Transcript Agent Based Activity Based

Micro-simulation And Modeling Applications:
Methods, Implications & Analysis (MAMAMIA)
Agent-based Activity-based
Microsimulation Modeling of
Urban Transportation Systems
Mohammed Medhat Amin
Department of Civil Engineering
University of Toronto
…A Shift To the Activity-Based Approach
 An important nature of travel demand ignored by trip-based approaches is that travel
is a derived demand – people travel to participate in other activities not for its own
consumption value
 A trip is generated to connect two spatially-separated sequential activities, and
individual trips do not stand in isolations, but more motivated by the activities they
enable
 It recognizes that activities – from which a person’s travel derives – are themselves
motivated by a complicated social environment that links them to other activities, to
other people, and to the features of the built environment
 Simply, activity-based models express the need to travel as a function of the need to
perform activities
…A Shift To the Activity-Based Approach
 Traditional approaches capture the effect of social roles and norms and the
distribution of environmental resources, etc… on travel (demand) implicitly, while the
activity-based approach treats these factors as endogenous to the system
 In activity-based approaches, every individual is a decision-maker who deals with a
huge choice set of various activity patterns in the time-space domain. Each combination
of activities, their locations, starting points and durations forms a unique activity pattern
 The need for estimating the impacts of policies that affect not only the broad
characteristics of urban form, but also target (directly/indirectly) the mechanisms that
produce human travel behavior
What is an Agent?
 What is an Agent (or autonomous agent)?
‘a system situated within and a part of an environment that senses that environment
and acts on it, over time, in pursuit of its own agenda and so as to effect what it
senses in the future’ [Franklin and Graesser, 1997 p.25]
 Agents
 are environment dependent. In other words, different agents belong to different
environments.
 sense and acts are two important properties of an agent, which determines how
they behave inside their environment
 To Describe an Agent
• Its environment
• Its sensing capabilities
• Its behavior (actions schemes)
What is an Agent?
• Agents independently make decisions as a function of their own attributes and the state
of the system that they find themselves within.
Agent
Action Output
Sensor Input
Environment
What is an Agent?
 In summary,
 Input (sensor) – Output (action) entity
 Never controls its entire environment
 Just some (partial) influence on/control of a system
 Effectoric capability – ability of an agent to modify/change its environment
 Actions have pre-conditions
 May obtain complete, accurate and up-to-date information about its environment
ABM, What?
 Agents: are like people who have characteristics, goals and rules of behavior.
 Objects: are the set of all represented passive entities that do not respond to stimuli
(e.g. building)
 Environment: are the topological space where agents and objects are located, move
and act, and where signals (e.g. sounds) propagate
 Behavioral Rules: define how agents behave and how environment changes as a
result of agent activities.
 Interaction Rules: describe how agents interact with each other and the environment
Behaviors are generated through agent interaction/communication with
other agents/objects and their environment(s)
Agents…
 Person Vs. Social agents
(e.g. individuals vs. employers)
 Active Vs. Passive agents
 Active agents interact with other agents and their environment to obtain some
pay off
 Passive agents, like locations, maintain a set of temporally restricted resources
necessary for various activity types (i.e. time-window for an activity)
 Reactive vs. Pro-active vs. Cognitive
 Reactive agents are able to perceive their environment and respond according to
their capabilities and the design objective
 Task accomplishing behavior
 Rules in the form of: if situation then action
 Pro-active agents are able to take initiatives in order to reach their goals
Agents…
 Cognitive agents have a set of perceived histories then encodes their experience
in a form that can be applied to the problem of (deliberately) deciding what to do
 have their activity plan
 memorize what they have sensed in order to perform global planning tasks
by using sources and information from their own experience
 Senses and acts are two important properties of an agent, which determines how
they behave inside their environment.
 The range and sensitivity of their sense and the range and effectiveness of
their actions determine the agent type.
Activities…
 Self-generated Vs. Driven from the environment
 I want to have lunch!
 I need to pick up my kid from a football practice
 Binding Vs. Non-binding Vs. Simultaneous
 Binding activities,
consider a travel activity using a train, a ‘physical’ constraint that the agent
can’t get off a moving train.

Non-binding activities,
agents in activity systems tend to have relative autonomy in their activity
participation, including the ability to terminate some activities under
way at will (e.g. a casual social engagement)

Recall that, in the travel demand context, the model is not intending as
an optimizer problem solver, and hence the completion of activities
need not to be guaranteed in order to meet some global goal.
