral studies more focused on the driving processes.
profile is achieved with several attributes. The impatience is particu- These studies monitor, for instance, physiological signals, gestures
larly relevant in this context, as it captures the anxiety produced by or speech to identify and/or predict decision-making, low-level ma-
the current traffic situation. neuvers or drivers mood [19]. These studies propose attributes and
The original work in [13] also discusses the phenomenon appear- processes that could be considered for simulation, but have some
ing at junctions known as dominance. It happens when a driver or a limitations for this purpose. They are difficult to use to validate sim-
group of them who are in a lane of a junction push their way, fol- ulation models or check the influence of new elements, as this would
lowed by other cars, and get to block the other lanes. This lane of imply researchers to carry out new studies.
cars will be the only one able to move forward as long as they do not The previous knowledge has been used in a variety of simulations
free the junction. If drivers of two or more lanes of a junction exhibit with different goals. Regarding the level of abstraction at which traf-
this behaviour at the same time, they can produce a deadlock where fic phenomena are considered, these simulations can be classified
nobody will go forward. in: macroscopic, mesoscopic and microscopic simulations. The first
With the presented models, this kind of behavior can be the con- ones attempt to capture the general principles governing the system
sequence of the traffic profile and certain attributes present in the instead of individuals, in a way similarly to analytical models. They
current state of the person. An aggressive driver is more dominant at are typically used to represent large areas of terrain with large quan-
a junction than a moderate or normal one, and therefore the former tities of vehicles and traffic infrastructure conflicts [3, 16]. On the
tries to cross the junction with greater determination. When drivers contrary, microscopic simulations present individual elements with
have the same traffic profile, their current state is also crucial for the higher complexity. Most of ABM in traffic belongs to this category
dominance. The attributes of current state more directly involved in [5]. The related computational costs make them suitable only to rep-
this behavior are impatience and self-confidence. A high impatience resent small areas of terrain with few individuals. Moreover, it is dif-
makes the driver prone to make quick decisions, not always enough ficult to embed general rules of behaviour in them, as rules usually
meditated. With a high self-confidence, the driver dares to perform appear on each agent. Mesoscopic simulations are hybrid between
maneuvers that in other circumstance she/he would not carry out. On the previous types. They try to solve their limitations locating each
the contrary, a low self-confidence leads the driver to doubt about information or behavior at the most suitable level, either individuals
maneuvers in complex settings (e.g. many cars around), causing that or groups [2]. Our work belongs to this category. As it is based on
other more determined drivers cross before her/him. Furthermore, INGENIAS [14], it supports modelling both individual agents and
drivers that are more impatient push others, which increase the frus- groups (not shown in this paper), as well as inheritance hierarchies
tration of the later. This causes a widespread anxious mood in the involving those abstractions.
junction [11], which makes it more hazardous. As a distinctive feature of our research, it works with simulations
The update of the impatience level requires that agents know their at the level of models. As shown in [6], this facilitates the automated
position and that of their neighbors. The own position can be in- generation of simulations for different target platforms from the spec-
cluded as a new attribute position of the driver agent. The positions ifications using model-driven techniques. Other works have this in-
of the other drivers are known through interactions of the driver with formation embedded in their programming tools [1], reducing the
an external application that mediates its perception. Direct commu- possibilities of studying and reusing that knowledge.
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This work has been done in the context of the project “Social
Ambient Assisting Living - Methods (SociAAL)” (grant TIN2011-
28335-C02-01) supported by the Spanish Ministry for Economy and
Competitiveness, the research programme MOSI-AGIL-CM (grant
S2013/ICE-3019) supported by the Autonomous Region of Madrid
and co-funded by EU Structural Funds FSE and FEDER, and the
“Programa de Creación y Consolidación de Grupos de Investigación”
(UCM-BSCH GR35/10-A).