Activities…
 Simultaneous activities,
if the current activity does not ‘tie up’ other resources at the agent’s
disposal, the agent should be able to apply them to another activity as long
as other criteria can be met (e.g. working on your laptop while being in the
train)
ABM, Why?
 The effects of inter-personal, inter-environmental, and inter-activity linkages have
proven difficult to capture in existing models.
 Linkages between the choices made by multiple individuals are either not
represented or taken to be fixed inputs to the model.
 This ultimately omits portions of the potential activity pattern space which may
actually be feasible with slight modifications of the constraint space
 These limitations reduce those models’ usefulness for the analysis of policy
measures whose responses may be affected by such linkages
 Agent-based models assume directly the behaviors of real-world decision-making
units such as individual or household. Therefore, urban travel demands become the
results of a multi-dimensional hierarchical choice process
ABM, Why?
 Agent-based models are characterized by their ability to represent complex
environment consisting of autonomous agents whose behavioral possibilities are
defined by their relationship to the environment
 The future state of urban systems generally can only be estimated by explicitly
tracing the evolutionary path of the system over time, beginning with current know
conditions.
 Using agent-based raises the possibility of emergent behavior; to predict outcomes
that are not ‘hardwired’ into the model. For example, the generation of single-parent
household
‘Turn the argument on its head, instead of artificial intelligence being inspired by
social processes, we will model social processes using the techniques of AI’
ABM, How?
 The MAIN IDEA
 By linking an individual to a behavioral mechanism, it is possible to simulate
an artificial world inhabited by interacted processes
 Thus it is possible to implement simulation by transposing the populations of a
real system to its artificial counterpart
 Each member of the population is represented as an agent who has built-in
behavior
 It allows modeling space-time dynamics within urban systems, as it enables to study
the relationships between micro-level individual actions and the emergent macro-level
phenomenon
ABM, How?
 The underlying assumption is that human behavior is adaptive, and that the engine
of adaptation is the individuals’ assessment of his/her imbalance with the environment –
‘person-environment fit’
 Rules need to be constructed in such a way that these agents are able to grow, learn
and interact with other agents and the environment
 Agent-based models view the system as a population of self-directed entities that
interact with each other according to specific behavioral rules that simultaneously
motivates and limit behavior, thus dictating the dynamics of the system
 Given an initial condition, all the agents will behave based on their ‘personal’
characteristics, learning and interacting rules in the system. These actions, in turn,
change the system state over time (congestion levels, housing prices…)
 The system will then evolve to a pattern, perhaps an equilibrium, from which
useful macro-level information can be extracted
ABM, How?
 An agent representing a person would therefore need:
 The ability to perceive information about environmental opportunities
 A model to anticipate such opportunities in the future
 And, a decision process that selects behavior based upon the anticipatory model
of the environment to achieve some set of goals
 An agent’s success in achieving its goals depends on its ability to anticipate the
opportunities presented by its environment
Estimation
Verification
Validation
Calibration
What should an ABM model?
 Each agent has a time-series of activities that define his/her behavior as interactions
with other agents in the environment and the environment itself
 Consider, a simple work activity may involve three agents: an employee, an
employer and a workplace
 Spatial Constraint
the employee must travel to the workplace
 Authority Constraint
Obtain the permission to access work-related resources from his employer.
 Coupling Constraint
another employee is required to complete the task
 The P-E Fit concept describes a personal equilibration in which the individual tries to
balance what s/he wants to do (i.e. the activity plan) with what the
environment allows (i.e. opportunity space)
What should an ABM model?
 Planning Process
 requires that the agent can anticipate the consequences of its actions; by
developing an internal model of the environment through experience
 Learning Process
 how the agent learns about its environment, how it learns about its options in
the environment, how it learns to evaluate its own behavior in that
environment
 occurs on different levels; perception, interpretation, possible actions, rules,
and payoffs…
 Decision Process
 that generates the activity plan.
 decision Rules can be If-then classical statements, or may be any algorithm
that maps from an interaction to an activity plan
 it arises from an adaptive learning process driven by the agent’s desire to
maximize some payoff through its actions over time.
What should an ABM model?
 Environment response
 the agent’s activities lead to a response (a consequence) from the
environment; one out of a set of all possible responses
 the agent’s activities change the world state
 Consider an agent’s decision to travel along a section of roadway
 reduces the available capacity
 the environment should reflect the impact of an additional traveler on the
roadway segment
 results in increasing the amount of time needed for agent’s travel activity
Issues in ABMs…
The “Interaction” View
 it permits greater flexibility for the representation of the universe of possible
activities and their associated constraints.
 For example, it recognizes that activities will have ‘agent-specific meanings’;
a particular activity episode might be a shopping activity to a shopper, while
simultaneously being a work activity to a cashier at the store, an economic
transaction for the shop keeper, and so on.
Role-based activity generation
 the need to engage in activities should be an endogenous feature of the model.
This will open the door to exploring the relationship between urban design,
socio-economic structure, and human behavior.
 Representation of resource distribution
 activities are simply interactions involving the exchange or consumption of
resources between entities in the environment. As a result, explicit
representation of resources distribution will enhance model expressiveness
Issues in ABMs…
 Experiential learning
 agents collect information about their environment as they interact with it, and
use it to develop anticipatory models of the environment
 Generality of design
 the model should be expressive enough to enable representation of a broad
range of local and global interactions, on a variety of timescales, with a
view toward exploratory analysis of possible trajectories rather than the
prediction of specific ones.
 Longitudinal
 the dynamics of urban systems are likely to exhibit sensitivity to the initial
conditions
 Longitudinal models will ultimately be necessary to explore how pathdependent trajectories impact the effectiveness of policy measures.
Issues in ABMs…
 Learning about the opportunity space
 Activities depend on resources (since they involve the allocation or transfer of
resources between agents)
 Therefore, an agent’s perception of the opportunity space will depend on its
knowledge of the resources it has available to it, which it continuously
updates over time through experience
 For instance, an individual recognizes that a particular activity meets particular
requirements
 Mainly important where there is no a one-to-one mapping between activities and
requirements – like particular activities that satisfy multiple needs
e.g. having dinner with friends may satisfy both meal and social leisure
needs
Issues in ABMs…
 Learning about the Decision Rules
The agent’s rule set arises from learning. In complex environment, the space of
perceived states and possible actions can get very large
 Furthermore, the agent’s ability to perceive constraints may also limit the
development of list of rules, and make such lists probabilistic (or Fuzzy!),
i.e. I think I can do this!
 Learning about decision rules is a complex process, possible involving insights
and innovation, but is generally a function of the existing rule set and the
agent’s experience
Issues in ABMs…
 Learning about interpretations of Historical Trajectories
 As agents gain experience, they develop better knowledge about casual
linkages in the environment according to some process
 Thus, a prior activity trajectory is used to update the agent’s anticipatory model
about what to expect from the environment
 A simple example of this is a model of expected travel time. As a individual
makes trips in the transportation system, s/he will learn about areas of
congestion in the system, perhaps linking time-of-day to anticipated travel
times.
Interesting!
“ Agent-based modeling is a third way of doing science. Like deduction, it
starts with a set of explicit assumptions. But unlike deduction, it does not (try
to) prove theorems. Instead an ABM generates simulated data that can be
analyzed inductively. Unlike typical induction, however, the simulated data
come from a rigorously specified set of rules rather than direct measurement
of the real world. Whereas the purpose of induction is to find patterns in data
and that of deduction is to find consequences of assumptions, the purpose of
ABM is to aid intuition.
…Agent-based modeling is a way of doing through experiment. Although the
assumptions may be simple, the consequences may not be at all obvious.
Numerous examples…of locally interacting agents are called “emergent
properties” of the system. Emergent properties are often surprising because it
can be hard to anticipate the full consequences of even simple forms of
interaction”
– Robert Axelrod (1997), emphasis added
Questions?
References…
• Agent-Based Approach to Modeling Environmental and Urban systems within
GIS, (B. Jiang, 2000), Institutionen for Teknik, University of Gavel, Sweden
• An Agent-based Activity Micro-simulation Kernel Using a Negotiation
Metaphor, (C. R. Rindt, J. E. Marca and M. G. McNally, August 2002): Institute
of Transportation Studies, University of California, Irvine
• Is it an Agent? Or Just a Program?: A Taxonomy for Autonomous (Franklin S.
and Graesser A., 1977)
•Microsimulation, (E. Miller, 2003)
• The Complexity of Corporations, (Robert Axelrod, 1997): Princetion University
Press
• Toward Dynamic, Longitudinal, Agent-Based Microsimulation Models of
Human Activity in Urban Settings, (C. R. Rindt, J. E. Marca and M. G. McNally,
2002): Institute of Transportation Studies, University of California, Irvine